Background subtraction is typically one of the first steps carried out in motion detection using static video cameras. This paper presents a novel method for background removal that processes only some pixels of each image. Some regions of interest of the objects in the image or frame are located with the help of edge detector. Once the region is detected only that area will be segmented instead of processing the whole image. This method achieves a significant reduction in computation time that can be used for subsequent image analysis. In this paper we detect the foreground object with the help of edge detector and combine the Fuzzy c-means clustering algorithm to segment the object by means of subtracting the current frame from the previous frame, the accurate background is identified.
This document summarizes a research paper that proposes a novel background removal algorithm using fuzzy c-means clustering. It begins by introducing background subtraction and some of the challenges. It then describes the proposed algorithm which uses edge detection to locate regions of interest before applying fuzzy c-means clustering to segment the foreground object. The algorithm achieves significant computation time reduction compared to other methods. Experimental results show the proposed method has higher true positive rates and accuracy compared to other algorithms, though precision and similarity are slightly lower.
A study and comparison of different image segmentation algorithmsManje Gowda
This document discusses and compares different image segmentation algorithms. It begins with an introduction to the topic and an agenda that outlines image segmentation techniques, results and discussion, conclusions, and references. Section 2 describes various image segmentation techniques like thresholding, region-based (region growing and data clustering), and edge-based segmentation. Section 3 shows results of applying algorithms like Otsu's method, K-means clustering, quad tree, delta E, and FTH to sample images and compares their performance on simple versus complex images. The conclusion is that delta E performs best for simple images with one object, while for complex images with multiple objects, performance degrades and further work is needed.
The document evaluates and compares the performance of three background subtraction algorithms: frame difference, statistical approach for real-time robust background subtraction and shadow detection, and adaptive background mixture models for real-time tracking. The frame difference method is the simplest but depends on threshold selection, while the statistical approach provides average accuracy and speed and the adaptive mixture models method provides the best accuracy but highest computational cost. Experimental results on video data demonstrate the tradeoffs between accuracy, computational requirements, and ability to handle challenges like lighting changes for each algorithm.
Wireless Vision based Real time Object Tracking System Using Template MatchingIDES Editor
In the present work the concepts of template
matching through Normalized Cross Correlation (NCC) has
been used to implement a robust real time object tracking
system. In this implementation a wireless surveillance pinhole
camera has been used to grab the video frames from the non
ideal environment. Once the object has been detected it is
tracked by employing an efficient Template Matching
algorithm. The templates used for the matching purposes are
generated dynamically. This ensures that any change in the
pose of the object does not hinder the tracking procedure. To
automate the tracking process the camera is mounted on a
disc coupled with stepper motor, which is synchronized with a
tracking algorithm. As and when the object being tracked
moves out of the viewing range of the camera, the setup is
automatically adjusted to move the camera so as to keep the
object of about 360 degree field of view. The system is capable
of handling entry and exit of an object. The performance of
the proposed Object tracking system has been demonstrated
in real time environment both for indoor and outdoor by
including a variety of disturbances and a means to detect a
loss of track.
Moving object detection using background subtraction algorithm using simulinkeSAT 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.
Optimization of Macro Block Size for Adaptive Rood Pattern Search Block Match...IJERA Editor
In area of video compression, Motion Estimation is one of the most important modules and play an important role
to design and implementation of any the video encoder. It consumes more than 85% of video encoding time due to
searching of a candidate block in the search window of the reference frame. Various block matching methods have
been developed to minimize the search time. In this context, Adaptive Rood Pattern Search is one of the less
expensive block matching methods, which is widely acceptable for better Motion Estimation in video data
processing. In this paper we have proposed to optimize the macro block size used in adaptive rood pattern search
method for improvement in motion estimation.
This summarizes an academic paper that proposes an unsupervised algorithm to detect regions of interest (ROIs) in images using fast feature detectors. It detects keypoints using Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to maximize interest points. It categorizes keypoints as foreground or background using k-nearest neighbors classification on texture descriptors. ROIs are identified as groups of foreground keypoints. Preliminary experiments showed this approach can efficiently detect ROIs without computationally expensive comparisons between images.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
This document summarizes a research paper that proposes a novel background removal algorithm using fuzzy c-means clustering. It begins by introducing background subtraction and some of the challenges. It then describes the proposed algorithm which uses edge detection to locate regions of interest before applying fuzzy c-means clustering to segment the foreground object. The algorithm achieves significant computation time reduction compared to other methods. Experimental results show the proposed method has higher true positive rates and accuracy compared to other algorithms, though precision and similarity are slightly lower.
A study and comparison of different image segmentation algorithmsManje Gowda
This document discusses and compares different image segmentation algorithms. It begins with an introduction to the topic and an agenda that outlines image segmentation techniques, results and discussion, conclusions, and references. Section 2 describes various image segmentation techniques like thresholding, region-based (region growing and data clustering), and edge-based segmentation. Section 3 shows results of applying algorithms like Otsu's method, K-means clustering, quad tree, delta E, and FTH to sample images and compares their performance on simple versus complex images. The conclusion is that delta E performs best for simple images with one object, while for complex images with multiple objects, performance degrades and further work is needed.
The document evaluates and compares the performance of three background subtraction algorithms: frame difference, statistical approach for real-time robust background subtraction and shadow detection, and adaptive background mixture models for real-time tracking. The frame difference method is the simplest but depends on threshold selection, while the statistical approach provides average accuracy and speed and the adaptive mixture models method provides the best accuracy but highest computational cost. Experimental results on video data demonstrate the tradeoffs between accuracy, computational requirements, and ability to handle challenges like lighting changes for each algorithm.
Wireless Vision based Real time Object Tracking System Using Template MatchingIDES Editor
In the present work the concepts of template
matching through Normalized Cross Correlation (NCC) has
been used to implement a robust real time object tracking
system. In this implementation a wireless surveillance pinhole
camera has been used to grab the video frames from the non
ideal environment. Once the object has been detected it is
tracked by employing an efficient Template Matching
algorithm. The templates used for the matching purposes are
generated dynamically. This ensures that any change in the
pose of the object does not hinder the tracking procedure. To
automate the tracking process the camera is mounted on a
disc coupled with stepper motor, which is synchronized with a
tracking algorithm. As and when the object being tracked
moves out of the viewing range of the camera, the setup is
automatically adjusted to move the camera so as to keep the
object of about 360 degree field of view. The system is capable
of handling entry and exit of an object. The performance of
the proposed Object tracking system has been demonstrated
in real time environment both for indoor and outdoor by
including a variety of disturbances and a means to detect a
loss of track.
Moving object detection using background subtraction algorithm using simulinkeSAT 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.
Optimization of Macro Block Size for Adaptive Rood Pattern Search Block Match...IJERA Editor
In area of video compression, Motion Estimation is one of the most important modules and play an important role
to design and implementation of any the video encoder. It consumes more than 85% of video encoding time due to
searching of a candidate block in the search window of the reference frame. Various block matching methods have
been developed to minimize the search time. In this context, Adaptive Rood Pattern Search is one of the less
expensive block matching methods, which is widely acceptable for better Motion Estimation in video data
processing. In this paper we have proposed to optimize the macro block size used in adaptive rood pattern search
method for improvement in motion estimation.
This summarizes an academic paper that proposes an unsupervised algorithm to detect regions of interest (ROIs) in images using fast feature detectors. It detects keypoints using Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to maximize interest points. It categorizes keypoints as foreground or background using k-nearest neighbors classification on texture descriptors. ROIs are identified as groups of foreground keypoints. Preliminary experiments showed this approach can efficiently detect ROIs without computationally expensive comparisons between images.
Development of Human Tracking System For Video Surveillancecscpconf
Visual surveillance in dynamic scenes, especially for human and some objects is one of the
most active research areas. An attempt has been made to this issue in this work. It has wide
spectrum of promising application including human identification to detect the suspicious
behavior, crowd flux statistics, and congestion analysis using multiple cameras.
In this paper deals with the problem of detecting and tracking multiple moving people in a static
background. Detection of foreground object is done by background subtraction. Detected
objects are identified and analyzed through different blobs. Then tracking is performed by
matching corresponding features of blob. An algorithm has been developed in this perspective
using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.
This document reviews different techniques for thinning images, including the Zhang and Suen algorithm and neural networks. It provides an overview of existing thinning approaches, such as iterative algorithms, and proposes a new approach using neural networks. The proposed approach aims to perform thinning invariant to rotations while being less sensitive to noise than existing methods. It evaluates techniques based on execution time, thinning rate, and other performance measures. The document concludes that neural networks may provide better results than existing techniques in terms of metrics like PSNR and MSE, while also reducing execution time for skeletonization.
An Efficient Block Matching Algorithm Using Logical ImageIJERA Editor
Motion estimation, which has been widely used in various image sequence coding schemes, plays a key role in the transmission and storage of video signals at reduced bit rates. There are two classes of motion estimation methods, Block matching algorithms (BMA) and Pel-recursive algorithms (PRA). Due to its implementation simplicity, block matching algorithms have been widely adopted by various video coding standards such as CCITT H.261, ITU-T H.263, and MPEG. In BMA, the current image frame is partitioned into fixed-size rectangular blocks. The motion vector for each block is estimated by finding the best matching block of pixels within the search window in the previous frame according to matching criteria. The goal of this work is to find a fast method for motion estimation and motion segmentation using proposed model. Recent day Communication between ends is facilitated by the development in the area of wired and wireless networks. And it is a challenge to transmit large data file over limited bandwidth channel. Block matching algorithms are very useful in achieving the efficient and acceptable compression. Block matching algorithm defines the total computation cost and effective bit budget. To efficiently obtain motion estimation different approaches can be followed but above constraints should be kept in mind. This paper presents a novel method using three step and diamond algorithms with modified search pattern based on logical image for the block based motion estimation. It has been found that, the improved PSNR value obtained from proposed algorithm shows a better computation time (faster) as compared to original Three step Search (3SS/TSS ) method .The experimental results based on the number of video sequences were presented to demonstrate the advantages of proposed motion estimation technique.
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
Haze removal for a single remote sensing image based on deformed haze imaging...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Robust foreground modelling to segment and detect multiple moving objects in ...IJECEIAES
This document summarizes a research paper that proposes a robust foreground modeling method to segment and detect multiple moving objects in videos. The proposed method uses a running average technique to model the background and subtract it from video frames to detect foreground objects. Morphological operations like dilation and erosion are applied to reduce noise and merge connected regions. Convex hull processing is also used to define object boundaries more clearly. The method was tested on standard video datasets and achieved better performance than other techniques in segmenting objects under various challenging conditions like illumination changes and occlusion. Experimental results demonstrated high precision, recall and specificity based on comparisons with ground truth data.
This document discusses data hiding techniques for images. It begins by introducing steganography and some common image steganography methods like LSB substitution, blocking, and palette modification. It then reviews related work on minimizing distortion in steganography, modifying matrix encoding for minimal distortion, and designing adaptive steganographic schemes. The document proposes using a universal distortion measure to evaluate embedding changes independently of the domain. It presents a system for reversible data hiding in encrypted images that partitions the image, encrypts it, hides data in the encrypted image, and allows extraction from the decrypted or encrypted image. Least significant bit substitution is discussed as an approach for hiding data in the encrypted image.
This document presents a method for image upscaling using a fuzzy ARTMAP neural network. It begins with an introduction to image upscaling and interpolation techniques. It then provides background on ARTMAP neural networks and fuzzy logic. The proposed method uses a linear interpolation algorithm trained with an ARTMAP network. Results show the method performs better than nearest neighbor interpolation in terms of peak signal-to-noise ratio, mean squared error, and structural similarity, though not as high as bicubic interpolation. Overall, the fuzzy ARTMAP network provides an effective way to perform image upscaling with fewer artifacts than traditional methods.
Multiple Person Tracking with Shadow Removal Using Adaptive Gaussian Mixture ...IJSRD
Person detection in a video surveillance system is major concern in real world. Several application likes abnormal event detection, congestion analysis, human gait characterization, fall detection, person identification, gender classification and for elderly people. In this algorithm, we use GMM method in background subtraction for multi person detection because of Gaussian Mixture Model (GMM) model is one such popular method this give a real time object detection. There is still not robustly but Multi person tracking with shadow removal fill this gap, in this work, HOG-LBP hybrid approach with GMM algorithm is presented for Multi person tracking with Shadow removal.
We presents a technique for moving objects extraction. There are several different approaches for moving object extraction, clustering is one of object extraction method with a stronger teorical foundation used in many applications. And need high performance in many extraction process of moving object. We compare K-Means and Self-Organizing Map method for extraction moving objects, for performance measurement of moving object extraction by applying MSE and PSNR. According to experimental result that the MSE value of K-Means is smaller than Self-Organizing Map. It is also that PSNR of K-Means is higher than Self-Organizing Map algorithm. The result proves that K-Means is a promising method to cluster pixels in moving objects extraction.
This document provides a survey of single scalar point multiplication algorithms for elliptic curves over prime fields. It discusses the background of elliptic curve cryptography and point multiplication. Point multiplication is the dominant operation in ECC and can be computed using on-the-fly techniques or precomputation if the point is fixed. The efficiency of point multiplication depends on the recoding method used to represent the scalar and the composite elliptic curve operations employed. Various recoding methods and point multiplication algorithms are analyzed, including binary, signed binary using NAF representation, and window methods.
Schematic model for analyzing mobility and detection of multipleIAEME Publication
The document discusses a schematic model for analyzing mobility and detecting multiple objects in traffic scenes. It aims to not only detect and count moving objects, but also understand crowd behavior and reduce issues with objects occluding each other. Previous work on object detection is reviewed, noting that most approaches do not integrate detecting multiple objects simultaneously or address problems of object occlusion. The proposed model uses background subtraction and unscented Kalman filtering to increase detection accuracy and reduce false positives when analyzing image sequences of traffic scenes to detect multiple moving objects. It was tested in MATLAB and results showed highly accurate detection rates.
IMPROVED PARALLEL THINNING ALGORITHM TO OBTAIN UNIT-WIDTH SKELETONijma
To extract the creditable features in a fingerprint image, many people use a thinning algorithm that plays a
very important role in preprocessing. In this paper, we propose a robust parallel thinning algorithm that
can preserve the connectivity of the binarized fingerprint image, while making the thinnest skeleton of only
1-pixel wide, which gets extremely close to the medial axis. The proposed thinning method repeats three
sub-iterations. The first sub-iteration takes off only the outermost boundary pixel using the inner points. To
extract the one-sided skeletons, the second sub-iteration seeks the skeletons with a 2-pixel width. The third
sub-iteration prunes the needless pixels with a 2-pixel width existing in the obtained skeletons. The
proposed thinning algorithm shows robustness against rotation and noise and makes the balanced medial
axis. To evaluate the performance of the proposed thinning algorithm, we compare it with and analyze
previous algorithms.
Implementation of Object Tracking for Real Time VideoIDES Editor
Real-time tracking of object boundaries is an
important task in many vision applications. Here we propose
an approach to implement the level set method. This approach
does not need to solve any partial differential equations (PDFs),
thus reducing the computation dramatically compared with
optimized narrow band techniques proposed before. With our
approach, real-time level-set based video tracking can be
achieved.
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
3 d mrf based video tracking in the compressed domaineSAT Journals
Abstract Object tracking is an interesting and needed procedure for many real time applications. But it is a challenging one, because of the presence of challenging sequences with abrupt motion, drastic illumination change, large pose variation, occlusion, cluttered background and also the camera shake. This paper presents a novel method of object tracking by using the algorithms spatio-temporal Markov random field (STMRF) and online discriminative feature selection (ODFS), which overcome the above mentioned problems and provide a better tracking process. This method is also capable of tracking multiple objects in video sequence even in the presence of an object interactions and occlusions that achieves better results with real time performance. Keywords: Video object tracking, spatio-temporal Markov random field (ST-MRF), online discriminative feature selection (ODFS).
A fast single image haze removal algorithm using color attenuation priorLogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
AN ENHANCED EDGE ADAPTIVE STEGANOGRAPHY APPROACH USING THRESHOLD VALUE FOR RE...ijcsa
The document summarizes an enhanced edge adaptive steganography approach using a threshold value for region selection. It aims to improve the quality and modification rate of a stego image compared to Sobel and Canny edge detection techniques. The proposed approach uses a threshold value to select high frequency pixels from the cover image for data embedding using LSBMR. Experimental results on 100 images show about a 0.2-0.6% improvement in image quality measured by PSNR and a 4-10% improvement in modification rate measured by MSE compared to Sobel and Canny edge detection.
Performance analysis on color image mosaicing techniques on FPGAIJECEIAES
Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
PC-based Vision System for Operating Parameter Identification on a CNC MachineIDES Editor
Identification of suitable or optimum operating
parameters on a CNC machine is a non-trivial task. Especially
when the material of the component changes, operating
parameters need to be suitably varied. In this paper, a PCbased
vision system is presented for the automatic identification
of component material and appropriate selection of operating
parameters. The objective of this work is to develop a support
system to aid the operator in quick identification of machining
parameters
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Moving Object Detection using Foreground Detection for Video Surveil...IRJET Journal
This document summarizes a research paper that proposes a new method for detecting moving objects in videos using foreground detection and background subtraction. The key steps of the proposed method include initializing a background model using the median of initial frames, dynamically updating the background model to adapt to lighting changes, subtracting the background model from current frames and applying a threshold to detect moving objects, and using morphological operations and projection analysis to extract human bodies and remove noise. The experimental results showed that the proposed method can accurately and reliably detect moving human bodies in real-time video surveillance.
This document reviews different techniques for thinning images, including the Zhang and Suen algorithm and neural networks. It provides an overview of existing thinning approaches, such as iterative algorithms, and proposes a new approach using neural networks. The proposed approach aims to perform thinning invariant to rotations while being less sensitive to noise than existing methods. It evaluates techniques based on execution time, thinning rate, and other performance measures. The document concludes that neural networks may provide better results than existing techniques in terms of metrics like PSNR and MSE, while also reducing execution time for skeletonization.
An Efficient Block Matching Algorithm Using Logical ImageIJERA Editor
Motion estimation, which has been widely used in various image sequence coding schemes, plays a key role in the transmission and storage of video signals at reduced bit rates. There are two classes of motion estimation methods, Block matching algorithms (BMA) and Pel-recursive algorithms (PRA). Due to its implementation simplicity, block matching algorithms have been widely adopted by various video coding standards such as CCITT H.261, ITU-T H.263, and MPEG. In BMA, the current image frame is partitioned into fixed-size rectangular blocks. The motion vector for each block is estimated by finding the best matching block of pixels within the search window in the previous frame according to matching criteria. The goal of this work is to find a fast method for motion estimation and motion segmentation using proposed model. Recent day Communication between ends is facilitated by the development in the area of wired and wireless networks. And it is a challenge to transmit large data file over limited bandwidth channel. Block matching algorithms are very useful in achieving the efficient and acceptable compression. Block matching algorithm defines the total computation cost and effective bit budget. To efficiently obtain motion estimation different approaches can be followed but above constraints should be kept in mind. This paper presents a novel method using three step and diamond algorithms with modified search pattern based on logical image for the block based motion estimation. It has been found that, the improved PSNR value obtained from proposed algorithm shows a better computation time (faster) as compared to original Three step Search (3SS/TSS ) method .The experimental results based on the number of video sequences were presented to demonstrate the advantages of proposed motion estimation technique.
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
Haze removal for a single remote sensing image based on deformed haze imaging...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Robust foreground modelling to segment and detect multiple moving objects in ...IJECEIAES
This document summarizes a research paper that proposes a robust foreground modeling method to segment and detect multiple moving objects in videos. The proposed method uses a running average technique to model the background and subtract it from video frames to detect foreground objects. Morphological operations like dilation and erosion are applied to reduce noise and merge connected regions. Convex hull processing is also used to define object boundaries more clearly. The method was tested on standard video datasets and achieved better performance than other techniques in segmenting objects under various challenging conditions like illumination changes and occlusion. Experimental results demonstrated high precision, recall and specificity based on comparisons with ground truth data.
This document discusses data hiding techniques for images. It begins by introducing steganography and some common image steganography methods like LSB substitution, blocking, and palette modification. It then reviews related work on minimizing distortion in steganography, modifying matrix encoding for minimal distortion, and designing adaptive steganographic schemes. The document proposes using a universal distortion measure to evaluate embedding changes independently of the domain. It presents a system for reversible data hiding in encrypted images that partitions the image, encrypts it, hides data in the encrypted image, and allows extraction from the decrypted or encrypted image. Least significant bit substitution is discussed as an approach for hiding data in the encrypted image.
This document presents a method for image upscaling using a fuzzy ARTMAP neural network. It begins with an introduction to image upscaling and interpolation techniques. It then provides background on ARTMAP neural networks and fuzzy logic. The proposed method uses a linear interpolation algorithm trained with an ARTMAP network. Results show the method performs better than nearest neighbor interpolation in terms of peak signal-to-noise ratio, mean squared error, and structural similarity, though not as high as bicubic interpolation. Overall, the fuzzy ARTMAP network provides an effective way to perform image upscaling with fewer artifacts than traditional methods.
Multiple Person Tracking with Shadow Removal Using Adaptive Gaussian Mixture ...IJSRD
Person detection in a video surveillance system is major concern in real world. Several application likes abnormal event detection, congestion analysis, human gait characterization, fall detection, person identification, gender classification and for elderly people. In this algorithm, we use GMM method in background subtraction for multi person detection because of Gaussian Mixture Model (GMM) model is one such popular method this give a real time object detection. There is still not robustly but Multi person tracking with shadow removal fill this gap, in this work, HOG-LBP hybrid approach with GMM algorithm is presented for Multi person tracking with Shadow removal.
We presents a technique for moving objects extraction. There are several different approaches for moving object extraction, clustering is one of object extraction method with a stronger teorical foundation used in many applications. And need high performance in many extraction process of moving object. We compare K-Means and Self-Organizing Map method for extraction moving objects, for performance measurement of moving object extraction by applying MSE and PSNR. According to experimental result that the MSE value of K-Means is smaller than Self-Organizing Map. It is also that PSNR of K-Means is higher than Self-Organizing Map algorithm. The result proves that K-Means is a promising method to cluster pixels in moving objects extraction.
This document provides a survey of single scalar point multiplication algorithms for elliptic curves over prime fields. It discusses the background of elliptic curve cryptography and point multiplication. Point multiplication is the dominant operation in ECC and can be computed using on-the-fly techniques or precomputation if the point is fixed. The efficiency of point multiplication depends on the recoding method used to represent the scalar and the composite elliptic curve operations employed. Various recoding methods and point multiplication algorithms are analyzed, including binary, signed binary using NAF representation, and window methods.
Schematic model for analyzing mobility and detection of multipleIAEME Publication
The document discusses a schematic model for analyzing mobility and detecting multiple objects in traffic scenes. It aims to not only detect and count moving objects, but also understand crowd behavior and reduce issues with objects occluding each other. Previous work on object detection is reviewed, noting that most approaches do not integrate detecting multiple objects simultaneously or address problems of object occlusion. The proposed model uses background subtraction and unscented Kalman filtering to increase detection accuracy and reduce false positives when analyzing image sequences of traffic scenes to detect multiple moving objects. It was tested in MATLAB and results showed highly accurate detection rates.
IMPROVED PARALLEL THINNING ALGORITHM TO OBTAIN UNIT-WIDTH SKELETONijma
To extract the creditable features in a fingerprint image, many people use a thinning algorithm that plays a
very important role in preprocessing. In this paper, we propose a robust parallel thinning algorithm that
can preserve the connectivity of the binarized fingerprint image, while making the thinnest skeleton of only
1-pixel wide, which gets extremely close to the medial axis. The proposed thinning method repeats three
sub-iterations. The first sub-iteration takes off only the outermost boundary pixel using the inner points. To
extract the one-sided skeletons, the second sub-iteration seeks the skeletons with a 2-pixel width. The third
sub-iteration prunes the needless pixels with a 2-pixel width existing in the obtained skeletons. The
proposed thinning algorithm shows robustness against rotation and noise and makes the balanced medial
axis. To evaluate the performance of the proposed thinning algorithm, we compare it with and analyze
previous algorithms.
Implementation of Object Tracking for Real Time VideoIDES Editor
Real-time tracking of object boundaries is an
important task in many vision applications. Here we propose
an approach to implement the level set method. This approach
does not need to solve any partial differential equations (PDFs),
thus reducing the computation dramatically compared with
optimized narrow band techniques proposed before. With our
approach, real-time level-set based video tracking can be
achieved.
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
3 d mrf based video tracking in the compressed domaineSAT Journals
Abstract Object tracking is an interesting and needed procedure for many real time applications. But it is a challenging one, because of the presence of challenging sequences with abrupt motion, drastic illumination change, large pose variation, occlusion, cluttered background and also the camera shake. This paper presents a novel method of object tracking by using the algorithms spatio-temporal Markov random field (STMRF) and online discriminative feature selection (ODFS), which overcome the above mentioned problems and provide a better tracking process. This method is also capable of tracking multiple objects in video sequence even in the presence of an object interactions and occlusions that achieves better results with real time performance. Keywords: Video object tracking, spatio-temporal Markov random field (ST-MRF), online discriminative feature selection (ODFS).
A fast single image haze removal algorithm using color attenuation priorLogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
AN ENHANCED EDGE ADAPTIVE STEGANOGRAPHY APPROACH USING THRESHOLD VALUE FOR RE...ijcsa
The document summarizes an enhanced edge adaptive steganography approach using a threshold value for region selection. It aims to improve the quality and modification rate of a stego image compared to Sobel and Canny edge detection techniques. The proposed approach uses a threshold value to select high frequency pixels from the cover image for data embedding using LSBMR. Experimental results on 100 images show about a 0.2-0.6% improvement in image quality measured by PSNR and a 4-10% improvement in modification rate measured by MSE compared to Sobel and Canny edge detection.
Performance analysis on color image mosaicing techniques on FPGAIJECEIAES
Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.
A Literature Survey: Neural Networks for object detectionvivatechijri
Humans have a great capability to distinguish objects by their vision. But, for machines object
detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural
Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational
models of the brain which helps in object detection and recognition. This paper describes and demonstrates the
different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies.
From the study of various research papers, the accuracies of different Neural Networks are discussed and
compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object
detection.
PC-based Vision System for Operating Parameter Identification on a CNC MachineIDES Editor
Identification of suitable or optimum operating
parameters on a CNC machine is a non-trivial task. Especially
when the material of the component changes, operating
parameters need to be suitably varied. In this paper, a PCbased
vision system is presented for the automatic identification
of component material and appropriate selection of operating
parameters. The objective of this work is to develop a support
system to aid the operator in quick identification of machining
parameters
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Moving Object Detection using Foreground Detection for Video Surveil...IRJET Journal
This document summarizes a research paper that proposes a new method for detecting moving objects in videos using foreground detection and background subtraction. The key steps of the proposed method include initializing a background model using the median of initial frames, dynamically updating the background model to adapt to lighting changes, subtracting the background model from current frames and applying a threshold to detect moving objects, and using morphological operations and projection analysis to extract human bodies and remove noise. The experimental results showed that the proposed method can accurately and reliably detect moving human bodies in real-time video surveillance.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper on background subtraction techniques for motion detection in video. It describes a proposed technique that stores and compares past pixel values to the current value to determine if a pixel belongs to the background or foreground. It also discusses using a k-means algorithm and Gaussian mixture model to build a probabilistic background model and classify pixels. The paper evaluates different shadow detection approaches and finds RGB color spaces perform best for segmentation and shadow removal.
This document summarizes a research paper on background subtraction techniques for motion detection in video. It describes a proposed technique that stores and compares past pixel values to the current value to determine if a pixel belongs to the background or foreground. It also discusses using a k-means algorithm and Gaussian mixture model to build a probabilistic background model and classify pixels. The paper evaluates different shadow detection approaches and finds RGB color spaces perform best for segmentation and shadow removal.
A Moving Target Detection Algorithm Based on Dynamic BackgroundChittipolu Praveen
The document analyzes and compares two common algorithms for moving target detection: background subtraction and frame difference. Background subtraction detects targets by comparing each frame to a background model, while frame difference compares adjacent frames. Both have advantages but also limitations, such as background subtraction being sensitive to dynamic background changes. The document then proposes a new algorithm based on background subtraction. It generates the background for the next frame by combining the current frame and background, so stationary objects become part of the background over time rather than being detected as foreground. Experimental results show this dynamic background algorithm can detect targets more effectively and precisely.
Currently, in both market and the academic communities have required applications based on image and video processing with several real-time constraints. On the other hand, detection of moving objects is a very important task in mobile robotics and surveillance applications. In order to achieve this, we are using a alternative means for real time motion detection systems. This paper proposes hardware architecture for motion detection based on the background subtraction algorithm, which is implemented on FPGAs (Field Programmable Gate Arrays). For achieving this, the following steps are executed: (a) a background image (in gray-level format) is stored in an external SRAM memory, (b) a low-pass filter is applied to both the stored and current images, (c) a subtraction operation between both images is obtained, and (d) a morphological filter is applied over the resulting image. Afterward, the gravity center of the object is calculated and sent to a PC (via RS-232 interface).
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document evaluates the performance of various foreground extraction algorithms for object detection in visual surveillance. It analyzes three background modeling techniques (change detection mask, median, histogram) and two background subtraction algorithms (frame difference, approximate median). Experimental results on test videos show that background modeling using the median value technique and background subtraction using frame differencing provides the most robust and efficient combination. Processing times are reported for different combinations of algorithms. The study concludes that the median-based approach has good computational efficiency and robustness for background modeling.
This document summarizes a research paper on tracking moving objects and determining their distance and velocity using background subtraction algorithms. It first describes background subtraction as a process to extract foreground objects from video by comparing each frame to a background model. It then discusses several algorithms used in the research, including median filtering for noise removal, morphological operations to smooth object regions, and connected component analysis to detect large foreground regions representing objects. The document evaluates these techniques on video to track a single object, determine the distance and velocity of that object between frames, and identify multiple moving objects.
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.
An interactive image segmentation using multiple user inputªseSAT Journals
Abstract In this paper, we consider the Interactive image segmentation with multiple user inputs. The proposed system is the use of multiple intuitive user inputs to better reflect the user’s intention. The use of multiple types of intuitive inputs provides the user’s intention under different scenario. The proposed method is developed as a combined segmentation and editing tool. It incorporates a simple user interface and a fast and reliable segmentation based on 1D segment matching. The user is required to click just a few "control points" on the desired object border, and let the algorithm complete the rest. The user can then edit the result by adding, removing and moving control points, where each interaction follows by an automatic, real-time segmentation by the algorithm. Interactive image segmentation involves a proposed algorithm, Constrained Random walks algorithm. The Constrained Random Walks algorithm facilitates the use of three types of user inputs. 1. Foreground and Background seed input 2. Soft Constraint input 3. Hard Constraint input. The effectiveness of the proposed method is validated by experimental results. The proposed algorithm is algorithmically simple, efficient and less time consuming. Keywords: Interactive image segmentation, Interactive image segmentation, digital image editing, multiple user inputs, random walks algorithm.
IMAGE RECOGNITION USING MATLAB SIMULINK BLOCKSETIJCSEA Journal
The world over, image recognition are essential players in promoting quality object recognition especially in emergency and search-rescue operation. In this paper precise image recognition system using Matlab Simulink Blockset to detect selected object from crowd is presented. The process involves extracting object
features and then recognizes it considering illumination, direction and pose. A Simulink model has been developed to eliminate the tiny elements from the image, then creating segments for precise object recognition. Furthermore, the simulation explores image recognition from the coloured and gray-scale images through image processing techniques in Simulink environment. The tool employed for computation
and simulation is the Matlab image processing blockset. The process comprises morphological operation method which is effective for captured images and video. The results of extensive simulations indicate that this method is suitable for application identifying a person from a crow. The model can be used in emergency and search-rescue operation as well as in medicine, information security, access control, law enforcement, surveillance system, microscopy etc.
Detection of a user-defined object in an image using feature extraction- Trai...IRJET Journal
The document proposes a method for detecting user-defined objects in images using feature extraction and training. The method combines contour detection, edge detection, k-means clustering, color identification, and image segmentation. It uses an original "source" object image to train the system to recognize and identify the target object in other images based on a feature set. The key steps include pre-processing images, extracting features like contours and edges, using k-means clustering to identify colors, and analyzing color and shape features to detect matching objects. The results demonstrate the ability to accurately detect target objects against complex backgrounds.
SENSITIVITY OF A VIDEO SURVEILLANCE SYSTEM BASED ON MOTION DETECTIONsipij
The implementation of a stand-alone system developed in JAVA language for motion detection has been discussed. The open-source OpenCV library has been adopted for video surveillance image processing thus implementing Background Subtraction algorithm also known as foreground detection algorithm. Generally the region of interest of a body or object to detect is related to a precise objects (people, cars, etc.) emphasized on a background. This technique is widely used for tracking a moving objects. In particular, the BackgroundSubtractorMOG2 algorithm of OpenCV has been applied. This algorithm is based on Gaussian distributions and offers better adaptability to different scenes due to changes in lighting and the detection of shadows as well. The implemented webcam system relies on saving frames and creating GIF and JPGs files with previously saved frames. In particular the Background Subtraction function, find Contours, has been adopted to detect the contours. The numerical quantity of these contours has been compared with the tracking points of sensitivity obtained by setting an user-modifiable slider able to save the frames as GIFs composed by different merged JPEGs. After a full design of the image processing prototype different motion test have been performed. The results showed the importance to consider few sensitivity points in order to obtain more frequent image storages also concerning minor movements.Sensitivity points can be modified through a slider function and are inversely proportional to the number of saved images. For small object in motion will be detected a low percentage of sensitivity points.Experimental results proves that the setting condition are mainly function of the typology of moving object rather than the light conditions. The proposed prototype system is suitable for video surveillance smart
camera in industrial systems.
Foreground algorithms for detection and extraction of an object in multimedia...IJECEIAES
Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called “background image” and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It’s quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required.
Development and Hardware Implementation of an Efficient Algorithm for Cloud D...sipij
This document discusses the development and hardware implementation of an efficient algorithm for cloud detection from satellite images. The algorithm uses an adaptive thresholding approach to segment clouds from background pixels in satellite imagery. It then determines the position of the segmented clouds to calculate cloud coverage percentages. The algorithm was tested on satellite images from Spot4 and Landsat archives. It was implemented on a TMS320C6713 DSK processor using Code Composer Studio and achieved accurate cloud detection and coverage calculation on images with resolutions up to 3600x3000 pixels.
The document compares frame difference and Kalman filter techniques for detecting moving vehicles in video surveillance. Frame difference is a simple but low accuracy method that uses thresholding on differences between frames. Kalman filtering provides better accuracy by modeling each pixel as a Kalman filter and updating estimates based on observations. The paper applies both methods to a vehicle video and finds that Kalman filtering produces cleaner detection with fewer false positives compared to frame difference.
PARALLEL GENERATION OF IMAGE LAYERS CONSTRUCTED BY EDGE DETECTION USING MESSA...ijcsit
Edge detection is one of the most fundamental algorithms in digital image processing. Many algorithms have been implemented to construct image layers extracted from the original image based on selecting threshold parameters. Changing theses parameters to get a high quality layer is time consuming. In this paper, we propose two parallel technique, NASHT1 and NASHT2, to generate multiple layers of an input
image automatically to enable the image tester to select the highest quality detected edges. In addition, the
effect of intensive I/O operations and the number of parallel running processes on the performance of the proposed techniques have also been studied.
Motion Object Detection Using BGS TechniqueMangaiK4
Abstract--- The detection of moving object is an important in many applications such as a vehicle identification in a traffic monitoring system,human detection in a crime branch.In this paper we identify a vehicle in a video sequence.This paper briefly explain the detection of moving vehicle in a video.We introduce a new algorithm BGS for idntifying vehicle in a video sequence.First, we differentiate the foreground from background in frames by learning the background.Then, the image is divided into many small nonoverlapped frames. The candidates of the vehicle part can be found from the frames if there is some change in gray level between the current image and the background.The extracted background subtraction method is used in subsequent analysis to detect a vehicle and classify moving vehicle
Motion Object Detection Using BGS TechniqueMangaiK4
Abstract--- The detection of moving object is an important in many applications such as a vehicle identification in a traffic monitoring system,human detection in a crime branch.In this paper we identify a vehicle in a video sequence.This paper briefly explain the detection of moving vehicle in a video.We introduce a new algorithm BGS for idntifying vehicle in a video sequence.First, we differentiate the foreground from background in frames by learning the background.Then, the image is divided into many small nonoverlapped frames. The candidates of the vehicle part can be found from the frames if there is some change in gray level between the current image and the background.The extracted background subtraction method is used in subsequent analysis to detect a vehicle and classify moving vehicle.
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A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
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of NoC design presents numerous challenges related to
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are implemented in distributed RAM and virtual channels for
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FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
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Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
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Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
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
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- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
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- Create S3 bucket.
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This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
A ROBUST BACKGROUND REMOVAL ALGORTIHMS USING FUZZY C-MEANS CLUSTERING
1. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
DOI : 10.5121/ijnsa.2013.5207 93
A ROBUST BACKGROUND REMOVAL ALGORTIHMS
USING FUZZY C-MEANS CLUSTERING
S.Lakshmi1
and Dr.V.Sankaranarayanan2
1
Jeppiaar Engineering College, Chennai
lakshmi1503@gmail.com
2
Director, Crescent Engineering College, Chennai
sankarammu@yahoo.com
ABSTRACT
Background subtraction is typically one of the first steps carried out in motion detection using static video
cameras. This paper presents a novel method for background removal that processes only some pixels of
each image. Some regions of interest of the objects in the image or frame are located with the help of edge
detector. Once the region is detected only that area will be segmented instead of processing the whole
image. This method achieves a significant reduction in computation time that can be used for subsequent
image analysis. In this paper we detect the foreground object with the help of edge detector and combine
the Fuzzy c-means clustering algorithm to segment the object by means of subtracting the current frame
from the previous frame, the accurate background is identified.
KEYWORDS
Background removal, Surveillance, Image segmentation, Foreground detection, Background Subtraction,
Fuzzy-C means Clustering
1. INTRODUCTION
Background subtraction is a process of extracting foreground objects in a particular image. The
foreground object boundaries extraction reduces the amount of data to be processed and also
provide important information about the object. If a car is going on the road, the car forms the
foreground object and the road is considered as background.
Recognizing the moving objects from a video sequence is a critical task in many applications. A
common approach is to perform background subtraction, which identifies moving objects from
the portion of video frame. There are many challenging issues in designing a good background
subtraction algorithm such as robust against changes in illumination and the shadows cast by
moving objects.
2. RELATED WORK
Background subtraction is a standard method for the object localization in the video sequences
especially for the surveilling applications where cameras are fixed [1].The straight forward
technique of background subtraction is to just subtract previous frame with the current frame and
threshold the result on each pixel. Several background subtraction algorithms have been proposed
in the recent literature. All of these methods try to effectively estimate the background model
from the temporal sequence of the frames. One of the simplest algorithms is frame differencing
2. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
94
[2]. The current frame is subtracted from the previous frame. This method was extended such that
the reference frame is obtained by averaging a period of frames also known as median filtering.
[3][4][5]. But such a simple method fails when the object moves very slowly. It involves
comparing a detected image with an estimate of the image if it contained no objects of interest.
The issues in background maintenances are introduced in[6]. Horprasert et al [7] proposed
statistical color model to remove shadow with RGB. But in his method non-stationary
background cannot be used.
The areas of the image plane where there is a significant difference between the observed and
estimated images indicate the location of the objects of interest. The name background
subtraction comes from the simple technique of subtracting the detected image from the estimated
image and thresholding the result to generate the objects of interest. Dongxiang Zhou, et al. has
presented their work on a novel background subtraction algorithm that is capable of detecting
objects of interest while all pixels are in motion. This algorithm makes use of texture feature
information instead of intensity information [8].The general requirements for a background
removal algorithm are the accuracy in object contour detection (spatial accuracy) and temporal
stability of the detection(temporal coherency)[9]. Gaussian Mixture Model is used to estimate the
complicated background such as outdoor background with trees [10].
The paper is organized as follows. Section 3 lists the major issues in background subtraction.
Section 4 presents the outline of the paper. The color image is converted into gray scale image in
Section 5.Section 6 presents the idea of detecting the edges of the foreground object by using the
algorithm suggested by the authors in [11]. Section 7 explains how the fuzzy c means clustering
algorithm used for segmentation of the image using the detected edges of the object and also
explains how to improve the accuracy of the segmentation for background subtraction. Section 8
shows experimental results in terms of accuracy and efficiency, when compared with former
MOG [10] and CB [12] techniques. Section 9 concludes the paper.
3. MAJOR ISSUES IN BACKGROUND SUBTRACTION
The background subtraction is to differentiate moving foreground objects from the static or
dynamic background. The important issues related to background subtraction is listed below.
They are
• Camera motion due to environmental factors
• Different parts of the day
• Different whether condition
• As the sun sets, changes in the background over time
• Detection of building and different natural structures
• The objects may be occluded by other objects.
• Sometimes, background is not static due to uttering leaves and
• Due to noise in the image during image acquisition process
4. SKETCH OF THE PAPER
First, we have to take any video sequence for few minutes and then convert it into frames.
Convert color image into grey scale image without losing the fine details of the given image.
Then the boundaries of the foreground objects are extracted by using our edge detection
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algorithm. Thirdly, Fuzzy C-means clustering algorithm can be used to perform the accurate
segmentation of the foreground object. Finally, the current frame pixel value will be subtracted
from the previous frame to get the subtracted background. The outline of the paper is shown in
Fig-4.1.
Fig-4.1-Outline of the paper
5. CONVERSION OF COLOR IMAGE INTO GRAY SCALE IMAGE
Generally images are represented in RGB color space. In the proposed algorithm, the color
images need to be changed to gray ones. There are three eminent algorithms used to convert the
color image into gray scale. They are lightness method, average method and luminosity method.
Convert the color image into gray scale image by using any one of the methods. In this paper we
have used average method for converting the color image into gray scale. In this average method
the intensity of the each and every pixel is calculated using the formula
I =
3
BGR ++
Fuzzy C means clustering algorithm for image
segmentation
Detection of the boundaries of the
foreground object using edge detector
Subtracted background image
Subtract current fame from previous
frame
Video Sequence
Convert video into Frame
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For doing this conversion we have a function named rgb2gray (image) in matlab.We can also
directly use this matlab function to convert the color image into gray scale image.
6. EDGE DETECTION
Digital image processing is the use of computer algorithms to perform image processing on
digital images. Since the boundaries of the objects can have more information of the object than
the object itself. For this reason edge detection plays a major role in computer vision. Hence, it is
necessary to extract the fine details of the objects.
First, we have to apply the edge detection algorithm presented by the authors in [11], for
detecting the edges of an image or frame. The aim of the edge detection is to eliminate the
unwanted data to be processed and also to reduce the processing time by storing very important
information.
Fig.6.1.1 Fig.6.1.2 Fig.6.1.3
Fig.6.2.1 Fig.6.2.2 Fig.6.2.3
Fig.6.1.1 and 6.2.1 are original images;
Fig.6.1.2 and 6.2.2 are the gray scale images
Fig.6.1.3 and 6.2.3 are the detected edges of the given original image
7. FUZZY C MEANS CLUSTERING
The need of adapting efficient clustering algorithm increases in critical applications. Fuzzy C-
Mean clustering algorithm is a way to show how data can be classified and clustered in
organization or in any application [13]. It was developed by Dunn [14].In this paper, using Fuzzy
c-means clustering algorithm background and foreground objects are segmented from the image
or frames. This algorithm mainly helps to segment the pixels whether it belongs to background or
foreground. The number of clusters is created based on the number of objects in the frames.
Applying this fuzzy c means clustering algorithm centroid will be selected. First the centroid is
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chosen randomly based on the mean of the pixels. The correct centroid will be calculated after
finding the degree of pixel using several iterations.
In this paper fuzzy c-means clustering method is used for choosing the centroid based on the
pixels and the detected edges using the novel edge detection algorithm [11].The following
algorithm shows how the fuzzy c-mean clustering technique can be used to segment the
foreground object from the given image/frame.
Algorithm
----------------------------------------------------------------------------------------------------------
Input: the boundaries of the object after applying the edge detection algorithm
Output: the segmented image by applying fuzzy c-means clustering algorithm
Step 1: Consider all the pixels at the left vertical axis of the frame.
Step 2: Scan every pixel in horizontal direction until it intersects with the edge pixel
which has been detected during edge detection process.
Step 3: Store the value of the edge pixel and continue the process until other edge pixel is
found and name those pixels as a, b and so on which gives the boundaries of the
object.
Step 4: Continue the scanning process until reach the last pixel in the right vertical axis of
the frame.
Step 5: If there is no edge pixel is found then those pixels are considered as background
pixels.
Step 6: Calculate the mid point between a pixel in edge a and a pixel in edge b using the
formula
Midpoint (a,b) =
2
),(),( bbaa yxyx +
where (xa,ya) is the coordinate of a pixel in edge a and (xb,yb) is the
coordinate of a pixel in edge b.
Step 7: Identify the mid points for all edge pixels by applying the Midpoint formula.
Step 8: Connect all the mid points column wise.
Step 9: Locate the column which contains more number of midpoints and mark the first
and last midpoint in that column.
Step10: Calculate the midpoint for that column using the midpoint formula which acts as
the centroid for the object in the frame or image.
----------------------------------------------------------------------------------------------------------
The foreground objects in the frame or image are segmented by applying this algorithm. The
detected edge pixels during edge detection process in the first stage are really very useful to
segment the objects exactly.
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The background is subtracted by applying the above method to every frame of the video and the
difference will be calculated between the previous and current frame as follows.
Pixel coordinates (x, y) = Previous frame – Current frame
The subtracted background for the given input images Fig-6.1.1 and Fig-6.2.1 are shown in the
figures Fig-7.1 and Fig-7.2.
Fig.7.1 Fig.7.2
8. RESULTS AND DISCUSSIONS
We applied the proposed algorithm to various video streams. Fig-6.1.1 and Fig-6.2.1 shows the
original image and Fig-6.1.2 and Fig-6.2.2 shows the grayscale images of the given original
image. The detected edges of the original image after applying the edge detection algorithm are
shown in Fig-6.1.3 and Fig-6.2.3. Apparently, the detected edges of the image or frame can
remove the most of the noise and significantly enhance the detection precision.
Fig.7.1 and Fig.7.2 are the segmented images of the given input image after applying the fuzzy C-
Means clustering algorithm. Several metrics are available to test the accuracy and performance of
our method. We used the metric in terms of FP rate, TP rate, Precision and similarity.
FP rate =
tnfp
fp
+
TP rate =
tntp
tp
+
Precision =
fptp
tp
+
Similarity =
fnfptp
tp
++
Recall =
fntp
tp
+
where tp denotes the total number of true positives;
tn denotes the number of true negative
fp denotes the number of false positives
fn denotes the number of false negatives
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(tp + fn) indicates the total number of pixels presented in the foreground and
(fp + tn) indicates the total number of pixels presented in the background.
We have tested the results for every 40 frames.
Fig.8.1 Fig.8.2
Fig.8.3 Fig.8.4
The proposed work is evaluated by using another frame Fig.8.1.The gray scale image of the
Fig.8.1 is given in Fig.8.2. The Fig.8.3 shows the detected edges of the given original image and
the Fig.8.4 shows the final result of background subtraction. Fig 8.5 is the original image of
another example and Fig.8.6 is the resultant image of our background subtraction algorithm.
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Fig.8.5 Fig.8.6
The accuracy of the proposed method has been evaluated and the results are compared and
tabulated with the previous methods in Table 1. The results are shown diagrammatically in Fig-
8.7.
TABLE 1.PERFORMANCE EVALUATION
FP TP Precision Similarity
MOG 0.035 0.931 0.835 0.785
CB 0.014 0.908 0.903 0.827
Proposed
Method
0.071 0.964 0.704 0.687
0
0.2
0.4
0.6
0.8
1
1.2
FP TP Precision Similarity
MOG CB ProposedMethod
Fig. 8.7 Comparison of the proposed method with other methods
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Despite the true positives are more or less same with all the techniques, the precision and the
similarity ratios are less in the proposed work. Hence, the proposed technique works better than
other techniques.
9. CONCLUSION
A background subtraction method has been proposed by using edge detector and fuzzy c-means
clustering algorithm. As documented in the experimental results, the proposed method provides
high efficient for background subtraction which can be applied for vision based applications such
as human motion analysis or surveillance system. Through this method, the execution speed is
improved.
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