The document presents a comparative study of multiscale edge detection using different edge detectors for MRI thigh images. It proposes a multiscale edge detection method that uses averaging filters at three scales to smooth images. Edges are then extracted from the smoothed images using Prewitt, Sobel, and Laplacian detectors. The edges extracted from each scale are combined to form the final multiscale edge detection. Experimental results found Prewitt and Sobel extracted clear boundaries, while Laplacian extracted more fine details but with more discontinuities and noise. The proposed multiscale method extracts edges at different scales and combines them to provide more robust edge detection for MRI thigh images.
Gabor filter is a powerful way to enhance biometric images like fingerprint images in order to extract correct features from these images, Gabor filter used in extracting features directly asin iris images, and sometimes Gabor filter has been used for texture analysis. In fingerprint images The even symmetric Gabor filter is contextual filter or multi-resolution filter will be used to enhance fingerprint imageby filling small gaps (low-pass effect) in the direction of the ridge (black regions) and to increase the discrimination between ridge and valley (black and white regions) in the direction, orthogonal to the ridge, the proposed method in applying Gabor filter on fingerprint images depending on translated fingerprint image into binary image after applying some simple enhancing methods to partially overcome time consuming problem of the Gabor filter.
In this paper; we introduce a system of automatic recognition of Amazigh characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal, Gabor filters and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the Support vector machines (SVM) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
Efficient fingerprint image enhancement algorithm based on gabor filtereSAT 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
Face Recognition Using Neural Network Based Fourier Gabor Filters & Random Pr...CSCJournals
Face detection and recognition has many applications in a variety of fields such as authentication, security, video surveillance and human interaction systems. In this paper, we present a neural network system for face recognition. Feature vector based on Fourier Gabor filters is used as input of our classifier, which is a Back Propagation Neural Network (BPNN). The input vector of the network will have large dimension, to reduce its feature subspace we investigate the use of the Random Projection as method of dimensionality reduction. Theory and experiment indicates the robustness of our solution.
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
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- A Comprehensive Analysis of Edge Detectors in SD-OCT Images for Gl...IRJET Journal
This document analyzes different edge detection operators for segmenting retinal boundaries in optical coherence tomography (OCT) images to aid in glaucoma diagnosis. It compares the performance of Canny, Prewitt, Roberts, Sobel, Laplacian of Gaussian, Kirsch compass mask, and Robinson compass mask operators on OCT images from a healthy subject. The Kirsch compass mask was found to outperform other techniques based on evaluation metrics like mean squared error, peak signal-to-noise ratio, structural similarity index, figure of merit, and performance ratio, providing the most accurate and robust edge detection results.
07 18sep 7983 10108-1-ed an edge edit ariIAESIJEECS
Edge exposure or edge detection is an important and classical study of the medical field and computer vision. Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for CFCM is fruitful. According to the calculation, a result acquired by using CFCM clustering function divides the image into four clusters in common. The proposed method is evidently robust into the modification of fuzzy c-means and canny algorithm. The convergence of this algorithm is very speedy compare to the entire edge detection algorithms. The consequences of this proposed algorithm make enhanced edge detection and better result than any other traditional image edge detection techniques.
Gabor filter is a powerful way to enhance biometric images like fingerprint images in order to extract correct features from these images, Gabor filter used in extracting features directly asin iris images, and sometimes Gabor filter has been used for texture analysis. In fingerprint images The even symmetric Gabor filter is contextual filter or multi-resolution filter will be used to enhance fingerprint imageby filling small gaps (low-pass effect) in the direction of the ridge (black regions) and to increase the discrimination between ridge and valley (black and white regions) in the direction, orthogonal to the ridge, the proposed method in applying Gabor filter on fingerprint images depending on translated fingerprint image into binary image after applying some simple enhancing methods to partially overcome time consuming problem of the Gabor filter.
In this paper; we introduce a system of automatic recognition of Amazigh characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal, Gabor filters and of the Zernike moment. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the Support vector machines (SVM) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.
Efficient fingerprint image enhancement algorithm based on gabor filtereSAT 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
Face Recognition Using Neural Network Based Fourier Gabor Filters & Random Pr...CSCJournals
Face detection and recognition has many applications in a variety of fields such as authentication, security, video surveillance and human interaction systems. In this paper, we present a neural network system for face recognition. Feature vector based on Fourier Gabor filters is used as input of our classifier, which is a Back Propagation Neural Network (BPNN). The input vector of the network will have large dimension, to reduce its feature subspace we investigate the use of the Random Projection as method of dimensionality reduction. Theory and experiment indicates the robustness of our solution.
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
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- A Comprehensive Analysis of Edge Detectors in SD-OCT Images for Gl...IRJET Journal
This document analyzes different edge detection operators for segmenting retinal boundaries in optical coherence tomography (OCT) images to aid in glaucoma diagnosis. It compares the performance of Canny, Prewitt, Roberts, Sobel, Laplacian of Gaussian, Kirsch compass mask, and Robinson compass mask operators on OCT images from a healthy subject. The Kirsch compass mask was found to outperform other techniques based on evaluation metrics like mean squared error, peak signal-to-noise ratio, structural similarity index, figure of merit, and performance ratio, providing the most accurate and robust edge detection results.
07 18sep 7983 10108-1-ed an edge edit ariIAESIJEECS
Edge exposure or edge detection is an important and classical study of the medical field and computer vision. Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for CFCM is fruitful. According to the calculation, a result acquired by using CFCM clustering function divides the image into four clusters in common. The proposed method is evidently robust into the modification of fuzzy c-means and canny algorithm. The convergence of this algorithm is very speedy compare to the entire edge detection algorithms. The consequences of this proposed algorithm make enhanced edge detection and better result than any other traditional image edge detection techniques.
This document presents a new color image segmentation approach based on overlap wavelet transform (OWT). OWT extracts wavelet features to better separate different patterns in an image. The proposed method also uses morphological operators and 2D histogram clustering for effective segmentation. It is concluded that the proposed OWT method improves segmentation quality, is reliable, fast and computationally less complex than direct histogram clustering. When tested on various color spaces, the proposed segmentation scheme produced better results in RGB color space compared to others. The main advantages are its use of a single parameter and faster speed.
This document describes an image fusion method using pyramidal decomposition. It proposes extracting fine details from input images using guided filtering and fusing the base layers of images across multiple exposures or focal points using a multiresolution pyramid approach. A weight map is generated considering exposure, contrast, and saturation to guide the fusion of base layers. The fused base layer is then combined with extracted fine details to produce a detail-enhanced fused image. The goal is to preserve details in both very dark and extremely bright regions of the input images. It is argued that this method can effectively fuse images from different exposures or focal points without introducing artifacts.
This document presents a hybrid approach for color image segmentation that integrates color edge information and seeded region growing. It uses color edge detection in CIE L*a*b color space to select initial seed regions and guide region growth. Seeded region growing is performed based on color similarity between pixels. The edge map and region map are fused to produce homogeneous regions with closed boundaries. Small regions are then merged. The approach is tested on images from the Berkeley segmentation dataset and produces reasonably good segmentation results by combining color and edge information.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.
Extraction of texture features by using gabor filter in wheat crop disease de...eSAT Journals
This document discusses a method for detecting diseases in wheat crops using image processing and artificial neural networks. It involves taking digital images of wheat crop leaves and preprocessing the images by applying Gaussian and median filters to reduce noise. The images are then segmented using CIELAB color space. Texture features like area, perimeter, contrast, and energy are extracted from the images using Gabor filters. These features are then fed into an artificial neural network classifier to identify the type of disease present in the wheat crop. The method aims to help farmers more quickly and accurately detect diseases so they can better manage their crops and increase agricultural productivity.
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.
Abstract: Many applications such as robot navigation, defense, medical and remote sensing performvarious processing tasks, which can be performed more easily when all objects in different images of the same scene are combined into a single fused image. In this paper, we propose a fast and effective method for image fusion. The proposed method derives the intensity based variations that is large and small scale, from the source images. In this approach, guided filtering is employed for this extraction. Gaussian and Laplacian pyramidal approach is then used to fuse the different layers obtained. Experimental results demonstrate that the proposed method can obtain better performance for fusion of
all sets of images. The results clearly indicate the feasibility of the proposed approach.
Edge detection of herbal plants is a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply and has discontinuities. They are defined as the set of curved line segments termed edges. Effective edge detection for microscopic image of herbal plant is proposed through this paper which compares the edge detected images and then performs further segmentation. Comparison between Sobel operator, Prewitt, Canny and Robert cross operators is performed. Our method after efficient edge detection performs Gabor filter and K-means clustering to procure a better image. It is then subjected to further segmentation. Experimental methods in our proposed algorithm show that our method achieves a better edge detection as compared to other edge detector operators. Our proposed algorithm provides the maximum PSNR value of 43.684 amongst the other commercial edge detection operators.
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
This paper presents an improved edge detection algorithm for facial and remotely sensed images using
vector order statistics. The developed algorithm processes coloured images directly without been converted
to grey scale. A number of the existing algorithms converts the coloured images into grey scale before
detection of edges. But this process leads to inaccurate precision of recognized edges, thus producing false
and broken edges in the output edge map. Facial and remotely sensed images consist of curved edge lines
which have to be detected continuously to prevent broken edges. In order to deal with this, a collection of
pixel approach is introduced with a view to minimizing the false and broken edges that exists in the
generated output edge map of facial and remotely sensed images.
An efficient image segmentation approach through enhanced watershed algorithmAlexander Decker
This document proposes an efficient image segmentation approach combining an enhanced watershed algorithm and color histogram analysis. The watershed algorithm is applied to preprocessed images after merging the results with an enhanced edge detection. Over-segmentation issues are addressed through a post-processing step applying color histogram analysis to each segmented region, improving overall performance. The document provides background on image segmentation techniques, reviews related work applying watershed algorithms, and discusses challenges like over-segmentation that watershed approaches can face.
Image segmentation refers to partitioning a digital image into multiple regions or sets of pixels based on characteristics like color or texture. The goal is to simplify the image representation to make it easier to analyze. Some applications in medical imaging include locating tumors, measuring tissue volumes, and computer-guided surgery. Common segmentation techniques include thresholding, edge detection, region growing, and split-and-merge approaches.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
This document summarizes a research paper that proposes a content-based image retrieval system using cascaded color and texture features. Color features are first extracted from images using statistical measures like mean, standard deviation, energy, entropy, skewness and kurtosis. Similarity to a query image is then measured using distance metrics. The top 150 most similar images are then analyzed to extract Haralick texture features. Similarity is again measured to retrieve the most relevant images. The paper finds that Canberra distance provides better retrieval results than other distance metrics like City Block and Minkowski.
Iris Localization - a Biometric Approach Referring Daugman's AlgorithmEditor IJCATR
In general, there are many methods of biometric identification. But the Iris
recognition is most accurate and secure means of biometric identification. Iris has
many properties which makes it ideal biometric identification. There are many
methods used to identify the Iris location. To locate Iris many traditional methods are
used. In this we proposed such methods which can identify Iris Center(IC) as well as
localize its center. In this paper we are proposing a method which can use novel IC
localization method on the fact that the elliptical shape (ES) of Iris varies according to
the rotation of eye movement. In this paper various IC locations are generated and
stored in database. Finally the location of IC is detected by matching the ES of the Iris
of input eye image withes candidates in DB. In this paper we are comparing different
methods for Iris localization.
This document summarizes various methods used to remove metal artifacts from dental CT images. It discusses projection completion methods, filtered back projection, maximum likelihood transmission, iterative reconstruction, and linear interpolation methods. The majority of metal artifact reduction methods involve reconstructing images while accounting for metal objects. Key steps include identifying metal regions, interpolating or weighting missing projection data, and iteratively reconstructing images until artifacts are reduced. Compressed sensing methods can also exploit sparsity to reduce artifacts with fewer angular projections.
Dynamic texture based traffic vehicle monitoring systemeSAT Journals
Abstract Dynamic Textures are function of both space and time which exhibit spatio-temporal stationary property. They have spatially repetitive patterns which vary with time. In this paper, importance of phase spectrum in the signals is utilized and a novel method for vehicle monitoring is proposed with the help of Fourier analysis, synthesis and image processing. Methods like Doppler radar or GPS navigation are used commonly for tracking. The proposed image based approach has an added advantage that the clear image of the object (vehicle) can be used for future reference like proof of incidence, identification of owner and registration number. Keywords-Fourier Transform, dynamic texture, phase spectrum
EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE...cscpconf
This paper proposes an exemplar based image inpainting using extended wavelet transform. The
Image inpainting modifies an image with the available information outside the region to be
inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The
Laplacian pyramid is first used to capture the point discontinuities, and then followed by a
directional filter bank to link point discontinuities into linear structures. The proposed model
effectively captures the edges and contours of natural scene images
In this paper a novel edge detection method has been proposed which outperform Otsu method [1]. The proposed detection algorithm has been devised using the concept of genetic algorithm in spatial domain. The key of edge detection is the choice of threshold; which determines the results of edge detection. GA has been used to determine an optimal threshold over the image. Results are compared with existing Otsu technique which shows better performances
The document describes a modified Otsu method for edge detection using genetic algorithms. It begins with an introduction and literature review of existing edge detection techniques like Roberts, Sobel, and Prewitt operators as well as Otsu thresholding. The proposed technique uses genetic algorithms to determine an optimal threshold for edge detection. It initializes a population of threshold values as chromosomes, calculates their fitness based on class variance, and applies genetic operators like selection, crossover and mutation to arrive at the optimal threshold. Experimental results showed the modified Otsu method performed better than the original Otsu technique.
Abstract Edge detection is a fundamental tool used in most image processing applications. We proposed a simple, fast and efficient technique to detect the edge for the identifying, locating sharp discontinuities in an image and boundary of an image. In this paper, we found that proposed method called LookUp Table performs well, which requires least computational time as compared to conventional Edge Detection techniques. And also in this paper we presented a comparative performance of various conventional Edge Detection Techniques. Keywords: Edge detectors, Lookup table.
This document presents a new color image segmentation approach based on overlap wavelet transform (OWT). OWT extracts wavelet features to better separate different patterns in an image. The proposed method also uses morphological operators and 2D histogram clustering for effective segmentation. It is concluded that the proposed OWT method improves segmentation quality, is reliable, fast and computationally less complex than direct histogram clustering. When tested on various color spaces, the proposed segmentation scheme produced better results in RGB color space compared to others. The main advantages are its use of a single parameter and faster speed.
This document describes an image fusion method using pyramidal decomposition. It proposes extracting fine details from input images using guided filtering and fusing the base layers of images across multiple exposures or focal points using a multiresolution pyramid approach. A weight map is generated considering exposure, contrast, and saturation to guide the fusion of base layers. The fused base layer is then combined with extracted fine details to produce a detail-enhanced fused image. The goal is to preserve details in both very dark and extremely bright regions of the input images. It is argued that this method can effectively fuse images from different exposures or focal points without introducing artifacts.
This document presents a hybrid approach for color image segmentation that integrates color edge information and seeded region growing. It uses color edge detection in CIE L*a*b color space to select initial seed regions and guide region growth. Seeded region growing is performed based on color similarity between pixels. The edge map and region map are fused to produce homogeneous regions with closed boundaries. Small regions are then merged. The approach is tested on images from the Berkeley segmentation dataset and produces reasonably good segmentation results by combining color and edge information.
Hierarchical Approach for Total Variation Digital Image InpaintingIJCSEA Journal
The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consum ing process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.
Extraction of texture features by using gabor filter in wheat crop disease de...eSAT Journals
This document discusses a method for detecting diseases in wheat crops using image processing and artificial neural networks. It involves taking digital images of wheat crop leaves and preprocessing the images by applying Gaussian and median filters to reduce noise. The images are then segmented using CIELAB color space. Texture features like area, perimeter, contrast, and energy are extracted from the images using Gabor filters. These features are then fed into an artificial neural network classifier to identify the type of disease present in the wheat crop. The method aims to help farmers more quickly and accurately detect diseases so they can better manage their crops and increase agricultural productivity.
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.
Abstract: Many applications such as robot navigation, defense, medical and remote sensing performvarious processing tasks, which can be performed more easily when all objects in different images of the same scene are combined into a single fused image. In this paper, we propose a fast and effective method for image fusion. The proposed method derives the intensity based variations that is large and small scale, from the source images. In this approach, guided filtering is employed for this extraction. Gaussian and Laplacian pyramidal approach is then used to fuse the different layers obtained. Experimental results demonstrate that the proposed method can obtain better performance for fusion of
all sets of images. The results clearly indicate the feasibility of the proposed approach.
Edge detection of herbal plants is a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply and has discontinuities. They are defined as the set of curved line segments termed edges. Effective edge detection for microscopic image of herbal plant is proposed through this paper which compares the edge detected images and then performs further segmentation. Comparison between Sobel operator, Prewitt, Canny and Robert cross operators is performed. Our method after efficient edge detection performs Gabor filter and K-means clustering to procure a better image. It is then subjected to further segmentation. Experimental methods in our proposed algorithm show that our method achieves a better edge detection as compared to other edge detector operators. Our proposed algorithm provides the maximum PSNR value of 43.684 amongst the other commercial edge detection operators.
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
This paper presents an improved edge detection algorithm for facial and remotely sensed images using
vector order statistics. The developed algorithm processes coloured images directly without been converted
to grey scale. A number of the existing algorithms converts the coloured images into grey scale before
detection of edges. But this process leads to inaccurate precision of recognized edges, thus producing false
and broken edges in the output edge map. Facial and remotely sensed images consist of curved edge lines
which have to be detected continuously to prevent broken edges. In order to deal with this, a collection of
pixel approach is introduced with a view to minimizing the false and broken edges that exists in the
generated output edge map of facial and remotely sensed images.
An efficient image segmentation approach through enhanced watershed algorithmAlexander Decker
This document proposes an efficient image segmentation approach combining an enhanced watershed algorithm and color histogram analysis. The watershed algorithm is applied to preprocessed images after merging the results with an enhanced edge detection. Over-segmentation issues are addressed through a post-processing step applying color histogram analysis to each segmented region, improving overall performance. The document provides background on image segmentation techniques, reviews related work applying watershed algorithms, and discusses challenges like over-segmentation that watershed approaches can face.
Image segmentation refers to partitioning a digital image into multiple regions or sets of pixels based on characteristics like color or texture. The goal is to simplify the image representation to make it easier to analyze. Some applications in medical imaging include locating tumors, measuring tissue volumes, and computer-guided surgery. Common segmentation techniques include thresholding, edge detection, region growing, and split-and-merge approaches.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
This document summarizes a research paper that proposes a content-based image retrieval system using cascaded color and texture features. Color features are first extracted from images using statistical measures like mean, standard deviation, energy, entropy, skewness and kurtosis. Similarity to a query image is then measured using distance metrics. The top 150 most similar images are then analyzed to extract Haralick texture features. Similarity is again measured to retrieve the most relevant images. The paper finds that Canberra distance provides better retrieval results than other distance metrics like City Block and Minkowski.
Iris Localization - a Biometric Approach Referring Daugman's AlgorithmEditor IJCATR
In general, there are many methods of biometric identification. But the Iris
recognition is most accurate and secure means of biometric identification. Iris has
many properties which makes it ideal biometric identification. There are many
methods used to identify the Iris location. To locate Iris many traditional methods are
used. In this we proposed such methods which can identify Iris Center(IC) as well as
localize its center. In this paper we are proposing a method which can use novel IC
localization method on the fact that the elliptical shape (ES) of Iris varies according to
the rotation of eye movement. In this paper various IC locations are generated and
stored in database. Finally the location of IC is detected by matching the ES of the Iris
of input eye image withes candidates in DB. In this paper we are comparing different
methods for Iris localization.
This document summarizes various methods used to remove metal artifacts from dental CT images. It discusses projection completion methods, filtered back projection, maximum likelihood transmission, iterative reconstruction, and linear interpolation methods. The majority of metal artifact reduction methods involve reconstructing images while accounting for metal objects. Key steps include identifying metal regions, interpolating or weighting missing projection data, and iteratively reconstructing images until artifacts are reduced. Compressed sensing methods can also exploit sparsity to reduce artifacts with fewer angular projections.
Dynamic texture based traffic vehicle monitoring systemeSAT Journals
Abstract Dynamic Textures are function of both space and time which exhibit spatio-temporal stationary property. They have spatially repetitive patterns which vary with time. In this paper, importance of phase spectrum in the signals is utilized and a novel method for vehicle monitoring is proposed with the help of Fourier analysis, synthesis and image processing. Methods like Doppler radar or GPS navigation are used commonly for tracking. The proposed image based approach has an added advantage that the clear image of the object (vehicle) can be used for future reference like proof of incidence, identification of owner and registration number. Keywords-Fourier Transform, dynamic texture, phase spectrum
EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE...cscpconf
This paper proposes an exemplar based image inpainting using extended wavelet transform. The
Image inpainting modifies an image with the available information outside the region to be
inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The
Laplacian pyramid is first used to capture the point discontinuities, and then followed by a
directional filter bank to link point discontinuities into linear structures. The proposed model
effectively captures the edges and contours of natural scene images
In this paper a novel edge detection method has been proposed which outperform Otsu method [1]. The proposed detection algorithm has been devised using the concept of genetic algorithm in spatial domain. The key of edge detection is the choice of threshold; which determines the results of edge detection. GA has been used to determine an optimal threshold over the image. Results are compared with existing Otsu technique which shows better performances
The document describes a modified Otsu method for edge detection using genetic algorithms. It begins with an introduction and literature review of existing edge detection techniques like Roberts, Sobel, and Prewitt operators as well as Otsu thresholding. The proposed technique uses genetic algorithms to determine an optimal threshold for edge detection. It initializes a population of threshold values as chromosomes, calculates their fitness based on class variance, and applies genetic operators like selection, crossover and mutation to arrive at the optimal threshold. Experimental results showed the modified Otsu method performed better than the original Otsu technique.
Abstract Edge detection is a fundamental tool used in most image processing applications. We proposed a simple, fast and efficient technique to detect the edge for the identifying, locating sharp discontinuities in an image and boundary of an image. In this paper, we found that proposed method called LookUp Table performs well, which requires least computational time as compared to conventional Edge Detection techniques. And also in this paper we presented a comparative performance of various conventional Edge Detection Techniques. Keywords: Edge detectors, Lookup table.
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
REGION CLASSIFICATION BASED IMAGE DENOISING USING SHEARLET AND WAVELET TRANSF...csandit
This paper proposes a neural network based region classification technique that classifies
regions in an image into two classes: textures and homogenous regions. The classification is
based on training a neural network with statistical parameters belonging to the regions of
interest. An application of this classification method is applied in image denoising by applying
different transforms to the two different classes. Texture is denoised by shearlets while
homogenous regions are denoised by wavelets. The denoised results show better performance
than either of the transforms applied independently. The proposed algorithm successfully
reduces the mean square error of the denoised result and provides perceptually good results.
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.
AN OPTIMAL SOLUTION FOR IMAGE EDGE DETECTION PROBLEM USING SIMPLIFIED GABOR W...IJCSEIT Journal
Edge detection plays a vital role in computer vision and image processing. Edge of the image is one of the
most significant features which are mainly used for image analyzing process. An efficient algorithm for
extracting the edge features of images using simplified version of Gabor Wavelet is proposed in this paper.
Conventional Gabor Wavelet is widely used for edge detection applications. Due do the high computational
complexity of conventional Gabor Wavelet, this may not be used for real time application. Simplified Gabor
wavelet based approach is highly effective at detecting both the location and orientation of edges. The
results proved that the performance of proposed Simplified version of Gabor wavelet is superior to
conventional Gabor Wavelet, other edge detection algorithm and other wavelet based approach. The
performance of the proposed method is proved with the help of FOM, PSNR and Average run time.
REGION CLASSIFICATION BASED IMAGE DENOISING USING SHEARLET AND WAVELET TRANSF...cscpconf
This paper proposes a neural network based region classification technique that classifies
regions in an image into two classes: textures and homogenous regions. The classification is
based on training a neural network with statistical parameters belonging to the regions of
interest. An application of this classification method is applied in image denoising by applying
different transforms to the two different classes. Texture is denoised by shearlets while
homogenous regions are denoised by wavelets. The denoised results show better performance
than either of the transforms applied independently. The proposed algorithm successfully
reduces the mean square error of the denoised result and provides perceptually good results.
Comparative Analysis of Common Edge Detection Algorithms using Pre-processing...IJECEIAES
Edge detection is the process of segmenting an image by detecting discontinuities in brightness. Several standard segmentation methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image pre-processing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard edge detection methods. The proposed preprocessing approach involves median filtering to reduce the noise in image and then edge detection technique is carried out. Finally, Standard edge detection methods can be applied to the resultant pre-processing image and its Simulation results are show that our pre-processed approach when used with a standard edge detection method enhances its performance.
This document compares different first order edge detection techniques, including Canny, Sobel, Roberts, and Prewitt. It presents the methodology and steps for edge detection, including smoothing, enhancement, detection and localization. It then describes each technique in detail, providing the algorithms and edge detected images. The conclusion is that the Canny filter produces better results than the other techniques, but parameters can be adjusted for different requirements. Comparing the techniques helps evaluate their ability to detect edges in images.
Algorithm for the Comparison of Different Types of First Order Edge Detection...IOSR Journals
This document compares different first order edge detection techniques, including Canny, Sobel, Roberts, and Prewitt. It presents an algorithm to rigorously evaluate and compare the performance of these techniques. The algorithm is applied to detect edges in a sample image containing multiple objects. Simulation results show that the Canny technique produces the best edges, while the others detect weaker edges. The document concludes by discussing the ability of edge detection to simplify images by identifying boundaries and discontinuities, which is important for tasks like image analysis, pattern recognition, and computer vision.
Mislaid character analysis using 2-dimensional discrete wavelet transform for...IJMER
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.
This document discusses techniques for image segmentation and edge detection. It proposes a generalized boundary detection method called Gb that combines low-level and mid-level image representations in a single eigenvalue problem to detect boundaries. Gb achieves state-of-the-art results at low computational cost. Soft segmentation is also introduced to improve boundary detection accuracy with minimal extra computation. Common methods for edge detection are described, including gradient-based, texture-based, and projection profile-based approaches. Improved Harris and corner detection algorithms are presented to more accurately detect edges and corners. The output of Gb using soft segmentations as input is shown to correlate well with occlusions and whole object boundaries while capturing general boundaries.
Performance of Efficient Closed-Form Solution to Comprehensive Frontier Exposureiosrjce
This document discusses boundary detection techniques for images. It proposes a generalized boundary detection method (Gb) that combines low-level and mid-level image representations in a single eigenvalue problem to detect boundaries. Gb achieves state-of-the-art results at low computational cost. Soft segmentation and contour grouping methods are also introduced to further improve boundary detection accuracy with minimal extra computation. The document presents outputs of Gb on sample images and concludes that Gb effectively detects boundaries in a principled manner by jointly resolving constraints from multiple image interpretation layers in closed form.
Performance Evaluation of Image Edge Detection Techniques CSCJournals
The success of an image recognition procedure is related to the quality of the edges marked. The
aim of this research is to investigate and evaluate edge detection techniques when applied to
noisy images at different scales. Sobel, Prewitt, and Canny edge detection algorithms are
evaluated using artificially generated images and comparison criteria: edge quality (EQ) and map
quality (MQ). The results demonstrated that the use of these criteria can be utilized as an aid for
further analysis and arbitration to find the best edge detector for a given image.
Denoising and Edge Detection Using SobelmethodIJMER
The main aim of our study is to detect edges in the image without any noise , In many of the images edges carry important information of the image, this paper presents a method which consists of sobel operator and discrete wavelet de-noising to do edge detection on images which include white Gaussian noises. There were so many methods for the edge detection, sobel is the one of the method, by using this sobel operator or median filtering, salt and pepper noise cannot be removed properly, so firstly we use complex wavelet to remove noise and sobel operator is used to do edge detection on the image. Through the pictures obtained by the experiment, we can observe that compared to other methods, the method has more obvious effect on edge detection.
In this paper, we analyze and compare the performance of fusion methods based on four different
transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled
contourlet transform. Fusion framework and scheme are explained in detail, and two different sets of
images are used in our experiments. Furthermore, eight different performancemetrics are adopted to
comparatively analyze the fusion results. The comparison results show that the nonsubsampled contourlet
transform method performs better than the other three methods, both spatially and spectrally. We also
observed from additional experiments that the decomposition level of 3 offered the best fusion performance,
anddecomposition levels beyond level-3 did not significantly improve the fusion results.
A Novel Method for Detection of Architectural Distortion in MammogramIDES Editor
Among various breast abnormalities architectural
distortion is the most difficult type of tumor to detect. When
area of interest is medical image data, the major concern is to
develop methodologies which are faster in computation and
relatively noise free in processing. This paper is an extension
of our own work where we propose a hybrid methodology that
combines a Gabor filtration with directional filters over the
directional spectrum for digitized mammogram processing.
The most commendable thing in comparison to other
approaches is that complexity has been lowered as well as the
computation time has also been reduced to a large extent. On
the MIAS database we achieved a sensitivity of 89 %.
Using Generic Image Processing Operations to Detect a Calibration GridJan Wedekind
Camera calibration is an important problem in 3D computer vision. The problem of determining the camera parameters has been studied extensively. However the algorithms for determining the required correspondences are either semi-automatic (i.e. they require user interaction) or they involve difficult to implement custom algorithms.
We present a robust algorithm for detecting the corners of a calibration grid and assigning the correct correspondences for calibration . The solution is based on generic image processing operations so that it can be implemented quickly. The algorithm is limited to distortion-free cameras but it could potentially be extended to deal with camera distortion as well. We also present a corner detector based on steerable filters. The corner detector is particularly suited for the problem of detecting the corners of a calibration grid.
- See more at: http://figshare.com/articles/Using_Generic_Image_Processing_Operations_to_Detect_a_Calibration_Grid/696880#sthash.EG8dWyTH.dpuf
2-Dimensional Wavelet pre-processing to extract IC-Pin information for disarr...IOSR Journals
Abstract: Due to higher processing power to cost ratio, it is now possible to replace the manual detection methods used in the IC (Integrated Circuit) industry by Image-processing based automated methods, to detect a broken pin of an IC connected on a PCB during manufacturing, which will make the process faster, easier and cheaper. In this paper an accurate and fast automatic detection method is used where the top view camera shots of PCBs are processed using advanced methods of 2-dimensional discrete wavelet pre-processing before applying edge-detection. Comparison with conventional edge detection methods such as Sobel, Prewitt and Canny edge detection without 2-D DWT is also performed. Keywords :2-dimensional wavelets, Edge detection, Machine vision, Image processing, Canny.
Similar to Comparative studies of multiscale edge detection using different edge detectors for MRI thigh (20)
Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
A secure and energy saving protocol for wireless sensor networksjournalBEEI
The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptography’s concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.
Plant leaf identification system using convolutional neural networkjournalBEEI
This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.
Customized moodle-based learning management system for socially disadvantaged...journalBEEI
This study aims to develop Moodle-based LMS with customized learning content and modified user interface to facilitate pedagogical processes during covid-19 pandemic and investigate how teachers of socially disadvantaged schools perceived usability and technology acceptance. Co-design process was conducted with two activities: 1) need assessment phase using an online survey and interview session with the teachers and 2) the development phase of the LMS. The system was evaluated by 30 teachers from socially disadvantaged schools for relevance to their distance learning activities. We employed computer software usability questionnaire (CSUQ) to measure perceived usability and the technology acceptance model (TAM) with insertion of 3 original variables (i.e., perceived usefulness, perceived ease of use, and intention to use) and 5 external variables (i.e., attitude toward the system, perceived interaction, self-efficacy, user interface design, and course design). The average CSUQ rating exceeded 5.0 of 7 point-scale, indicated that teachers agreed that the information quality, interaction quality, and user interface quality were clear and easy to understand. TAM results concluded that the LMS design was judged to be usable, interactive, and well-developed. Teachers reported an effective user interface that allows effective teaching operations and lead to the system adoption in immediate time.
Understanding the role of individual learner in adaptive and personalized e-l...journalBEEI
Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.
Prototype mobile contactless transaction system in traditional markets to sup...journalBEEI
1) Researchers developed a prototype contactless transaction system using QR codes and digital payments to support physical distancing during the COVID-19 pandemic in traditional markets.
2) The system allows sellers and buyers in traditional markets to conduct fast, secure transactions via smartphones without direct cash exchange. Buyers scan sellers' QR codes to view product details and make e-wallet payments.
3) Testing showed the system's functions worked properly and users found it easy to use and useful for supporting contactless transactions and digital transformation of traditional markets. However, further development is needed to increase trust in digital payments for users unfamiliar with the technology.
Wireless HART stack using multiprocessor technique with laxity algorithmjournalBEEI
The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.
Implementation of double-layer loaded on octagon microstrip yagi antennajournalBEEI
This document describes the implementation of a double-layer structure on an octagon microstrip yagi antenna (OMYA) to improve its performance at 5.8 GHz. The double-layer consists of two double positive (DPS) substrates placed above the OMYA. Simulation and experimental results show that the double-layer configuration increases the gain of the OMYA by 2.5 dB compared to without the double-layer. The measured bandwidth of the OMYA with double-layer is 14.6%, indicating the double-layer can increase both the gain and bandwidth of the OMYA.
The calculation of the field of an antenna located near the human headjournalBEEI
In this work, a numerical calculation was carried out in one of the universal programs for automatic electro-dynamic design. The calculation is aimed at obtaining numerical values for specific absorbed power (SAR). It is the SAR value that can be used to determine the effect of the antenna of a wireless device on biological objects; the dipole parameters will be selected for GSM1800. Investigation of the influence of distance to a cell phone on radiation shows that absorbed in the head of a person the effect of electromagnetic radiation on the brain decreases by three times this is a very important result the SAR value has decreased by almost three times it is acceptable results.
Exact secure outage probability performance of uplinkdownlink multiple access...journalBEEI
In this paper, we study uplink-downlink non-orthogonal multiple access (NOMA) systems by considering the secure performance at the physical layer. In the considered system model, the base station acts a relay to allow two users at the left side communicate with two users at the right side. By considering imperfect channel state information (CSI), the secure performance need be studied since an eavesdropper wants to overhear signals processed at the downlink. To provide secure performance metric, we derive exact expressions of secrecy outage probability (SOP) and and evaluating the impacts of main parameters on SOP metric. The important finding is that we can achieve the higher secrecy performance at high signal to noise ratio (SNR). Moreover, the numerical results demonstrate that the SOP tends to a constant at high SNR. Finally, our results show that the power allocation factors, target rates are main factors affecting to the secrecy performance of considered uplink-downlink NOMA systems.
Design of a dual-band antenna for energy harvesting applicationjournalBEEI
This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
Transforming data-centric eXtensible markup language into relational database...journalBEEI
eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.
Key performance requirement of future next wireless networks (6G)journalBEEI
The document provides an overview of the key performance indicators (KPIs) for 6G wireless networks compared to 5G networks. Some of the major KPIs discussed for 6G include: achieving data rates of up to 1 Tbps and individual user data rates up to 100 Gbps; reducing latency below 10 milliseconds; supporting up to 10 million connected devices per square kilometer; improving spectral efficiency by up to 100 times through technologies like terahertz communications and smart surfaces; and achieving an energy efficiency of 1 pico-joule per bit transmitted through techniques like wireless power transmission and energy harvesting. The document outlines how 6G aims to integrate terrestrial, aerial and maritime communications into a single network to provide ubiquitous connectivity with higher
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
Modeling climate phenomenon with software grids analysis and display system i...journalBEEI
This study aims to model climate change based on rainfall, air temperature, pressure, humidity and wind with grADS software and create a global warming module. This research uses 3D model, define, design, and develop. The results of the modeling of the five climate elements consist of the annual average temperature in Indonesia in 2009-2015 which is between 29oC to 30.1oC, the horizontal distribution of the annual average pressure in Indonesia in 2009-2018 is between 800 mBar to 1000 mBar, the horizontal distribution the average annual humidity in Indonesia in 2009 and 2011 ranged between 27-57, in 2012-2015, 2017 and 2018 it ranged between 30-60, during the East Monsoon, the wind circulation moved from northern Indonesia to the southern region Indonesia. During the west monsoon, the wind circulation moves from the southern part of Indonesia to the northern part of Indonesia. The global warming module for SMA/MA produced is feasible to use, this is in accordance with the value given by the validate of 69 which is in the appropriate category and the response of teachers and students through a 91% questionnaire.
An approach of re-organizing input dataset to enhance the quality of emotion ...journalBEEI
The purpose of this paper is to propose an approach of re-organizing input data to recognize emotion based on short signal segments and increase the quality of emotional recognition using physiological signals. MIT's long physiological signal set was divided into two new datasets, with shorter and overlapped segments. Three different classification methods (support vector machine, random forest, and multilayer perceptron) were implemented to identify eight emotional states based on statistical features of each segment in these two datasets. By re-organizing the input dataset, the quality of recognition results was enhanced. The random forest shows the best classification result among three implemented classification methods, with an accuracy of 97.72% for eight emotional states, on the overlapped dataset. This approach shows that, by re-organizing the input dataset, the high accuracy of recognition results can be achieved without the use of EEG and ECG signals.
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.
Quality of service performances of video and voice transmission in universal ...journalBEEI
The universal mobile telecommunications system (UMTS) has distinct benefits in that it supports a wide range of quality of service (QoS) criteria that users require in order to fulfill their requirements. The transmission of video and audio in real-time applications places a high demand on the cellular network, therefore QoS is a major problem in these applications. The ability to provide QoS in the UMTS backbone network necessitates an active QoS mechanism in order to maintain the necessary level of convenience on UMTS networks. For UMTS networks, investigation models for end-to-end QoS, total transmitted and received data, packet loss, and throughput providing techniques are run and assessed and the simulation results are examined. According to the results, appropriate QoS adaption allows for specific voice and video transmission. Finally, by analyzing existing QoS parameters, the QoS performance of 4G/UMTS networks may be improved.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
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
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Comparative studies of multiscale edge detection using different edge detectors for MRI thigh
1. Bulletin of Electrical Engineering and Informatics
Vol. 10, No. 4, August 2021, pp. 1979~1986
ISSN: 2302-9285, DOI: 10.11591/eei.v10i4.2220 1979
Journal homepage: http://beei.org
Comparative studies of multiscale edge detection using different
edge detectors for MRI thigh
Belinda Chong Chiew Meng, Dayang Suhaida Awang Damit, Nor Salwa Damanhuri
Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Malaysia
Article Info ABSTRACT
Article history:
Received Feb 13, 2020
Revised Apr 15, 2021
Accepted May 30, 2021
Edge detection plays an important role in computer vision to extract object
boundary. Multiscale edge detection method provides a variety of image
features by different resolution at multiscale of edges. The method extracts
coarse and fine structure edges simultaneously in an image. Due to this, the
multiscale method enables more reliable edges are detected. Most of the
multiscale methods are not translation invariant due to the decimated process.
They mostly depend on the corresponding transform coefficients. These
methods need more computation and a larger storage space. This study
proposes a multiscale method that uses an average filter to smooth image at
three different scales. Three different classical edge detectors namely Prewitt,
Sobel and Laplacian were used to extract the edges from the smooth images.
The edges extracted from the different scales of smooth images were then
combined to form the multiscale edge detection. The performances of the
multiscale images extracted from the three classical edge detectors were then
compared and discussed.
Keywords:
Edge detection
Laplacian
Multiscale
Prewitt
Sobel
This is an open access article under the CC BY-SA license.
Corresponding Author:
Belinda Chong Chiew Meng
Faculty of Electrical Engineering
Universiti Teknologi MARA, Cawangan Pulau Pinang
Jalan Permatang Pauh, 13500 Permatang Pauh, Malaysia
Email: belinda.chong@uitm.edu.my
1. INTRODUCTION
Edge detection is one of the fundamental tools to find the boundaries of an objects for object
segmentation and feature extraction [1]. There are two main edge detection operators used for edge detection;
the first derivative based and the second derivative based. The first derivative edge detector operator
computes the image gradient values to detect image edges. The edges are estimated based on gradient
magnitude which is calculated in the x and y directions [2]. The second derivative edge detector operator uses
zero-crossing for edge detector. The method finds the rate of change in grey intensity and then detects the
local maxima in the gradient magnitude. It locates the centres of thick edges and the localization of edges is
good [2], [3].
In most cases, the edge pixels are discontinued in the edge detection. This is because the process is
based on local change in image intensity. In practical, a connected curve that shows the boundary of an object
is desirable for feature extraction. Generally, this type of edge detector is sensitive to noise, and this is
because the differential edge detectors behaves as a high-pass filter and thus, it has a tendency to amplify
noise [4], [5]. Due to this, it is difficult to differentiate the true and false edges based on the differential edge
detector. Multiscale edge detection has attracted a lot of attention in image processing applications due to the
fact that edges are multiscale in nature [6]-[8]. The edges extracted from a different scale or resolution are
then combined to get the final edge detection result. Multiscale edge detection method has the ability to
2. ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 4, August 2021 : 1979 – 1986
1980
describe the variety of the edge structures [8]-[10]. Wavelet transform is one of the methods often used for
multiscale edge extraction. The wavelet coefficients represent the scale of features in an image. It can be used
to measure how closely the correlated wavelet is with each section of the image [11]-[13].
S. V Seeri et al. used Haar discrete wavelet transform as multi-resolution method to model the
characteristics of textured images [14]. This method well characterized the edges of the textured images. The
method fused edges that were extracted from three sub bands which were horizontal (H), vertical (V) and
diagonal (D) for edge information. After the edges were extracted, the method also integrated with Sobel
edge detector, fuzzy thresholding and morphological operators method to segment and classify the text
regions. One of the advantages of the method is that it is able to localize the text regions accurately within the
three types of scripts which are English, Kannada and Hindi. Wavelet transform, multiscale method is
sensitive to local regularity and it has the limitation of detecting orientation of singularity curve. K. Guo et al.
applied framework of Shearlet transform into wavelet transform to improve the edge orientation and curve
point detection [15]. The modified method had the ability to estimate the local flatness of an edge and it
could also provide detailed information on geometry edge. Obviously, the method has the advantage of non-
parabolic scaling and it is able to discriminate features.
Thigh bone is the longest and strongest bone of the entire human body. Various diseases often occur
in children and adults. Magnetic resonance imaging (MRI) is an important imaging tool used to investigate
the presence of certain abnormalities in thigh [16], [17]. Because of the nature of quantitative MRI imaging
data, this requires a considerable amount of human intervention and expertise training opportunities. This
process is tedious, time consuming and labour intensive. The development of automatic computer-aided
diagnosis system has the additional benefits to help in diagnosing various pathologies [18], [19]. The purpose
of this study is to present the formation of multiscale edge detection method that uses three different kernel
sizes of averaging filter to smooth image into three different scales. The edges were extracted from three
averaging filters that represented coarse to fine scale. Three classical edge extraction operators namely
Prewitt, Sobel and Laplacian were used to extract the edges to establish the multiscale edge detection
method. The performance of the three classical edge extraction methods was compared.
This paper is organized as follows: in section 2, theories related to averaging filter and edge
detection methods on first-order and second-order edge detector operator are presented, while in section 3, an
edge extraction method for multiscale edge extraction method is proposed. To clarify, we used three edge
extraction operators for the process of multiscale. The experimental results and discussions on the
comparison results are then discussed in section 4. Finally, some conclusions are drawn and discussed in
section 5.
2. AVERAGING FILTER AND EDGE DETECTION METHOD
Generally, averaging filter is also known as mean filter. It has smoothing effect by average value of
the image to reduce noise. On the other hand, classical edge detector is often used for edge detection due to
its simplicity [20], [21].
2.1. Averaging filter
The implementation of average filtering is to smooth images. The average filter reduces the amount
of intensity variation by averaging the pixel itself with the pixel value around its neighbours [22], [23].
Figure 1 shows the kernel averaging operator for 3x3, 5x5 and 7x7. A 3x3 square kernel as shown in
Figure 1 (a) is often used to smooth images. The smoothing effect produced depends on the size of the
averaging filter. Some details in the image will be smoothed by using a 3x3 averaging filter. The effect of
smoothness will be increased by using a 5x5 averaging filter as shown in Figure 1 (b). Next, Figure 1 (c)
show the kernel of 7x7 averaging filter. The smoothing output of a 7x7 averaging filter will be more than a
5x5 averaging filter. Therefore, larger kernels can be used for more severe smoothing.
1 49
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(a) (b) (c)
Figure 1. Averaging filter, (a) 3x3 kernel, (b) 5x5 kernel, (c) 7x7 kernel
3. Bulletin of Electr Eng & Inf ISSN: 2302-9285
Comparative studies of multiscale edge detection using different edge … (Belinda Chong Chiew Meng)
1981
2.2. The first order edge detector operator
Figure 2 shows the Prewitt edge operator model [24], Figure 2 (a) shows vertical direcation,
Figure 2 (b) shows horizontal direction. Prewitt edge has one by one model that is used to detect edges in an
image. Based on the model, the values that are symmetrical and central (x, y) will make the model take the
maximum value that is similar to the detected region.
Figure 3 shows the Sobel edge operator model [25], Figure 3 (a) shows vertical direcation,
Figure 3 (b) shows horizontal direction. Sobel operator computes an approximation of edge from the gradient
of the image intensity function. Sobel operator gives weight to the point that lies closer to (x, y). As a result,
it has smoothing effect and it is also less sensitive to image noise.
-1 0 +1 +1 +1 +1
-1 0 +1 0 0 0
-1 0 +1 -1 -1 -1
(a) (b)
-1 0 +1 -1 -2 -1
-2 0 +2 0 0 0
-1 0 +1 +1 +2 +1
(a) (b)
Figure 2. Prewitt edge model, (a) vertical
direcation, (b) horizontal direction
Figure 3. Sobel edge model, (a) vertical direcation,
(b) horizontal direction
2.3. The second order edge detector operator
Laplacian is the second spatial derivative that has a 2D and isotropic measure of an image. Figure 4
shows the two commonly used Laplacian edge operator models [25], Figure 4 (a) shows outward edges,
Figure 4 (b) shows inward edges. The operator enhances the regions of intensity discontinuities and
highlights the regions that have rapid intensity change. Thus it is often used to detect fine edges. Due to its
characteristic that it is sensitive to noise, the image often is smoothed with Gaussian smoothing filter to
reduce noise.
0 +1 0 0 -1 0
+1 -4 +1 -1 +4 -1
0 +1 0 0 -1 0
(a) (b)
Figure 4. Laplacian operator, (a) outward edges, (b) inward edges
3. THE PROPOSED MULTISCALE METHOD
The method adapted the concept of multiscale method to extract edges from different resolutions on
thigh images. The proposed method is illustrated in Figure 5. Three different kernel sizes of averaging filters
were used to smooth the sharpened images and produce three different scales. This was conducted in order to
achieve the coarse to fine scale for the multiscale concept. The method involved three main processes which
were image sharpening, multiscale and edge extraction by classical edge detectors as shown in Figure 5.
Firstly, the thigh images were sharpened by using Prewitt operator which rotated in eight directions;
north, west, south, east, north west, south west, south east and north east. In this process, eight sharpened
images were produced. Prewitt operator was chosen due to its its less computation complexity. Prewitt edge
detector produces edges which were the most similar to the original images because of its weight on the
model.
The concept of multiscale method which included coarse-to-fine edge extraction method based on
the size of the averaging filter was then applied to smooth the sharpened images. The eight sharpened images
were then smoothed with three different averaging filters which were 3x3, 5x5 and 7x7. The averaging filter
was used to produce images from three different scales which were high scale (3x3 kernel), middle scale (5x5
kernel) and low scale (7x7 kernel). Edge extraction was then performed on the output of each averaging filter
using three different classical edge extraction detectors. The edges extracted from the eight output images
from each averaging filter (Prewitt, Sobel and Laplacian) were then combined. A comparison analysis was
carried out based on the edge detection from each edge detector. Figure 6 illustrates the steps of the edge
extraction and the edges combination for each scale.
The comparison analysis was then carried out for each detector. Figure 6 shows that the output from
the three averaging filters were channelled into Prewitt, Sobel and Laplacian edge detectors for edge
4. ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 4, August 2021 : 1979 – 1986
1982
extraction from coarse-to-fine edge extraction. The output of multiscale edge extraction from each edge
detector was then compared.
Figure 5. The concepts of multiscale edge extraction method
Figure 6. Comparison of multiscale methods
4. RESULTS AND DISCUSSION
Edge extraction is a process to detect the boundaries, so that objects can be extracted from the
background image. Figure 7 (a) shows the original MRI image. The edges extracted from an edge detector
operator often depends on image brightness whereby curved line or edge is extracted when the intensity
variation is high in the image. In Figure 7 (b) and 7 (c), it is obvious that the edges extracted from Prewitt and
Sobel edge detectors are very similar. The extracted edges from the outlines of the boundaries on muscle, fat
and bone morrow are clear. However, it can be seen that some discontinued edges appear in the image. The
discontinuity of surface depth and surface orientation cause discontinuity of edges. For Laplacian edge
detection, the detector could extract more fine detailed edges as shown in Figure 7 (d). Unfortunately, more
discontinued edges were found in the image and the fine detailed edges appear as noise in the image.
Generally, the discontinuity of edges could fail to locate important edges that could cause inaccuracy of
object extraction.
The multiscale edge extraction method extracted edges over a wide range of scales. When the
multiscale edge information fuse together, an edge map could be obtained. The edges information from
coarse to fine scale that made the edges were robust. The proposed multiscale edge extraction method is to
begin with image sharpening. Images were sharpened using Prewitt compass operator. This was because
Prewitt was simple and the edges extracted were very similar to the original image. As shown in Figure 8 (a)
to Figure 8 (f), eight images were produced based on eight different orientations. The sharpened images
enhanced the edges of the thigh image from eight different orientations.
5. Bulletin of Electr Eng & Inf ISSN: 2302-9285
Comparative studies of multiscale edge detection using different edge … (Belinda Chong Chiew Meng)
1983
(a) (b) (c) (d)
Figure 7. Edge detection for classical edge detector, (a) original, (b) prewitt extraction, (c) sobel extraction,
(d) laplacian extraction
(a) (b) (c) (d)
(e) (f) (g) (h)
Figure 8. Images sharpen on eight orientation using Prewitt compass operator, (a) east, (b) north, (c) north
east, (d) north west, (e) south, (f) south east, (g) south west, (h) west
Applying the multiscale concept, three differences sizes of average filters were used to smooth the
sharpened images for three different scales. Figure 9 (a) shows the original MRI image. Figure 9 (b),
Figure 9 (c) and Figure 9 (d) show the output of the averaging filter of 3x3, 5x5 and 7x7 respectively. The
output image was a weighted sum of the input pixel. As the mask size increased, the image became blurry
and it react to coarse-scale (low scale) methods. The smaller the mask size, it responded more to the spatial
accuracy of fine-scale (high scale) methods.
(a) (b) (c) (d)
Figure 9. Three difference of average filters, (a) original, (b) average 3x3, (c) average 5x5, (d) average 7x7
Three different classical edge detectors were used to extract the edges from the eight orientations of
the sharpened thigh images. The edges extracted from each sharpened orientation image from the same scale
were then combined. Figure 10 shows the edge extraction comparison between Prewitt, Sobel and Laplacian
edge extraction methods. Figure 10 (a), Figure 10 (b) and Figure 10 (c) show the output of Prewitt 3x3, 5x5
and 7x7 respectively. Figure 10 (d) is the output of multiscale edge extraction for Prewitt edge detector.
Figure 10 (e), Figure 10 (f) and Figure 10 (g) show the output of Sobel 3x3, 5x5 and 7x7 respectively.
6. ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 4, August 2021 : 1979 – 1986
1984
Figure 10 (h) is the output of multiscale edge extraction for Sobel edge detector. Figure 10 (i), Figure 10 (j)
and Figure 10 (k) show the output of Laplacian 3x3, 5x5 and 7x7 respectively. Figure 10 (l) is the output of
multiscale edge extraction for Laplacian edge detector.
(a) (b) (c) (d)
(e) (f) (g) (h)
(i) (j) (k) (l)
Figure 10. Result of three multiscale methods, (a) prewitt 3x3, (b) prewitt 5x5, (c) prewitt 7x7, (d) prewitt
multiscale, (e) sobel 3x3, (f) sobel 5x5, (g) sobel 7x7, (h) sobel multiscale, (i) laplacian 3x3, (j) laplacian
5x5, (k) laplacian 7x7, (l) laplacian multiscale
The output of the multiscale edge extraction in Figure 10 (d) for Prewitt detector, Figure 10 (h) for
Sobel detector and Figure 10 (l) for Laplacian detector could product continuous edges if compared to
Figure 7 (b), Figure 7 (c) and Figure 7 (d). In Figure 10, the resultant multiscale edges can be seen
continuous and the boundaries are clearly seen. In the experiment, both Prewitt and Sobel edge detectors
produced the edges that were most similar to the original image. Prewitt operator was sensitive to horizontal
and vertical edge; Sobel operator was sensitive to diagonal edge. Due to the proposal of multiscale method
considering all eight directions for edge extraction, the combination of eight direction edges caused the
Prewitt and Sobel operators to produce edges that were similar to the original image.
Laplacian operator is the second derivative operator that the method searches zero crossing to detect
edges in an image. As shown in Figure 10, when the kernel size gets bigger, Laplacian 7x7 (Figure 10 (k))
may yield better results in edge localization compared to Prewitt 7x7 (Figure 10 (c)) and Sobel 7x7
(Figure 10 (g)). Besides this, in the comparison analysis, it could be seen that the Laplacian operator was
very sensitive to noise as shown in Figure 10 (l). It was due to Laplacian operator attempting to find zero
crossing in the process of extracting edges. The fine detailed edges were extracted and amplified that this
caused the multiscale edge extracted become noisy by using Laplacian operator. The operator tended to
accentuate noise and cause noises to be detected in the background.
Figure 11 shows the comparison of other different parts of thigh. In the figure, it can be seen that the
edge extraction from Prewitt and Sobel detectors was close to the original thigh image. However, Laplacian
detection contained noise. This can be concluded that Prewitt and Sobel are more suitable for edge extraction
on thigh image. The edge extraction preserves the similarity if compared to the original image.
7. Bulletin of Electr Eng & Inf ISSN: 2302-9285
Comparative studies of multiscale edge detection using different edge … (Belinda Chong Chiew Meng)
1985
Patient 1
Patient 2
Patient 3
MRI image Prewitt Sobel Laplacian
Figure 11. Comparison of multiscale for Prewitt, Sobel and Laplacian on other thigh images
5. CONCLUSION
This paper presents a multiscale method that was created by using three different kernel size of
averaging filters. The averaging filter smoothed the image and produced three different scales according to
the kernel size of the filter. Prewitt, Sobel and Laplacian classical edge detectors were then used to extract the
edges at different scales. The performances of these three multiscale edge extractions were then compared. In
this study, it can be concluded that Prewitt and Sobel operators could extract edges most similar to the
original image. Laplacian could extract fine detailed edges. However, it is sensitive to noise. Laplacian
operator could convolve with some smoothing filters to reduce noise in the background. Finally, it can be
concluded that the proposed multiscale method that uses either Prewitt or Sobel edge detector as an edge
detector offers a great advantage in term of producing multiscale edge detection. In addition, due to its
simplicity, the method could reduce computation complexity and more importantly it has good edge
preservation and could help in feature extraction. For further improvements, it is recommended that the
image should be de-noised before the multiscale process takes place.
ACKNOWLEDGEMENTS
Authors would like to thank Universiti Teknologi MARA, Cawangan Pulau Pinang for all the
research facilities provided for this experiment.
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BIOGRAPHIES OF AUTHORS
Dr. Belinda Chong Chiew Meng received her B.Sc. (Hons) and M. Eng. in Electrical
Engineering in 1997 and 2002 from Universiti Teknologi Malaysia. She received the Ph. D
degrees in 2017 from Universiti Sains Malaysia. Currently, she is a senior lecturer at the Faculty
of Electrical Engineering, Universiti Teknologi MARA (UiTM), Cawangan Pulau Pinang,
Malaysia. Her main research interests include image processing and intelligent system.
Dayang Suhaida Awang Damit received her B.Eng Hons in Electrical Engineering from
Universiti Teknologi Malaysia in 2006 and MSc in Electronic Design from Universiti Sains
Malaysia in 2010. She is currently joined the Department of Electrical Engineering
(Communication), UiTM as a Lecturer where she occupied herself with teaching and other
educational tasks. Her broad research interests are Image Processing, Microwave absorber, and
Circuit design.
Dr. Nor Salwa Damanhuri received her B.Sc. (Hons) in Electrical & Electronics Engineering
from Universiti Tenaga Nasional (UNITEN) Malaysia. In 2004, she received the Excellence
Scheme Programme from MARA to pursue her MSc. in Control Systems Engineering in University
of Sheffield, United Kingdom. She had 4 years experiences as a Product Engineer in Freescale
Semiconductor Malaysia (fka Motorola Semiconductor) before embark her journey as a lecturer at
the Universiti Teknologi MARA (UiTM) Pulau Pinang. She obtained her PhD in Bioengineering
from University of Canterbury, New Zealand in 2015. Currently, she is a senior lecturer in Faculty
of Electrical Engineering, UiTM Pulau Pinang. Her research interests include lung mechanics,
system identification methods, modelling for type 2 diabetic patients and solar PV system.