Machine vision is very important for ensuring the success of intelligent transportation systems, particularly in the area of road maintenance. For this reason, many studies had been focusing on automatic image-based crack detection as a replacement for manual inspection that had depended on the specialist’s knowledge and expertise. In the image processing technique, the pre-processing and edge detection stages are important for filtering out noises and in enhancing the quality of the edges in the image. Since threshold is one of the powerful methods used in the edge detection of an image, we have therefore proposed a modified Otsu-Canny Edge Detection Algorithm in the selection of the two threshold values as well as implemented a multi-resolution level fixed partitioning method in the analysis of the global and local threshold values of the image. This is then followed by a statistical measure in selecting the edge image with the best global threshold. This study had utilized the road crack image dataset that were obtained from Crackforest. The results had revealed the proposed method to not only perform better than the conventional Canny edge detection method but had also shown the maximum value derived from the local threshold of 5x5 partitioned image outperforming the other partitioned scales.
This document summarizes a research paper on segmenting and classifying brain tumors in MRI images using cellular automata and neural networks. The researchers first use co-occurrence matrices and run length features to automatically select seed points in abnormal tumor regions. A cellular automata algorithm then performs seeded segmentation on the images to detect and highlight the tumor region. Finally, the images are classified into normal, benign, or malignant categories using texture features and a radial basis function neural network. The neural network approach provides fast and accurate tumor classification compared to other methods. In summary, this paper presents an automatic method for segmenting and classifying brain tumors in MRI images based on cellular automata for segmentation and neural networks for classification.
Automatic image slice marking propagation on segmentation of dental CBCTTELKOMNIKA JOURNAL
Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly
used to help doctors provide more detailed information for further examination. Teeth segmentation on
CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of
the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have
related information, the semi-automatic image segmentation method, that needs manual marking from
the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice
marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will
be propagated as the marker for the segmentation of the next slices. The experimental results show that
the proposed method is successful in segmenting the teeth on CBCT images with the value of
Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.
AUTOMATED MANAGEMENT OF POTHOLE RELATED DISASTERS USING IMAGE PROCESSING AND ...ijcsit
Potholes though seem inconsequential, may cause accidents resulting in loss of human life. In this paper, we present an automated system to efficiently manage the potholes in a ward by deploying geotagging and image processing techniques that overcomes the drawbacks associated with the existing
survey-oriented systems. Image processing is used for identification of target pothole regions in the 2D
images using edge detection and morphological image processing operations. A method is developed to
accurately estimate the dimensions of the potholes from their images, analyze their area and depth,estimate the quantity of filling material required and therefore enabling pothole attendance on a priority basis. This will further enable the government official to have a fully automated system for e f f e c t i v e l y ma n a g i ng pothole related disasters.
Application of neural network method for road crack detectionTELKOMNIKA JOURNAL
The study presents a road pavement crack detection system by extracting
picture features then classifying them based on image features. The applied
feature extraction method is the gray level co-occurrence matrices (GLCM).
This method employs two order measurements. The first order utilizes
statistical calculations based on the pixel value of the original image alone,
such as variance, and does not pay attention to the neighboring pixel
relationship. In the second order, the relationship between the two pixel-pairs
of the original image is taken into account. Inspired by the recent success
in implementing Supervised Learning in computer vision, the applied method
for classification is artificial neural network (ANN). Datasets, which are used
for evaluation are collected from low-cost smart phones. The results show that
feature extraction using GLCM can provide good accuracy that is equal
to 90%.
This document summarizes a research paper that reviews different methods for scene text detection and the challenges associated with it. The paper begins with an introduction that describes the overall process of automated scene text detection systems. It then provides a literature review of various text detection methods proposed in previous research, which can be categorized as connected component based methods or texture based methods. Some example methods are described. The paper discusses challenges in scene text detection, such as variable imaging conditions, complex backgrounds, and a wide range of text sizes and fonts. Finally, it discusses performance metrics like precision, recall, and f-measure that are used to evaluate scene text detection methods based on a standard dataset.
Matching algorithm performance analysis for autocalibration method of stereo ...TELKOMNIKA JOURNAL
Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.
Land Boundary Detection of an Island using improved Morphological OperationCSCJournals
Image analysis is one of the important tasks to obtain the information about earth surface. To detect and mark a particular land area, it is required to have the image from remote place. To recognize the same, the accurate boundary of that area has to be detected. In this paper, the example of remote sensing image has been considered. The accurate detection of the boundary is a complex task. A novel method has been proposed in this paper to detect the boundary of such land. Mathematical morphology is a simple and efficient method for this type of task. The morphological analysis is performed using structure elements (SE). By using mathematical morphology the images can be enhanced and then the boundary can be detected easily. Simultaneously the noise is removed by using the proposed model. The results exhibit the performance of the proposed method. Keywords: Remote Sensing images ; Edge detection; Gray- scale Morphological analysis, Structuring Element (SE).
ANOVA and Fisher Criterion based Feature Selection for Lower Dimensional Univ...CSCJournals
Unethical uses of data hiding methods have made Image Steganalysis a very important area of
research work in the field of Digital Investigations. Effectiveness of any Image Steganalysis
algorithm depends on feature selection and feature reduction. The goal of this paper is to develop
a reduced dimensional merged feature set for universal image steganalysis using Fisher Criterion
and ANOVA techniques. Statistical features extracted from wavelet subbands and binary
similarity patterns extracted from DCT of an image are merged to make combined feature set.
Fisher criterion and ANOVA test are applied to evaluate the combined feature vector score and
then only those features are selected which are found sensitive in both feature selection methods.
These reduced dimensional 15-D feature vector is used to train SVM classifier with RBF kernel.
The proposed algorithm is tested against steganography methods like F5, Outguess and LSB
based method. Stego images are generated using widely available stego tools for two standard
image databases: CorelDraw and BSDS500. Results are further validated using 10 fold cross
validation process. The proposed algorithm achieves overall 97% detection accuracy against
various steganography methods
This document summarizes a research paper on segmenting and classifying brain tumors in MRI images using cellular automata and neural networks. The researchers first use co-occurrence matrices and run length features to automatically select seed points in abnormal tumor regions. A cellular automata algorithm then performs seeded segmentation on the images to detect and highlight the tumor region. Finally, the images are classified into normal, benign, or malignant categories using texture features and a radial basis function neural network. The neural network approach provides fast and accurate tumor classification compared to other methods. In summary, this paper presents an automatic method for segmenting and classifying brain tumors in MRI images based on cellular automata for segmentation and neural networks for classification.
Automatic image slice marking propagation on segmentation of dental CBCTTELKOMNIKA JOURNAL
Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly
used to help doctors provide more detailed information for further examination. Teeth segmentation on
CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of
the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have
related information, the semi-automatic image segmentation method, that needs manual marking from
the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice
marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will
be propagated as the marker for the segmentation of the next slices. The experimental results show that
the proposed method is successful in segmenting the teeth on CBCT images with the value of
Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.
AUTOMATED MANAGEMENT OF POTHOLE RELATED DISASTERS USING IMAGE PROCESSING AND ...ijcsit
Potholes though seem inconsequential, may cause accidents resulting in loss of human life. In this paper, we present an automated system to efficiently manage the potholes in a ward by deploying geotagging and image processing techniques that overcomes the drawbacks associated with the existing
survey-oriented systems. Image processing is used for identification of target pothole regions in the 2D
images using edge detection and morphological image processing operations. A method is developed to
accurately estimate the dimensions of the potholes from their images, analyze their area and depth,estimate the quantity of filling material required and therefore enabling pothole attendance on a priority basis. This will further enable the government official to have a fully automated system for e f f e c t i v e l y ma n a g i ng pothole related disasters.
Application of neural network method for road crack detectionTELKOMNIKA JOURNAL
The study presents a road pavement crack detection system by extracting
picture features then classifying them based on image features. The applied
feature extraction method is the gray level co-occurrence matrices (GLCM).
This method employs two order measurements. The first order utilizes
statistical calculations based on the pixel value of the original image alone,
such as variance, and does not pay attention to the neighboring pixel
relationship. In the second order, the relationship between the two pixel-pairs
of the original image is taken into account. Inspired by the recent success
in implementing Supervised Learning in computer vision, the applied method
for classification is artificial neural network (ANN). Datasets, which are used
for evaluation are collected from low-cost smart phones. The results show that
feature extraction using GLCM can provide good accuracy that is equal
to 90%.
This document summarizes a research paper that reviews different methods for scene text detection and the challenges associated with it. The paper begins with an introduction that describes the overall process of automated scene text detection systems. It then provides a literature review of various text detection methods proposed in previous research, which can be categorized as connected component based methods or texture based methods. Some example methods are described. The paper discusses challenges in scene text detection, such as variable imaging conditions, complex backgrounds, and a wide range of text sizes and fonts. Finally, it discusses performance metrics like precision, recall, and f-measure that are used to evaluate scene text detection methods based on a standard dataset.
Matching algorithm performance analysis for autocalibration method of stereo ...TELKOMNIKA JOURNAL
Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.
Land Boundary Detection of an Island using improved Morphological OperationCSCJournals
Image analysis is one of the important tasks to obtain the information about earth surface. To detect and mark a particular land area, it is required to have the image from remote place. To recognize the same, the accurate boundary of that area has to be detected. In this paper, the example of remote sensing image has been considered. The accurate detection of the boundary is a complex task. A novel method has been proposed in this paper to detect the boundary of such land. Mathematical morphology is a simple and efficient method for this type of task. The morphological analysis is performed using structure elements (SE). By using mathematical morphology the images can be enhanced and then the boundary can be detected easily. Simultaneously the noise is removed by using the proposed model. The results exhibit the performance of the proposed method. Keywords: Remote Sensing images ; Edge detection; Gray- scale Morphological analysis, Structuring Element (SE).
ANOVA and Fisher Criterion based Feature Selection for Lower Dimensional Univ...CSCJournals
Unethical uses of data hiding methods have made Image Steganalysis a very important area of
research work in the field of Digital Investigations. Effectiveness of any Image Steganalysis
algorithm depends on feature selection and feature reduction. The goal of this paper is to develop
a reduced dimensional merged feature set for universal image steganalysis using Fisher Criterion
and ANOVA techniques. Statistical features extracted from wavelet subbands and binary
similarity patterns extracted from DCT of an image are merged to make combined feature set.
Fisher criterion and ANOVA test are applied to evaluate the combined feature vector score and
then only those features are selected which are found sensitive in both feature selection methods.
These reduced dimensional 15-D feature vector is used to train SVM classifier with RBF kernel.
The proposed algorithm is tested against steganography methods like F5, Outguess and LSB
based method. Stego images are generated using widely available stego tools for two standard
image databases: CorelDraw and BSDS500. Results are further validated using 10 fold cross
validation process. The proposed algorithm achieves overall 97% detection accuracy against
various steganography methods
CLASSIFICATION AND COMPARISON OF LICENSE PLATES LOCALIZATION ALGORITHMSsipij
The Intelligent Transportation Systems (ITS) are the subject of a world economic competition. They are the
application of new information and communication technologies in the transport sector, to make the
infrastructures more efficient, more reliable and more ecological. License Plates Recognition (LPR) is the
key module of these systems, in which the License Plate Localization (LPL) is the most important stage,
because it determines the speed and robustness of this module. Thus, during this step the algorithm must
process the image and overcome several constraints as climatic and lighting conditions, sensors and angles
variety, LPs’ no-standardization, and the real time processing. This paper presents a classification and
comparison of License Plates Localization (LPL) algorithms and describes the advantages, disadvantages
and improvements made by each of them.
CLASSIFICATION AND COMPARISON OF LICENSE PLATES LOCALIZATION ALGORITHMSsipij
The Intelligent Transportation Systems (ITS) are the subject of a world economic competition. They are the
application of new information and communication technologies in the transport sector, to make the
infrastructures more efficient, more reliable and more ecological. License Plates Recognition (LPR) is the
key module of these systems, in which the License Plate Localization (LPL) is the most important stage,
because it determines the speed and robustness of this module. Thus, during this step the algorithm must
process the image and overcome several constraints as climatic and lighting conditions, sensors and angles
variety, LPs’ no-standardization, and the real time processing. This paper presents a classification and
comparison of License Plates Localization (LPL) algorithms and describes the advantages, disadvantages
and improvements made by each of them.
This document provides an overview of image processing techniques for traffic applications. It discusses automatic lane finding using color-based and texture-based segmentation as well as feature-driven approaches. Object detection methods like thresholding, edge detection using Canny operator, and background differencing are also covered. Additionally, the document proposes an emergency vehicle detection system using red beacon detection and frequency analysis to override normal traffic light patterns when an emergency vehicle is detected.
This document describes an improved particle filter tracking system that uses both color and moving edge information. It aims to address limitations of existing color-based particle filter tracking systems, such as inaccurate tracking when the target and background have similar colors, occlusion occurs, or the target is deformed. The proposed system selects an appropriate bounding box around the target using moving edge information to maintain an accurate target model during tracking. An experiment using 100 targets in 10 video clips showed the new system achieved a 94.6% accuracy rate for tracking, higher than an existing color-based particle filter system. It also had a 91.8% accuracy for occluded targets, much better than the previous system.
Color Particle Filter Tracking using Frame Segmentation based on JND Color an...IOSRJVSP
Object tracking is one of the most important components in numerous applications of computer vision. Color can provide an efficient visual feature for tracking non-rigid objects in real-time. The color is chosen as tracking feature to make the process scale and rotation invariant. The color of an object can vary over time due to variations in the illumination conditions, the visual angle and the camera parameters. This paper presents the integration of color distributions into particle filtering. The color feature is extracted using our novel 4D color histogram of the image, which is determined using JND color similarity threshold and connectivity of the neighboring pixels. Particle filter tracks several hypotheses simultaneously and weighs them according to their similarity to the target model. The popular Bhattacharyya coefficient is used as similarity measure between two color distributions. The tracking results are compared on the basis of precision over the data set of video sequences from the website http://visualtracking.net of CVPR13 bench marking paper. The proposed tracker yields better precision values as compared to previous reported results
Object extraction using edge, motion and saliency information from videoseSAT Journals
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
AN ENHANCED BLOCK BASED EDGE DETECTION TECHNIQUE USING HYSTERESIS THRESHOLDING sipij
Edge detection is a crucial step in various image processing systems like computer vision , pattern
recognition and feature extraction. The Canny edge detection algorithm even though exhibits high
accuracy, is computationally more complex compared to other edge detection techniques. A block based
distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for
edge detection depending on block type and the distribution of gradients in each block. A novel method of
computation of high threshold has been proposed in this paper. Block-based hysteresis thresholds are
computed using a non uniform gradient magnitude histogram. The algorithm exhibits remarkably high
edge detection accuracy, scalability and significantly reduced computational time. Pratt’s Figure of Merit
quantifies the accuracy of the edge detector, which showed better values than that of original Canny and
distributed Canny edge detector for benchmark dataset. The method detected all visually prominent edges
for diverse block size.
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
An implementation of novel genetic based clustering algorithm for color image...TELKOMNIKA JOURNAL
The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application.
Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clust...IJECEIAES
Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain.
COMPARATIVE STUDY ON VEHICLE DETECTION TECHNIQUES IN AERIAL SURVEILLANCEIJCI JOURNAL
Aerial surveillance system becomes a great trendy on past decades. Aerial surveillance vehicle tracking techniques plays a vital role and give rising to optimistic techniques continuously. This system can be very handy in various applications such as police, traffic monitoring, natural disaster and military. It is often covers large area and providing better perspective of moving objects. The detection of moving vehicle can be both from the dynamic aerial imagery, wide area motion imagery or images under low resolution and also the static in nature. It has been very difficult issue whether identify the object in the air view, the camera angles, movement objects and motionless object. This paper deals with comparative study on various vehicle detection and tracking approach in aerial videos with its experimental results and measures working condition, hit rate and false alarm rate
A CONCERT EVALUATION OF EXEMPLAR BASED IMAGE INPAINTING ALGORITHMS FOR NATURA...cscpconf
Image inpainting derives from restoration of art works, and has been applied to repair ancient
art works. Inpainting is a technique of restoring a partially damaged or occluded image in an
undetectable way. It fills the damaged part of an image by employing information of the
undamaged part according to some rules to make it look “reasonable” to human eyes. Digital
image inpainting is relatively new area of research, but numerous and different approaches to
tackle the inpainting problem have been proposed since the concept was first introduced. This
paper analyzes and compares the recent exemplar based inpainting algorithms by Minqin Wang
and Hao Guo et al. A number of examples on real images are demonstrated to evaluate the
results of algorithms using Peak Signal to Noise Ratio (PSNR)
Interpretability Evaluation of Annual Mosaic Image of MTB Model for Land Cove...TELKOMNIKA JOURNAL
To verify whether the annual mosaic image of MTB model is acceptable for further digital
analysis, it is necessary to evaluate the visual interpretability. The MTB model is an effort to integrate
multi-scene and multi-temporal data, to obtain a minimum cloud cover mosaic image in locations that are
often covered by clouds and haze. This study is to evaluate the interpretability of the annual mosaic image
for analysis of the land cover changes. The data used are the images of 2015, 2016, and 2017 covers a
part of central Sumatra. Visual interpretations with a series of steps are used, starting with identification of
the objects using interpretation keys, followed by spectral band correlations, scattergram analysis, and
ended by consistency assessment. The consistency assessment step is performed to determine the level
of clearness and easiness of the object recognition in the annual mosaic images. The results showed that
the most optimal spectral bands used for RGB combinations for visual interpretation were Band SWIR-1,
Band NIR, and Band Red. Based on the evaluation results, the annual mosaic image o f MTB model
performed the consistent results of the clearness objects and the easiness of the object recognition. Thus
the annual mosaic image of MTB model of 0.02x0.02 degree tile is acceptable for further digital processing
as well as digital land cover analysis.
IRJET - Traffic Density Estimation by Counting Vehicles using Aggregate Chann...IRJET Journal
This document presents a method for estimating traffic density by counting vehicles in images using aggregate channel features. The proposed method uses adaptive boosting and aggregate channel features to train an object detector to detect vehicles in images obtained from videos. Bounding boxes are placed around detected vehicles and overlapping boxes are removed. Traffic density is then estimated by counting the number of bounding boxes and dividing by the maximum possible number of vehicles in the area. The estimated densities can be used to control traffic light timing, with higher densities corresponding to shorter green light durations. The method is tested on real-world traffic images and is found to accurately detect vehicles and estimate densities.
Strong Image Alignment for Meddling Recognision PurposeIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Image fusion using nsct denoising and target extraction for visual surveillanceeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET- Performance Analysis of Lung Disease Detection and ClassificationIRJET Journal
This document presents a study on the performance analysis of lung disease detection and classification using computed tomography (CT) scans. It begins with an introduction on the importance of early and accurate diagnosis of lung diseases. The study then describes the various steps involved - image acquisition, preprocessing, lung region extraction, identification of affected lung side, segmentation using thresholding and morphological methods, feature extraction of texture features, and classification using K-nearest neighbors. Performance metrics like accuracy, precision, sensitivity and specificity are evaluated. Finally, the study concludes that the proposed automatic system achieved accurate classification of segmented lung diseases.
A hybrid content based image retrieval system using log-gabor filter banksIJECEIAES
In this paper, a new efficient image retrieval system using sequential process of three stages with filtering technique for the feature selection is proposed. In the first stage the color features are extracted using color histogram method and in the second stage the texture features are obtained using log-Gabor filters and in the third stage shape features are extracted using shape descriptors using polygonal fitting algorithm. The proposed log-Gabor filter in the second stage has advantages of retrieving images over regular Gabor filter for texture. It provides better representation of the images. Experimental evaluation of the proposed system shows improved performance in retrieval as compared to other existing systems in terms of average precision and average recall.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
This document presents a methodology for land use mapping using segmentation techniques on coarse resolution SAR data. It explores extracting urban extents using the BuiltArea algorithm, then segmenting the SAR images using different algorithms like Canny edge detection. Land use classes like commercial, residential and green areas are classified after feature selection and majority rule application. Testing on Shanghai showed potential for moderate SAR in urban monitoring. Preliminary fusion with optical data from Beijing-1 satellite improved segmentation accuracy and classification results. Future work will explore polarimetric features and additional classes with multitemporal SAR segmentation approaches.
Stereo matching based on absolute differences for multiple objects detectionTELKOMNIKA JOURNAL
This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.
Edge detection is one of the most frequent processes in digital image processing for various purposes, one of which is detecting road damage based on crack paths that can be checked using a Canny algorithm. This paper proposed a mobile application to detect cracks in the road and with customized threshold function in the requests to produce useful and accurate edge detection. The experimental results show that the use of threshold function in a canny algorithm can detect better damage in the road
CLASSIFICATION AND COMPARISON OF LICENSE PLATES LOCALIZATION ALGORITHMSsipij
The Intelligent Transportation Systems (ITS) are the subject of a world economic competition. They are the
application of new information and communication technologies in the transport sector, to make the
infrastructures more efficient, more reliable and more ecological. License Plates Recognition (LPR) is the
key module of these systems, in which the License Plate Localization (LPL) is the most important stage,
because it determines the speed and robustness of this module. Thus, during this step the algorithm must
process the image and overcome several constraints as climatic and lighting conditions, sensors and angles
variety, LPs’ no-standardization, and the real time processing. This paper presents a classification and
comparison of License Plates Localization (LPL) algorithms and describes the advantages, disadvantages
and improvements made by each of them.
CLASSIFICATION AND COMPARISON OF LICENSE PLATES LOCALIZATION ALGORITHMSsipij
The Intelligent Transportation Systems (ITS) are the subject of a world economic competition. They are the
application of new information and communication technologies in the transport sector, to make the
infrastructures more efficient, more reliable and more ecological. License Plates Recognition (LPR) is the
key module of these systems, in which the License Plate Localization (LPL) is the most important stage,
because it determines the speed and robustness of this module. Thus, during this step the algorithm must
process the image and overcome several constraints as climatic and lighting conditions, sensors and angles
variety, LPs’ no-standardization, and the real time processing. This paper presents a classification and
comparison of License Plates Localization (LPL) algorithms and describes the advantages, disadvantages
and improvements made by each of them.
This document provides an overview of image processing techniques for traffic applications. It discusses automatic lane finding using color-based and texture-based segmentation as well as feature-driven approaches. Object detection methods like thresholding, edge detection using Canny operator, and background differencing are also covered. Additionally, the document proposes an emergency vehicle detection system using red beacon detection and frequency analysis to override normal traffic light patterns when an emergency vehicle is detected.
This document describes an improved particle filter tracking system that uses both color and moving edge information. It aims to address limitations of existing color-based particle filter tracking systems, such as inaccurate tracking when the target and background have similar colors, occlusion occurs, or the target is deformed. The proposed system selects an appropriate bounding box around the target using moving edge information to maintain an accurate target model during tracking. An experiment using 100 targets in 10 video clips showed the new system achieved a 94.6% accuracy rate for tracking, higher than an existing color-based particle filter system. It also had a 91.8% accuracy for occluded targets, much better than the previous system.
Color Particle Filter Tracking using Frame Segmentation based on JND Color an...IOSRJVSP
Object tracking is one of the most important components in numerous applications of computer vision. Color can provide an efficient visual feature for tracking non-rigid objects in real-time. The color is chosen as tracking feature to make the process scale and rotation invariant. The color of an object can vary over time due to variations in the illumination conditions, the visual angle and the camera parameters. This paper presents the integration of color distributions into particle filtering. The color feature is extracted using our novel 4D color histogram of the image, which is determined using JND color similarity threshold and connectivity of the neighboring pixels. Particle filter tracks several hypotheses simultaneously and weighs them according to their similarity to the target model. The popular Bhattacharyya coefficient is used as similarity measure between two color distributions. The tracking results are compared on the basis of precision over the data set of video sequences from the website http://visualtracking.net of CVPR13 bench marking paper. The proposed tracker yields better precision values as compared to previous reported results
Object extraction using edge, motion and saliency information from videoseSAT Journals
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
AN ENHANCED BLOCK BASED EDGE DETECTION TECHNIQUE USING HYSTERESIS THRESHOLDING sipij
Edge detection is a crucial step in various image processing systems like computer vision , pattern
recognition and feature extraction. The Canny edge detection algorithm even though exhibits high
accuracy, is computationally more complex compared to other edge detection techniques. A block based
distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for
edge detection depending on block type and the distribution of gradients in each block. A novel method of
computation of high threshold has been proposed in this paper. Block-based hysteresis thresholds are
computed using a non uniform gradient magnitude histogram. The algorithm exhibits remarkably high
edge detection accuracy, scalability and significantly reduced computational time. Pratt’s Figure of Merit
quantifies the accuracy of the edge detector, which showed better values than that of original Canny and
distributed Canny edge detector for benchmark dataset. The method detected all visually prominent edges
for diverse block size.
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
An implementation of novel genetic based clustering algorithm for color image...TELKOMNIKA JOURNAL
The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application.
Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clust...IJECEIAES
Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain.
COMPARATIVE STUDY ON VEHICLE DETECTION TECHNIQUES IN AERIAL SURVEILLANCEIJCI JOURNAL
Aerial surveillance system becomes a great trendy on past decades. Aerial surveillance vehicle tracking techniques plays a vital role and give rising to optimistic techniques continuously. This system can be very handy in various applications such as police, traffic monitoring, natural disaster and military. It is often covers large area and providing better perspective of moving objects. The detection of moving vehicle can be both from the dynamic aerial imagery, wide area motion imagery or images under low resolution and also the static in nature. It has been very difficult issue whether identify the object in the air view, the camera angles, movement objects and motionless object. This paper deals with comparative study on various vehicle detection and tracking approach in aerial videos with its experimental results and measures working condition, hit rate and false alarm rate
A CONCERT EVALUATION OF EXEMPLAR BASED IMAGE INPAINTING ALGORITHMS FOR NATURA...cscpconf
Image inpainting derives from restoration of art works, and has been applied to repair ancient
art works. Inpainting is a technique of restoring a partially damaged or occluded image in an
undetectable way. It fills the damaged part of an image by employing information of the
undamaged part according to some rules to make it look “reasonable” to human eyes. Digital
image inpainting is relatively new area of research, but numerous and different approaches to
tackle the inpainting problem have been proposed since the concept was first introduced. This
paper analyzes and compares the recent exemplar based inpainting algorithms by Minqin Wang
and Hao Guo et al. A number of examples on real images are demonstrated to evaluate the
results of algorithms using Peak Signal to Noise Ratio (PSNR)
Interpretability Evaluation of Annual Mosaic Image of MTB Model for Land Cove...TELKOMNIKA JOURNAL
To verify whether the annual mosaic image of MTB model is acceptable for further digital
analysis, it is necessary to evaluate the visual interpretability. The MTB model is an effort to integrate
multi-scene and multi-temporal data, to obtain a minimum cloud cover mosaic image in locations that are
often covered by clouds and haze. This study is to evaluate the interpretability of the annual mosaic image
for analysis of the land cover changes. The data used are the images of 2015, 2016, and 2017 covers a
part of central Sumatra. Visual interpretations with a series of steps are used, starting with identification of
the objects using interpretation keys, followed by spectral band correlations, scattergram analysis, and
ended by consistency assessment. The consistency assessment step is performed to determine the level
of clearness and easiness of the object recognition in the annual mosaic images. The results showed that
the most optimal spectral bands used for RGB combinations for visual interpretation were Band SWIR-1,
Band NIR, and Band Red. Based on the evaluation results, the annual mosaic image o f MTB model
performed the consistent results of the clearness objects and the easiness of the object recognition. Thus
the annual mosaic image of MTB model of 0.02x0.02 degree tile is acceptable for further digital processing
as well as digital land cover analysis.
IRJET - Traffic Density Estimation by Counting Vehicles using Aggregate Chann...IRJET Journal
This document presents a method for estimating traffic density by counting vehicles in images using aggregate channel features. The proposed method uses adaptive boosting and aggregate channel features to train an object detector to detect vehicles in images obtained from videos. Bounding boxes are placed around detected vehicles and overlapping boxes are removed. Traffic density is then estimated by counting the number of bounding boxes and dividing by the maximum possible number of vehicles in the area. The estimated densities can be used to control traffic light timing, with higher densities corresponding to shorter green light durations. The method is tested on real-world traffic images and is found to accurately detect vehicles and estimate densities.
Strong Image Alignment for Meddling Recognision PurposeIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Image fusion using nsct denoising and target extraction for visual surveillanceeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET- Performance Analysis of Lung Disease Detection and ClassificationIRJET Journal
This document presents a study on the performance analysis of lung disease detection and classification using computed tomography (CT) scans. It begins with an introduction on the importance of early and accurate diagnosis of lung diseases. The study then describes the various steps involved - image acquisition, preprocessing, lung region extraction, identification of affected lung side, segmentation using thresholding and morphological methods, feature extraction of texture features, and classification using K-nearest neighbors. Performance metrics like accuracy, precision, sensitivity and specificity are evaluated. Finally, the study concludes that the proposed automatic system achieved accurate classification of segmented lung diseases.
A hybrid content based image retrieval system using log-gabor filter banksIJECEIAES
In this paper, a new efficient image retrieval system using sequential process of three stages with filtering technique for the feature selection is proposed. In the first stage the color features are extracted using color histogram method and in the second stage the texture features are obtained using log-Gabor filters and in the third stage shape features are extracted using shape descriptors using polygonal fitting algorithm. The proposed log-Gabor filter in the second stage has advantages of retrieving images over regular Gabor filter for texture. It provides better representation of the images. Experimental evaluation of the proposed system shows improved performance in retrieval as compared to other existing systems in terms of average precision and average recall.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
This document presents a methodology for land use mapping using segmentation techniques on coarse resolution SAR data. It explores extracting urban extents using the BuiltArea algorithm, then segmenting the SAR images using different algorithms like Canny edge detection. Land use classes like commercial, residential and green areas are classified after feature selection and majority rule application. Testing on Shanghai showed potential for moderate SAR in urban monitoring. Preliminary fusion with optical data from Beijing-1 satellite improved segmentation accuracy and classification results. Future work will explore polarimetric features and additional classes with multitemporal SAR segmentation approaches.
Stereo matching based on absolute differences for multiple objects detectionTELKOMNIKA JOURNAL
This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods.
Edge detection is one of the most frequent processes in digital image processing for various purposes, one of which is detecting road damage based on crack paths that can be checked using a Canny algorithm. This paper proposed a mobile application to detect cracks in the road and with customized threshold function in the requests to produce useful and accurate edge detection. The experimental results show that the use of threshold function in a canny algorithm can detect better damage in the road
Enhancement performance of road recognition system of autonomous robots in sh...sipij
This document summarizes a research paper that proposed an algorithm to improve road recognition for autonomous vehicles in shadow scenarios. The researchers conducted experiments to test their algorithm's performance on key metrics like true positive rate and error rate. Their algorithm first converted images to HSV color space to detect shadows, then used normalized difference index and morphological operations to eliminate shadow effects before segmentation and classification. Test results showed their algorithm enhanced road recognition in the presence of shadows, advancing autonomous vehicle navigation capabilities.
An Exclusive Review of Popular Image Processing TechniquesChristo Ananth
Christo Ananth,"An Exclusive Review of Popular Image Processing Techniques", International Journal of Advanced Research in Management Architecture Technology & Engineering(IJARMATE),Volume 8,Issue 6,June 2022,pp:15-22.
Christo Ananth et al. discussed about a review paper which brings out a summary of popular image processing techniques in practice for students, faculty members and researchers in medical image processing field. Through Image processing, we do some operations on an image, to get an enhanced image or we try to acquire some useful information from it. They help in manipulating digital images through the use of computers. We Perform Image Restoration, Linear Filtering, Independent Component Analysis, Pixelation, Template Matching, Image Generation Techniques even to image to obtain promisable results. This Review Paper also summarizes some of the enhancement approaches which have impacted image segmentation approaches over these years.
Identification and Rejection of Defective Ceramic Tile using Image Processing...IJMTST Journal
Manual Ceramic Tile inspection process is tedious if human operator is employed to look for defective tiles and their elimination. The plain ceramic tiles often have the following types of defects viz Cracks, Blobs and pin holes [1]. The fatigue of human operator deteriorates the quality of the tile being produced. In this paper a novel and simple automatic tile defect identification and elimination system is proposed. The proposed system is built around MATLAB and ARDUINO. The systems performance is evaluated in terms of accuracy and time taken for detection. The Proposed system promises superior performance when compared to the other existing system
Online video-based abnormal detection using highly motion techniques and stat...TELKOMNIKA JOURNAL
At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for the accuracy while attaining simultaneously low values for the processing time.
This document discusses crack detection in concrete structures using deep learning techniques. It begins by describing traditional manual inspection and image processing methods for crack detection, noting limitations such as being time-consuming, inaccurate, and unable to handle complex image data. The document then introduces convolutional neural networks (CNNs) as a deep learning technique for crack detection, which can automatically learn features from image data without predefined feature extraction. It provides details on common CNN architecture components like convolution, activation and pooling layers. The document concludes by outlining the process of developing a CNN model for crack detection, including collecting a dataset, training the model, and evaluating the trained model's performance using classification metrics.
Parking detection system using background subtraction and HSV color segmentationjournalBEEI
Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.
A computer vision-based lane detection approach for an autonomous vehicle usi...Md. Faishal Rahaman
Lane detection systems play a critical role in ensuring safe and secure driving by alerting the driver of lane departures. Lane detection may also save passengers' lives if they go off the road owing to driver distraction. The article presents a three-step approach for detecting lanes on high-speed video pictures in real-time and invariant lighting. The first phase involves doing appropriate prepossessing, such as noise reduction, RGB to grey-scale conversion, and binarizing the input picture. Then, a polygon area in front of the vehicle is picked as the zone of interest to accelerate processing. Finally, the edge detection technique is used to acquire the image's edges in the area of interest, and the Hough transform is used to identify lanes on both sides of the vehicle. The suggested approach was implemented using the IROADS database as a data source. The recommended method is effective in various daylight circumstances, including sunny, snowy, and rainy days, as well as inside tunnels. The proposed approach processes frame on average in 28 milliseconds and have a detection accuracy of 96.78 per cent, as shown by implementation results. This article aims to provide a simple technique for identifying road lines on high-speed video pictures utilizing the edge feature.
Stereo matching algorithm using census transform and segment tree for depth e...TELKOMNIKA JOURNAL
This article proposes an algorithm for stereo matching corresponding process that will be used in many applications such as augmented reality, autonomous vehicle navigation and surface reconstruction. Basically, the proposed framework in this article is developed through a series of functions. The final result from this framework is disparity map which this map has the information of depth estimation. Fundamentally, the framework input is the stereo image which represents left and right images respectively. The proposed algorithm in this article has four steps in total, which starts with the matching cost computation using census transform, cost aggregation utilizes segment-tree, optimization using winner-takes-all (WTA) strategy, and post-processing stage uses weighted median filter. Based on the experimental results from the standard benchmarking evaluation system from the Middlebury, the disparity map results produce an average low noise error at 9.68% for nonocc error and 18.9% for all error attributes. On average, it performs far better and very competitive with other available methods from the benchmark system.
This document discusses lane line detection using computer vision techniques. It begins with an introduction that outlines the importance of lane detection for traffic safety and autonomous vehicles. It then reviews several academic papers on lane detection approaches. The problem is defined as detecting lane lines to guide autonomous vehicles and avoid accidents. The methodology section outlines the experimental procedure, which includes preprocessing the image, applying edge detection and masking, using Hough transforms to identify lines, and overlaying the detected lines on the original image. Test images are presented and conclusions discuss how the techniques learned will help identify lane lines to keep autonomous vehicles in their lanes.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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A DEEP LEARNING APPROACH FOR DEFECT DETECTION AND SEGMENTATION IN X-RAY COMPU...gerogepatton
Additive manufacturing is an emerging and crucial technology that can overcome the limitations of
traditional manufacturing techniques to accurately manufacture highly complex parts. X-ray Computed
Tomography (XCT) is a widely used method for non-destructive testing of AM parts. However, detection
and segmentation of defects in XCT images of AM have many challenges due to contrast, size, and
appearance of defects. This study developed deep learning techniques for detecting and segmenting defects
in XCT images of AM. Due to a large number of required defect annotations, this paper applied image
processing techniques to automate the defect labeling process. A single-stage object detection algorithm
(YOLOv5) was applied to the problem of defect detection in image data. Three different variants of
YOLOv5 were implemented and their performances were compared. U-Net was applied for defect
segmentation in XCT slices. Finally, this research demonstrates that deep learning techniques can improve
the automatic defect detection and segmentation in XCT data of AM
IRJET- Object Detection using Hausdorff DistanceIRJET Journal
This document proposes a new object recognition system using Hausdorff distance. The system aims to improve on existing methods like YOLO that struggle with small objects and can capture garbage data. The document outlines preprocessing steps like noise cancellation, representing shapes as point sets, and extracting features. It then describes using Hausdorff distance and shape context to find the best match between input and reference shapes. Testing on datasets showed encouraging results for recognizing handwritten digits.
This document summarizes an analysis of iris recognition based on false acceptance rate (FAR) and false rejection rate (FRR) using the Hough transform. It first provides an overview of iris recognition and its typical stages: image acquisition, localization/segmentation, normalization, feature extraction, and pattern matching. It then describes existing methods used in each stage, including the Hough transform and rubber sheet model for localization and normalization. The proposed methodology applies Canny edge detection, Hough transform for boundary detection, normalization with the rubber sheet model, and calculates metrics like mean squared error, root mean squared error, signal-to-noise ratio, and root signal-to-noise ratio to evaluate the accuracy of iris recognition using FAR
IRJET - Object Detection using Hausdorff DistanceIRJET Journal
This document proposes using Hausdorff distance for object detection as it can better handle noise compared to other methods like Euclidean distance. The document discusses preprocessing images using Gaussian filtering for noise cancellation. It then represents shapes as point sets for feature extraction before using Hausdorff distance to match shapes between reference and test images for object recognition. Encouraging results were obtained when testing on MNIST, COIL and private handwritten digit datasets.
Real time Pothole and Speed Breaker Detection Using Image Processing TechniquesIRJET Journal
This document discusses techniques for real-time detection of potholes and speed bumps using image processing. It begins with an introduction describing the need for such a system given road conditions in India. Then it describes common algorithms used for pothole detection including color analysis, edge detection, and convolutional neural networks. Speed bump detection methods like Canny edge detection and feature extraction are also outlined. The document evaluates the performance of different algorithms and describes the overall methodology as collecting road images and analyzing them to identify threats to enable route planning or driver assistance. It concludes that continued development of detection techniques can improve applications for autonomous vehicles and other computer vision systems.
Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clus...IJECEIAES
Nowadays, the fingerprint identification system is the most exploited sector of biometric. Fingerprint image segmentation is considered one of its first processing stage. Thus, this stage affects typically the feature extraction and matching process which leads to fingerprint recognition system with high accuracy. In this paper, three major steps are proposed. First, Soble and TopHat filtering method have been used to improve the quality of the fingerprint images. Then, for each local block in fingerprint image, an accurate separation of the foreground and background region is obtained by K-means clustering for combining 5-dimensional characteristics vector (variance, difference of mean, gradient coherence, ridge direction and energy spectrum). Additionally, in our approach, the local variance thresholding is used to reduce computing time for segmentation. Finally, we are combined to our system DBSCAN clustering which has been performed in order to overcome the drawbacks of K-means classification in fingerprint images segmentation. The proposed algorithm is tested on four different databases. Experimental results demonstrate that our approach is significantly efficacy against some recently published techniques in terms of separation between the ridge and non-ridge region.
Real Time Implementation of Ede Detection Technique for Angiogram Images on FPGAIRJET Journal
This document presents a new edge detection algorithm for angiogram images and its implementation on an FPGA. It begins with an introduction to angiography and importance of edge detection in analyzing angiogram images. It then describes the proposed algorithm which includes histogram equalization for enhancement followed by a modified Canny edge detection approach. The key steps of the modified Canny approach are also outlined. Experimental results on angiogram images demonstrate that the proposed FPGA implementation takes only 0.562ms for execution while maintaining accuracy. In conclusion, the algorithm is able to efficiently detect blood vessel edges in angiogram images making it useful for analyzing vascular diseases.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Similar to Road crack detection using adaptive multi resolution thresholding techniques (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
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Road crack detection using adaptive multi resolution thresholding techniques (Zuraini Othman)
1875
binary images. There had been studies conducted on both edge-based and threshold-based
segmentation such as the Canny edge detection and Otsu thresholding methods [9–11].
The Otsu method is based on grey level histograms that are deduced by using the least square
method and is currently regarded as the most stable technique used for image threshold
segmentation. From a statistical perspective, this method also generates the best threshold
value [12], which is one of the key factors that greatly affect the performance of the traditional
Canny Operator. As such, this research had followed the method used from previous studies
such as [12–18], which is to provide an improved self-adaptive threshold Canny Operator that
inherits the merit of Otsu method in choosing the low threshold (Lt) and high threshold (Ht)
values adaptively.
The selection of a threshold value is vital in ensuring that an accurate edge is given,
which not only produces a clear image of the cracks, but also to filter out the cracks from other
layers as layerwise instead of laminate-wise. The length and location of each individual crack is
then measured from the filtered images by using a simple heuristic procedure [7]. In cement,
the crack patterns are detected by using a combination of threshold and filter-like edge
detection methods, which is similar to the method Sobel had used in detecting cracks within
a binary image. By utilizing a suitable threshold binary image, the pixels are categorized into
the foreground and the background image and the residual noise is eliminated through the use
of Sobel’s filtering. After undergoing the filtering process, the Otsu method is then used to
detect the major cracks. This detection method had been discussed earlier in [2].
The modified Canny edge detection algorithm had been used in a few studies
such as [19]. Since edge preserving filters are used in the applications of road cracks detection,
this algorithm was therefore tested on randomly chosen pavement images data.
While the traditional Canny edge detection method had provided a relatively simple but precise
methodology for edge detection problem, the Gaussian filter that was used to smooth
the images, however, had caused the loss of edge information during noise suppression.
As such, the Mallat wavelet transform was therefore proposed to reinforce the weak edges of
the input images and quadratically optimising the genetic algorithm to obtain a suitable
threshold in self-adapting standard when performing the Canny algorithm steps. As a result,
this newly improved Canny model had met the needs for real-time road cracks detection and
had compensated the disadvantages of a traditional Canny algorithm by effectively and rapidly
identifying road cracks in a short amount of time.
As mentioned earlier, the Otsu method is generally used in the conventional Canny
method to adaptively find the high and low threshold values. Much research has been done on
the modification of the Canny method by using the Otsu method such as the fixed partitioning
technique that was used for the global and local threshold analysis of the image. By using
this approach, the image is divided into several equal portions, where the local histogram for
each of the respective part is then calculated. One of the main advantages of using this method
is that it provides an additional input to the histogram as a way of obtaining the spatial
distribution of the image content [20]. This proposed method had used a comprehensive
technique in generating edge images. [21] had discussed how the Salient Detection method can
be utilised for crack detection. Visually, salient regions are more conspicuous because they are
in contrast with the surroundings. Although the current methods had illustrated their efficacy for
the detection of salient areas in the Berkeley database [22], they had demonstrated poor
performance in terms of the continuity and completeness of the detected crack. In [15], since
the modified Canny method had demonstrated effective detection within the Berkeley database,
this algorithm was adopted for this study but with certain modifications.
This paper had proposed an algorithm for finding edge images within the CrackForest
dataset [21] through the use of an adaptive threshold approach, which is based on a local value
after the fixed partitioning of the image in five different levels. While the high threshold value (Ht)
was obtained through the use of Otsu method, the low (Lt) threshold value on the other hand,
was determined by halving the high threshold value. Contrary to [14], the three statistical
measures, namely the minimum, maximum, and mean values from all the Lt and Ht are
generated. These are the measurements from the best obtained edge images that had been
compared with the ground truth images provided by the dataset. The outcomes from
the experiment were then compared against the results obtained from the Canny method.
Based on the comparison results, it was revealed that the proposed method had provided edge
images with the best accuracy level. The following sections of this paper will provide a detailed
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discussion on the materials and methods used for the research, an explanation of
the experimental and comparison results as well as the conclusion of the study conducted.
2. Materials and Methods
This paper had proposed a road crack detection algorithm that consisted of
the following steps: image retrieval and pre-processing, road crack detection and finally,
the analysis of the performance measurement shown in Figure 1.
Figure 1. Flowchart of the proposed road crack detection algorithm
2.1 Image Pre-Processing
Here, the algorithm of the pre-processing phase in [23] is adopted to get a clearer crack
edge of road images. At this stage, the image will undergo several manipulations such as pixel
smoothing, normalisation, white line detection and saturation before the crack is detected.
Figure 2 shows the original image and the pre-processed image with its grey level histogram.
(a) (b)
Figure 2. Sample of the (a) original image and (b) the smoothed image with
its grey level histogram
2.2 Crack Detection Using Adaptive Multi Resolution Thresholding Techniques
After the original images had been retrieved in grey level image format and subjected to
the pre-processing phase, the fixed partitioning is then carried out to separate the image into
five different levels. While implementing the Canny edge detection [24], the Otsu method [9] is
adopted in the selection of the threshold values. However, a few changes had to be made in
order to obtain the best low threshold (Lt) and high threshold values (Ht) as shown in [14, 15]
through the utilisation of different image resolutions. At this stage, the global and local spatial
values will be selected from the variance values depicted in the 2x2 partition (CO2x2),
3x3 partition (CO3x3), 4x4 partition (CO4x4) and 5x5 partition (CO5x5). Next, the local
threshold values for each of the partition are used to generate a global edge image shown in
Figure 3. Unlike the previous studies, the global threshold values from CO2x2, CO3x3, CO4x4
and CO5x5 are then applied into the statistical measures to determine the portion that gives
the most accurate minimum (min), maximum (max) and average (mean) values.
Image
retrieved
Analysis of
performance
measurement
Image
pre-processing
Crack detection using adaptive
multi resolution thresholding
techniques
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1877
Let’s assume the fixed partitioning for each of the resolution as such:
𝐿1,1 ∈ COG (1)
𝐿2,1, 𝐿2,2, 𝐿2,3 and 𝐿2,4 ∈ CO2×2 (2)
𝐿3,1, 𝐿3,2, 𝐿3,3, 𝐿3,4, 𝐿3,5, 𝐿3,6, 𝐿3,7, 𝐿3,8 and 𝐿3,9 ∈ CO3×3 (3)
𝐿4,1, 𝐿4,2, 𝐿4,3, 𝐿4,4, 𝐿4,5, 𝐿4,6, 𝐿4,7, 𝐿4,8, 𝐿4,9, 𝐿4,10, 𝐿4,11, 𝐿4,12, 𝐿4,13, 𝐿4,14, 𝐿4,15 and
𝐿4,16 ∈ CO4×4 (4)
𝐿5,1, 𝐿5,2, 𝐿5,3, 𝐿5,4, 𝐿5,5, 𝐿5,6, 𝐿5,7, 𝐿5,8, 𝐿5,9, 𝐿5,10, 𝐿5,11, 𝐿5,12, 𝐿5,13, 𝐿5,14, 𝐿5,15, 𝐿5,16,
𝐿5,17, 𝐿5,18, 𝐿5,19, 𝐿5,20, 𝐿5,21, 𝐿5,22, 𝐿5,23, 𝐿5,24 and 𝐿5,25 ∈ CO5×5 (5)
Figure 3. The process used in the adaptive multi-resolution thresholding technique
for edge detection
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here, each of the fixed partitioning is represented as 𝐿𝑖,𝑗 with 𝑖 = partition involved and
𝑗 = 1, 2, … , 𝑖2
. The statistical measures for each of the resolution level at high (RHt) and low
threshold values (RLt) for min, max and mean are defined as such:
RHt =
1
n
× arg max(Ht ∈ Li,j) and RLt =
1
n
×arg max(Lt ∈ Li,j) (6)
RHt =
1
n
× arg min(Ht ∈ Li,j) and RLt =
1
n
×arg min(Lt ∈ Li,j) (7)
RHt =
1
n
× mean(Ht ∈ Li,j) and RLt =
1
n
× mean(Lt ∈ Li,j) (8)
where weight 𝑛 = 1,2,3, … ,10.
2.3 Performance Measurement
At this stage, each of the obtained edge detection images will be compared against
the ground truth image. The measurements as discussed in [25] are then used in
the result’s analysis:
Recall =
True Positive
True Positive + False Negative
(9)
Precision =
True Positive
True Positive + False Positive
(10)
FMeasure = 2 ×
Precision × Recall
Precision + Recall
(11)
3. Experiment and Results
This study had utilized the provided CrackForest dataset [21] and ground truth edge
images. The FMeasure results that are shown in Figure 4 had been obtained by using [14, 15]
algorithms after the pre-processing phase [23]. There were fifty results obtained for each of
the statistical measure, namely, min from (6), max from (7) and mean from (8), for each of the n
used. As shown in Figure 4 (a)(i), Figure 4 (b)(i), Figure 4 (a)(ii) and Figure 4 (b)(ii),
the corresponding CrackForest dataset results from 𝑛 = 1 and the max value from (7) had
yielded dominant values for each of the edge image generated.
Table 1 shows the FMeasure results of the 10 images used, where the value for CO5x5
was shown to be higher than the conventional Canny method and the other levels of resolution
used. Although the edge image that was obtained for image 001 shown in Figure 5 had
generated noise by using the Canny method, it did not display any visible differences from
the edge images obtained in COG, CO2x2, CO3x3, CO4x4 and CO5x5. The average results
from the dataset used had also shown the accurate edge image generated by CO5x5 shown in
Table 2. In Figure 6, by comparing the edge image obtained from the Canny method, there is a
clear indication that the image produced by the proposed method had been similar to
the ground truth image. From all of the obtained images and results for road cracks, we have
found (12) to provide the most favourable result:
RHt = arg max(Ht ∈ Li,j) and RLt = arg max(Lt ∈ Li,j) (12)
Table 1. F-Measure Max Results on 10 Images from
Crackforest Dataset
Method Canny COG CO2x2 CO3x3 CO4x4 CO5x5
001 86.35352 99.45778 99.45812 99.46042 99.46075 9 .46075
002 88.56953 98.98639 99.00228 99.00448 99.01731 99.01731
003 88.4293 99.43131 99.44877 99.45173 99.45173 99.45173
004 89.52685 99.3298 99.3298 99.33016 99.34008 99.34008
005 89.55255 99.43277 99.44134 99.44233 99.44299 99.44299
006 86.88847 99.19375 99.30702 99.33511 99.30702 99.33511
007 88.23459 99.4437 99.46317 99.46415 99.46449 99.46449
008 89.09414 99.31765 99.31865 99.31898 99.31931 99.32427
009 87.2423 99.09312 99.1823 99.2214 99.22239 99.22239
010 87.91355 99.443 99.44465 99.44597 99.44597 99.46378
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1879
(a)(i) (a)(ii)
(b)(i) (b)(ii)
Figure 4. Results obtained on two images: (a) image 001 and (b) image 002, Results
(i) for 𝑛 = 1,2,3 … ,10 and (ii) for min, max and mean for 𝑛 = 1 only
(a) (b) (c) (d)
(e) (f) (g) (h)
Figure 5. (a) Original image, (b) Ground truth image, which is followed by the edge image
generated by (c) Canny method, (d) COG, (e) CO2x2, (f) CO3x3, (g) CO4x4 and (h) CO5x5
7. ISSN: 1693-6930
TELKOMNIKA Vol. 17, No. 4, August 2019: 1874-1881
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Table 2. The F-Measure Values that were Obtained from the Average Max
Method Average
Canny 87.80299
COG 99.29134
CO2x2 99.31586
CO3x3 99.3238
CO4x4 99.32689
CO5x5 99.33232
(a) (b) (c) (d)
Figure 6. (a) Original image, (b) Ground truth image, which is followed by the edge image
generated by (c) Canny method and (d) the proposed method
4. Conclusion
This study had proposed a new crack detection method through the application of
Otsu-Canny Edge Detection Algorithm as well as incorporating calculations in the global and
local threshold analysis of the fixed partitioned images at multiple resolution levels. To obtain
the optimal threshold value, a sampling approach was utilised in the calculation of statistical
measures, namely the minimum, maximum and mean values from the class variance of each
partitioned image. The most accurate image is then selected based on the resolution level,
statistical measure and the weight used.
Based on the results obtained from the CrackForest image datasets, the proposed
method was found to perform better than the Canny method in terms of its edge image results
and the F-Measure values. In this study, although the modified version of the Canny method
had resulted in the detection of unwanted edges, it was still selected as the edge images had
provided the most complete edge boundaries. The local spatial adaptive approach through
the use of Otsu method was also proven to enhance the edges by eliminating the noise
acquired from the conventional Canny method. The results had reflected more accurate edge
images as it had taken in the foreground image of interest and ignored the background regions.
Acknowledgements
The deepest gratitude and thanks to Universiti Teknikal Malaysia Melaka (UTeM)
in supporting this research PJP/2018/FTMK(2B)/S01629.
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