Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate
the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and
edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing
algorithms, the JSEG, and the fast scanning algorithm. Due to the presence of speckle noise, segmentation of
Synthetic Aperture Radar (SAR) images is still a challenging problem. We proposed a fast SAR image
segmentation method based on Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA). In
this method, threshold estimation is regarded as a search procedure that examinations for an appropriate value in
a continuous grayscale interval. Hence, PSO-GSA algorithm is familiarized to search for the optimal threshold.
Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in
terms of segmentation accuracy, segmentation time, and Thresholding.
PERFORMANCE ANALYSIS USING SINGLE SEEDED REGION GROWING ALGORITHMAM Publications
Image segmentation is an important process and its results are used in many image processing
applications. Color images can increase the quality of segmentation, but increase the complexity of the problem. This
paper focuses on measurement of parameters that is RI,GCE,MMSE and time for segmentation using "Seeded Region
growing algorithm". Image segmentation techniques using region growing requires initial seeds selection, which
increases computational cost & execution time. To overcome this problem, a single seeded region growing technique for
image segmentation is proposed, which starts from the center pixel of the image as the initial seed. It grows region
according to the grow formula and selects the next seed from connected pixel of the region. The optimization is done
with fuzzy logic to improve the value of parameters.
The determination of Region-of-Interest has been recognised as an important means by which
unimportant image content can be identified and excluded during image compression or image
modelling, however existing Region-of-Interest detection methods are computationally
expensive thus are mostly unsuitable for managing large number of images and the compression
of images especially for real-time video applications. This paper therefore proposes an
unsupervised algorithm that takes advantage of the high computation speed being offered by
Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to
achieve fast and efficient Region-of-Interest detection.
Query Image Searching With Integrated Textual and Visual Relevance Feedback f...IJERA Editor
There are many researchers who have studied the relevance feedback in the literature of content based image
retrieval (CBIR) community, but none of CBIR search engines support it because of scalability, effectiveness
and efficiency issues. In this, we had implemented an integrated relevance feedback for retrieving of web
images. Here, we had concentrated on integration of both textual features (TF) and visual features (VF) based
relevance feedback (RF), simultaneously we also tested them individually. The TFRF employs and effective
search result clustering (SRC) algorithm to get salient phrases. Then a new user interface (UI) is proposed to
support RF. Experimental results show that the proposed algorithm is scalable, effective and accurated
A CONCERT EVALUATION OF EXEMPLAR BASED IMAGE INPAINTING ALGORITHMS FOR NATURA...cscpconf
Image inpainting derives from restoration of art works, and has been applied to repair ancient
art works. Inpainting is a technique of restoring a partially damaged or occluded image in an
undetectable way. It fills the damaged part of an image by employing information of the
undamaged part according to some rules to make it look “reasonable” to human eyes. Digital
image inpainting is relatively new area of research, but numerous and different approaches to
tackle the inpainting problem have been proposed since the concept was first introduced. This
paper analyzes and compares the recent exemplar based inpainting algorithms by Minqin Wang
and Hao Guo et al. A number of examples on real images are demonstrated to evaluate the
results of algorithms using Peak Signal to Noise Ratio (PSNR)
Land Boundary Detection of an Island using improved Morphological OperationCSCJournals
Image analysis is one of the important tasks to obtain the information about earth surface. To detect and mark a particular land area, it is required to have the image from remote place. To recognize the same, the accurate boundary of that area has to be detected. In this paper, the example of remote sensing image has been considered. The accurate detection of the boundary is a complex task. A novel method has been proposed in this paper to detect the boundary of such land. Mathematical morphology is a simple and efficient method for this type of task. The morphological analysis is performed using structure elements (SE). By using mathematical morphology the images can be enhanced and then the boundary can be detected easily. Simultaneously the noise is removed by using the proposed model. The results exhibit the performance of the proposed method. Keywords: Remote Sensing images ; Edge detection; Gray- scale Morphological analysis, Structuring Element (SE).
A NOVEL APPROACH FOR SEGMENTATION OF SECTOR SCAN SONAR IMAGES USING ADAPTIVE ...ijistjournal
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.
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.
PERFORMANCE ANALYSIS USING SINGLE SEEDED REGION GROWING ALGORITHMAM Publications
Image segmentation is an important process and its results are used in many image processing
applications. Color images can increase the quality of segmentation, but increase the complexity of the problem. This
paper focuses on measurement of parameters that is RI,GCE,MMSE and time for segmentation using "Seeded Region
growing algorithm". Image segmentation techniques using region growing requires initial seeds selection, which
increases computational cost & execution time. To overcome this problem, a single seeded region growing technique for
image segmentation is proposed, which starts from the center pixel of the image as the initial seed. It grows region
according to the grow formula and selects the next seed from connected pixel of the region. The optimization is done
with fuzzy logic to improve the value of parameters.
The determination of Region-of-Interest has been recognised as an important means by which
unimportant image content can be identified and excluded during image compression or image
modelling, however existing Region-of-Interest detection methods are computationally
expensive thus are mostly unsuitable for managing large number of images and the compression
of images especially for real-time video applications. This paper therefore proposes an
unsupervised algorithm that takes advantage of the high computation speed being offered by
Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to
achieve fast and efficient Region-of-Interest detection.
Query Image Searching With Integrated Textual and Visual Relevance Feedback f...IJERA Editor
There are many researchers who have studied the relevance feedback in the literature of content based image
retrieval (CBIR) community, but none of CBIR search engines support it because of scalability, effectiveness
and efficiency issues. In this, we had implemented an integrated relevance feedback for retrieving of web
images. Here, we had concentrated on integration of both textual features (TF) and visual features (VF) based
relevance feedback (RF), simultaneously we also tested them individually. The TFRF employs and effective
search result clustering (SRC) algorithm to get salient phrases. Then a new user interface (UI) is proposed to
support RF. Experimental results show that the proposed algorithm is scalable, effective and accurated
A CONCERT EVALUATION OF EXEMPLAR BASED IMAGE INPAINTING ALGORITHMS FOR NATURA...cscpconf
Image inpainting derives from restoration of art works, and has been applied to repair ancient
art works. Inpainting is a technique of restoring a partially damaged or occluded image in an
undetectable way. It fills the damaged part of an image by employing information of the
undamaged part according to some rules to make it look “reasonable” to human eyes. Digital
image inpainting is relatively new area of research, but numerous and different approaches to
tackle the inpainting problem have been proposed since the concept was first introduced. This
paper analyzes and compares the recent exemplar based inpainting algorithms by Minqin Wang
and Hao Guo et al. A number of examples on real images are demonstrated to evaluate the
results of algorithms using Peak Signal to Noise Ratio (PSNR)
Land Boundary Detection of an Island using improved Morphological OperationCSCJournals
Image analysis is one of the important tasks to obtain the information about earth surface. To detect and mark a particular land area, it is required to have the image from remote place. To recognize the same, the accurate boundary of that area has to be detected. In this paper, the example of remote sensing image has been considered. The accurate detection of the boundary is a complex task. A novel method has been proposed in this paper to detect the boundary of such land. Mathematical morphology is a simple and efficient method for this type of task. The morphological analysis is performed using structure elements (SE). By using mathematical morphology the images can be enhanced and then the boundary can be detected easily. Simultaneously the noise is removed by using the proposed model. The results exhibit the performance of the proposed method. Keywords: Remote Sensing images ; Edge detection; Gray- scale Morphological analysis, Structuring Element (SE).
A NOVEL APPROACH FOR SEGMENTATION OF SECTOR SCAN SONAR IMAGES USING ADAPTIVE ...ijistjournal
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.
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.
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.
Perceptual Weights Based On Local Energy For Image Quality AssessmentCSCJournals
This paper proposes an image quality metric that can effectively measure the quality of an image that correlates well with human judgment on the appearance of the image. The present work adds a new dimension to the structural approach based full-reference image quality assessment for gray scale images. The proposed method assigns more weight to the distortions present in the visual regions of interest of the reference (original) image than to the distortions present in the other regions of the image, referred to as perceptual weights. The perceptual features and their weights are computed based on the local energy modeling of the original image. The proposed model is validated using the image database provided by LIVE (Laboratory for Image & Video Engineering, The University of Texas at Austin) based on the evaluation metrics as suggested in the video quality experts group (VQEG) Phase I FR-TV test.
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A novel predicate for active region merging in automatic image segmentationeSAT 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 Feature extraction plays a vital role in the analysis and interpretation of remotely sensed data. The two important components of Feature extraction are Image enhancement and information extraction. Image enhancement techniques help in improving the visibility of any portion or feature of the image. Information extraction techniques help in obtaining the statistical information about any particular feature or portion of the image. This presented work focuses on the various feature extraction techniques and area of optical character recognition is a particularly important in Image processing. Keywords— Image character recognition, Methods for Feature Extraction, Basic Gabor Filter, IDA, and PCA.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Object extraction using edge, motion and saliency information from videoseSAT Journals
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
IMAGE SEGMENTATION BY USING THRESHOLDING TECHNIQUES FOR MEDICAL IMAGEScseij
Image binarization is the process of separation of pixel values into two groups, black as background and
white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This
paper describes a locally adaptive thresholding technique that removes background by using local mean
and standard deviation. Most common and simplest approach to segment an image is using thresholding.
In this work we present an efficient implementation for threshoding and give a detailed comparison of
Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is
implemented on medical images. The quality of segmented image is measured by statistical parameters:
Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).
Enhanced Optimization of Edge Detection for High Resolution Images Using Veri...ijcisjournal
dge Detection plays a crucial role in Image Processing and Segmentation where a set of algorithms aims
to identify various portions of a digital image at which a sharpened image is observed in the output or
more formally has discontinuities. The contour of Edge Detection also helps in Object Detection and
Recognition. Image edges can be detected by using two attributes such as Gradient and Laplacian. In our
Paper, we proposed a system which utilizes Canny and Sobel Operators for Edge Detection which is a
Gradient First order derivative function for edge detection by using Verilog Hardware Description
Language and in turn compared with the results of the previous paper in Matlab. The process of edge
detection in Verilog significantly reduces the processing time and filters out unneeded information, while
preserving the important structural properties of an image. This edge detection can be used to detect
vehicles in Traffic Jam, Medical imaging system for analysing MRI, x-rays by using Xilinx ISE Design
Suite 14.2.
Conheça o Grupo A, empresa que engloba várias editoras - Artmed, Artes Médicas, Bookman, McGraw-Hill do Brasil e Penso - e diversas plataformas de distribuição de informação técnica, científica e profissional.
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.
Perceptual Weights Based On Local Energy For Image Quality AssessmentCSCJournals
This paper proposes an image quality metric that can effectively measure the quality of an image that correlates well with human judgment on the appearance of the image. The present work adds a new dimension to the structural approach based full-reference image quality assessment for gray scale images. The proposed method assigns more weight to the distortions present in the visual regions of interest of the reference (original) image than to the distortions present in the other regions of the image, referred to as perceptual weights. The perceptual features and their weights are computed based on the local energy modeling of the original image. The proposed model is validated using the image database provided by LIVE (Laboratory for Image & Video Engineering, The University of Texas at Austin) based on the evaluation metrics as suggested in the video quality experts group (VQEG) Phase I FR-TV test.
FUZZY SEGMENTATION OF MRI CEREBRAL TISSUE USING LEVEL SET ALGORITHMAM Publications
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A novel predicate for active region merging in automatic image segmentationeSAT 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 Feature extraction plays a vital role in the analysis and interpretation of remotely sensed data. The two important components of Feature extraction are Image enhancement and information extraction. Image enhancement techniques help in improving the visibility of any portion or feature of the image. Information extraction techniques help in obtaining the statistical information about any particular feature or portion of the image. This presented work focuses on the various feature extraction techniques and area of optical character recognition is a particularly important in Image processing. Keywords— Image character recognition, Methods for Feature Extraction, Basic Gabor Filter, IDA, and PCA.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Object extraction using edge, motion and saliency information from videoseSAT Journals
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
IMAGE SEGMENTATION BY USING THRESHOLDING TECHNIQUES FOR MEDICAL IMAGEScseij
Image binarization is the process of separation of pixel values into two groups, black as background and
white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This
paper describes a locally adaptive thresholding technique that removes background by using local mean
and standard deviation. Most common and simplest approach to segment an image is using thresholding.
In this work we present an efficient implementation for threshoding and give a detailed comparison of
Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is
implemented on medical images. The quality of segmented image is measured by statistical parameters:
Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).
Enhanced Optimization of Edge Detection for High Resolution Images Using Veri...ijcisjournal
dge Detection plays a crucial role in Image Processing and Segmentation where a set of algorithms aims
to identify various portions of a digital image at which a sharpened image is observed in the output or
more formally has discontinuities. The contour of Edge Detection also helps in Object Detection and
Recognition. Image edges can be detected by using two attributes such as Gradient and Laplacian. In our
Paper, we proposed a system which utilizes Canny and Sobel Operators for Edge Detection which is a
Gradient First order derivative function for edge detection by using Verilog Hardware Description
Language and in turn compared with the results of the previous paper in Matlab. The process of edge
detection in Verilog significantly reduces the processing time and filters out unneeded information, while
preserving the important structural properties of an image. This edge detection can be used to detect
vehicles in Traffic Jam, Medical imaging system for analysing MRI, x-rays by using Xilinx ISE Design
Suite 14.2.
Conheça o Grupo A, empresa que engloba várias editoras - Artmed, Artes Médicas, Bookman, McGraw-Hill do Brasil e Penso - e diversas plataformas de distribuição de informação técnica, científica e profissional.
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
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.
A novel predicate for active region merging in automatic image segmentationeSAT Journals
Abstract Image segmentation is an elementary task in computer vision and image processing. This paper deals with the automatic image segmentation in a region merging method. Two essential problems in a region merging algorithm: order of merging and the stopping criterion. These two problems are solved by a novel predicate which is described by the sequential probability ratio test and the minimal cost criterion. In this paper we propose an Active Region merging algorithm which utilizes the information acquired from perceiving edges in color images in L*a*b* color space. By means of color gradient recognition method, pixels with no edges are clustered and considered alone to recognize some preliminary portion of the input image. The color information along with a region growth map consisting of completely grown regions are used to perform an Active region merging method to combine regions with similar characteristics. Experiments on real natural images are performed to demonstrate the performance of the proposed Active region merging method. Index Terms: Adaptive threshold generation, CIE L*a*b* color gradient, region merging, Sequential Probability Ratio Test (SPRT).
IMAGE SUBSET SELECTION USING GABOR FILTERS AND NEURAL NETWORKSijma
An automatic method for the selection of subsets of images, both modern and historic, out of a set of
landmark large images collected from the Internet is presented in this paper. This selection depends on the
extraction of dominant features using Gabor filtering. Features are selected carefully from a preliminary
image set and fed into a neural network as a training data. The method collects a large set of raw landmark
images containing modern and historic landmark images and non-landmark images. The method then
processes these images to classify them as landmark and non-landmark images. The classification
performance highly depends on the number of candidate features of the landmark.
I MAGE S UBSET S ELECTION U SING G ABOR F ILTERS A ND N EURAL N ETWORKSijma
An automatic method for the selection of subsets of
images, both modern and historic, out of a set of
landmark large images collected from the Internet i
s presented in this paper. This selection depends o
n the
extraction of dominant features using Gabor filteri
ng. Features are selected carefully from a prelimin
ary
image set and fed into a neural network as a traini
ng data. The method collects a large set of raw lan
dmark
images containing modern and historic landmark imag
es and non-landmark images. The method then
processes these images to classify them as landmark
and non-landmark images. The classification
performance highly depends on the number of candida
te features of the landmark.
Classification of Multi-date Image using NDVI valuesijsrd.com
Advance Wide Field Sensor (AWiFS) of IRS P6 is an improved version of WiFS of IRS-1C/1D. AWiFS operates in four spectral bands identical to LISS III (Low-Imaging Sensing Satellite). Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements. These indexes can be used to prediction of classes of Remote Sensing (RS) images. In this paper, we will classify the AWiFS image on NDVI values of 5 different date's images (Captured by AWiFS satellite). For classifying images, we will use an algorithm called Sum of Squared Difference (SSD). It will compare the clustered image with the Reference image based on SSD and the best match on the basis of SSD algorithm, it will classify the image. It is simple 1 step process, which will be faster compared to the classical approach.
Matching algorithm performance analysis for autocalibration method of stereo ...TELKOMNIKA JOURNAL
Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.
RADAR Images are strongly preferred for analysis of geospatial information about earth surface to assesse envirmental conditions radar images are captured by different remote sensors and that images are combined together to get complementary information. To collect radar images SAR(Synthetic Aperture Radar) sensors are used which are active sensors and can gather information during day and night without affecting weather conditions. We have discussed DCT and DWT image fusion methods,which gives us more informative fused image simultaneously we have checked performance parameters among these two methods to get superior method from these two techniques
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.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Optimization of Macro Block Size for Adaptive Rood Pattern Search Block Match...IJERA Editor
In area of video compression, Motion Estimation is one of the most important modules and play an important role
to design and implementation of any the video encoder. It consumes more than 85% of video encoding time due to
searching of a candidate block in the search window of the reference frame. Various block matching methods have
been developed to minimize the search time. In this context, Adaptive Rood Pattern Search is one of the less
expensive block matching methods, which is widely acceptable for better Motion Estimation in video data
processing. In this paper we have proposed to optimize the macro block size used in adaptive rood pattern search
method for improvement in motion estimation.
An Analysis and Comparison of Quality Index Using Clustering Techniques for S...CSCJournals
In this paper, the proposed approach consists of mainly three important steps: preprocessing, gridding and segmentation of micro array images. Initially, the microarray image is preprocessed using filtering and morphological operators and it is given for gridding to fit a grid on the images using hill-climbing algorithm. Subsequently, the segmentation is carried out using the fuzzy c-means clustering. Initially the enhanced fuzzy c-means clustering algorithm (EFCMC) is implemented to effectively clustering the image whether the image may be affected by the noises or not. Then, the EFCM method was employed the real microarray images and noisy microarray images in order to investigate the efficiency of the segmentation. Finally, the segmentation efficiency of the proposed approach was compared with the various algorithms in terms of quality index and the obtained results ensures that the performance efficiency of the proposed algorithm was improved in term of quality index rather than other algorithms.
Multitude Regional Texture Extraction for Efficient Medical Image Segmentationinventionjournals
Image processing plays a major role in evaluation of images in many concerns. Manual interpretation of the image is time consuming process and it is susceptible to human errors. Computer assisted approaches for analyzing the images have increased in latest evolution of image processing. Also it has highlighted its performance more in the field of medical sciences. Many techniques are available for the involvement in processing of images, evaluation, extraction etc. The main goal of image segmentation is cluster pixeling the regions corresponding to individual surfaces, objects, or natural parts of objects and to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. The proposed method is to conquer segmentation and texture extraction with Regional and Multitude and techniques involved in it. Ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted brain segmentation and disease diagnosis applications.First for ultra sound we present region based segmentation.Homogeneous regions depends on image granularity features. Second a local threshold based multitude texture regional seed segmentation for medical image segmentation is proposed. Here extraction is done with dimensions comparable to the speckle size are to be extracted. The algorithm provides a less medical metrics awareness in a minimum user interaction environment. The shape and size of the growing regions depend on look up table entries.
A NOVEL PROBABILISTIC BASED IMAGE SEGMENTATION MODEL FOR REALTIME HUMAN ACTIV...sipij
Automatic human activity detection is one of the difficult tasks in image segmentation application due to
variations in size, type, shape and location of objects. In the traditional probabilistic graphical
segmentation models, intra and inter region segments may affect the overall segmentation accuracy. Also,
both directed and undirected graphical models such as Markov model, conditional random field have
limitations towards the human activity prediction and heterogeneous relationships. In this paper, we have
studied and proposed a natural solution for automatic human activity segmentation using the enhanced
probabilistic chain graphical model. This system has three main phases, namely activity pre-processing,
iterative threshold based image enhancement and chain graph segmentation algorithm. Experimental
results show that proposed system efficiently detects the human activities at different levels of the action
datasets.
1. A Singh Bhandari et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 9( Version 1), September 2014, pp.05-11
www.ijera.com 5 | P a g e
SAR Image Segmentation Based On Hybrid PSOGSA Optimization Algorithm Amandeep Kaur*, Charanjit Singh**, Amandeep Singh Bhandari*** *(Student, Department of Electronics and Communication Engg., Punjabi University, Patiala) ** (Assistant Professor, Department of Electronics and Communication Engg., Punjabi University, Patiala) *** (Assistant Professor, Department of Electronics and Communication Engg., Punjabi University, Patiala) ABSTRACT Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR) images is still a challenging problem. We proposed a fast SAR image segmentation method based on Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA). In this method, threshold estimation is regarded as a search procedure that examinations for an appropriate value in a continuous grayscale interval. Hence, PSO-GSA algorithm is familiarized to search for the optimal threshold. Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in terms of segmentation accuracy, segmentation time, and Thresholding.
I. INTRODUCTION
An image is an artifact that depicts visual perception similar in appearance to some physical object or a person. Images may be two-dimensional (2D), such as screen display, a photograph, etc., or three- dimensional (3D), such as a hologram or a statue. A digital image is a numeric representation of a two- dimensional image. An image can be defined as a two-dimensional function 푓 푥,푦 , where 푥 and 푦 are three-dimensional coordinates, and the amplitude off at any pair of coordinates 푥,푦 is called the intensity of the digital image at that point.
Image processing is a method of converting an image into digital form and performs some operations on it, to get an enhanced image or to extract some useful information from it. It is a form of signal processing in which the input is an image, like photograph frame, and the output may be either an image or a set of characteristics or parameters associated with the image.
Segmentation is a process that divides an image into its basic parts or objects. In general, independent segmentation is one of the toughest tasks in digital image processing. Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm.
RESEARCH ARTICLE OPEN ACCESS
2. A Singh Bhandari et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 9( Version 1), September 2014, pp.05-11
www.ijera.com 6 | P a g e
Figure 1: Fundamental steps in digital image processing.
Particle Swarm Optimization (PSO)
Particle Swarm Optimization (PSO) is one of the more recently developed evolutionary techniques, and it is based on a suitable model of social interaction between independent agents (particles) and it uses social knowledge in order to find the global maximum or minimum of a generic function. In the PSO the so called swarm intelligence (i.e. the experience accumulated during the evolution) is used to search the parameter space by controlling the trajectories of a set of particles according to a swarm- like set of rules. The position of each particle is used to compute the value of the function to be optimized. Particles are moved in the domain of the problem with variable speeds and every position they reach represents a particular configuration of the variables set, which is then evaluated in order to get a score [19].
Gravitational Search Algorithm (GSA)
In this section, we introduce our optimization algorithm based on the law of gravity [19]. In the proposed algorithm, agents are considered as objects and their performance is measured by their masses. All these objects attract each other by the gravity force, and this force causes a global movement of all objects towards the objects with heavier masses. Hence, masses cooperate using a direct form of communication, through gravitational force. The heavy masses which correspond to good solutions – move more slowly than lighter ones, this guarantees the exploitation step of the algorithm.
In GSA, each mass (agent) has four specifications: position, inertial mass, active gravitational mass, and passive gravitational mass. The position of the mass corresponds to a solution of the problem, and its gravitational and inertial masses are determined using a fitness function. In other words, each mass presents a solution, and the algorithm is navigated by properly adjusting the gravitational and inertia masses.
SAR Images
Ecological monitoring, earth-resource mapping, and military schemes require broad-area imaging at high resolutions. Many times the imagery must be developed in inclement weather or during night as well as day. Synthetic Aperture Radar (SAR) delivers such a capability. SAR systems take benefit of the long-range propagation characteristics of radar signals and the complex material processing capability of modern digital electronics to provide high resolution imagery. Synthetic aperture radar complements photographic and other optical imaging competences because of the minimum constraints on time-of-day and atmospheric circumstances and because of the unique responses of terrain and cultural targets to radar frequencies [20]. Synthetic aperture radar skill has provided terrain structural information to geologists for mineral examination, oil spill boundaries on water to environmentalists, sea state and ice hazard maps to navigators, and reconnaissance and targeting info to military processes.
II. LITERATURE SURVEY:
S. C. Zhu et al (1996), [12] presented a novel statistical and variational approach to image segmentation based on a new algorithm named region
Knowledge Base
Image restoration
Image enhancement
Image acquisition
Segmentation
Representation
& description
Object recognition
Color Image Processing
Wavelets and multi- resolution Processing
Morphological processing
Compression
3. A Singh Bhandari et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 9( Version 1), September 2014, pp.05-11
www.ijera.com 7 | P a g e
competition. This algorithm was derived by minimizing a generalized Bayes/MDL criterion using the variational principle. They provided theoretical analysis of region competition including accuracy of boundary location, criteria for initial conditions, and the relationship to edge detection using filters. A. Tsai et al (2001), [15] presented first address the problem of simultaneous image segmentation and smoothing by approaching the Mumford–Shah paradigm from a curve evolution perspective. Various implementations of this algorithm are introduced to increase its speed of convergence. This more general model leads us to a novel PDE-based approach for simultaneous image magnification, segmentation, and smoothing, thereby extending the traditional applications of the Mumford–Shah functional which only considers simultaneous segmentation and smoothing. W. Tao et al (2007), [18] developed a novel approach that provides effective and robust segmentation of color images. By incorporating the advantages of the mean shift (MS) segmentation and the normalized cut (Ncut) partitioning methods, the proposed method requires low computational complexity and is therefore very feasible for real- time image segmentation processing. Because the number of the segmented regions is much smaller than that of the image pixels, the proposed method allows a low-dimensional image clustering with significant reduction of the complexity compared to conventional graph-partitioning methods that are directly applied to the image pixels. The superiority of that method was examined and demonstrated through a large number of experiments using color natural scene images. S. Mirjalili et al (2010), [19] proposed a hybrid population-based algorithm (PSOGSA) is with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms strength. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard PSO and GSA.
M. Miao et.al. (2011), [20] proposed a fast SAR image segmentation method based on Artificial Bee Colony (ABC) algorithm using threshold estimation as a search procedure that searches for an appropriate value in a continuous grayscale interval.ABC algorithm is used to search for the optimal threshold. Experimental results indicate that the proposed method is superior to Genetic Algorithm (GA) based and Artificial Fish Swarm (AFS) based segmentation methods in terms of segmentation accuracy and segmentation time.
C. Liu et al(2013), [22] presented a novel variational framework for multiphase synthetic aperture radar (SAR)image segmentation based on the fuzzy region competition method. A new energy functional is proposed to integrate the Gamma model and the edge detector based on the ratio of exponentially weighted averages (ROEWA) operator within the optimization process.
III. PROPOSED WORK
In the proposed work, Segmentation subdivides an image into its constituent regions or objects. The level to which the subdivision is carried depends on the problem being solved. That is, segmentation should stop when the objects of interest in an application have been isolated. To compare the efficiency of our method with others, segmentation methods based on PSO-GSA algorithm are used to segment some typical images, covering a noise-free optical image, an optical image polluted by synthetic noise (composed of Gaussian noise with mean 0 and variance 0.01, speckle noise with variance 0.005, and salt and pepper noise with density 0.02), and a real SAR image. In this experiment, for PSO-GSA algorithm, the population size is 10, the fixed maximum number of iterations is 10, and the limit times for abandonment is 10, the lower and upper bounds are 0 and 255 respectively, because in grey scale each pixel value is between 0 to 255, there are 265 levels in grey scale images so we take the bounds between 0 to 255
PSO Algorithm
[Step 1] Initialization.
Initialize positions and associate velocity to all particles (potential solutions) in the population randomly in the D-dimension space.
[Step 2] Evolution:
Evaluate the fitness value of all particles.
[Step 3] Compute the personal best:
Compare the personal best (푝푏푒푠푡) of every particle with its current fitness value. If the current fitness value is better, then assign the current fitness value to 푝푏푒푠푡and assign the current coordinates to 푝푏푒푠푡coordinates.
[Step 4] Determine the current best fitness value:
Determine the current best fitness value in the whole population and its coordinates. If the current best fitness value is better than global best (푔푏푒푠푡), then assign the current best fitness value to 푔푏푒푠푡and assign the current coordinates to 푔푏푒푠푡coordinates.
4. A Singh Bhandari et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 9( Version 1), September 2014, pp.05-11
www.ijera.com 8 | P a g e
[Step 5] Calculate accelerations of the agents:
Update velocity (푉푖푑 푡) and position (푋푖푑 푡) of the d- th dimension of the i-th particle using the following equations: 푉푖푑 푡=휔 푡 ∗푉푖푑 푡−1+푐1 푡 ×푟푎푛푑1푖푑 푡×(푝푏푒푠푡푖푑 푡−1−푋푖푑 푡−1+푐2 푡 × 1−푟푎푛푑1푖푑 푡 ×(푔푏푒푠푡푑푡 −1−푋푖푑 푡−1) 푉푖푑 푡>푉푚푎푥 푑표푟푉푖푑 푡<푉푚푖푛 푑,푡푕푒푛푉푖푑 푡=푈(푉푚푖푛 푑,푈푚푎푥 푑) 푋푖푑 푡=푟푎푛푑2푖푑 푡×푋푖푑 푡−1+ 1−푟푎푛푑2푖푑 푡 ×푉푖푑 푡 푐1 푡 , 푐2 푡 = time-varying acceleration coefficients with 푐1 푡 decreasing linearly from 2.5 to 0.5 and 푐2 푡 increasing linearly from 0.5 to 2.5 over the full range of the search, and w(t) = time-varying inertia weight changing randomly between U(0:4 ; 0:9) with iterations, 푟푎푛푑1, 푟푎푛푑2, are uniform random numbers between 0 and 1, having different values in different dimension, t is the current generation number. The above equation has been introduced to clamp the velocity along each dimension to uniformly distributed random value between 푉푚푖푛 푑and 푉푚푎푥 푑if they try to cross the desired domain of interest. These clipping techniques are sometimes necessary to prevent particles from explosion. The maximum velocity is set to the upper limit of the dynamic range of the search (푉푚푎푥 푑=푋푚푎푥 푑) and the minimum velocity (푉푚푖푛 푑) is set to (푋푚푖푛 푑). However, position-clipping technique is avoided in modified PSO algorithm. Moreover, the fitness function evaluations of errant particles (positions outside the domain of interest) are skipped to improve the speed of the algorithm.
[Step 6] Repeat from Steps 2 to 5 until iterations reaches their maximum limit:
Repeat Steps 2 to 5 until a stop criterion is satisfied or a pre-specified number of iteration is completed, usually when there is no further update of best fitness value.
GSA Algorithm
[Step 1] Initialize of the agents.
Initialize the positions of the 푁 number of agents randomly within the given search interval as below: 푋푖=푥푖 1,…..푥푖 푑,……푥푖 푛 푓표푟 푖=1,2,…….,푁 Where,푥푖 푑 represents the positions of the i-th agent in the d-th dimension and 푁 is the space dimension.
[Step 2] Fitness evolution and best fitness computation for each agents:
Perform the fitness evolution for all agents at each iteration and also compute the best and worst fitness at each iteration defined as below (for minimization problems): 푏푒푠푡 푡 =푚푖푛 푗휖 1,…,푁 푓푖푡푗(푡) 푤표푟푠푡 푡 =푚푎푥 푗휖 1,..,푁 푓푖푡푗(푡) Where, 푓푖푡푗(푡) represents the fitness of the j-th agent at 푖푡푒푟푎푡푖표푛 푡,푏푒푠푡(푡) 푎푛푑 푤표푟푠푡(푡) represents the best and worst fitness at generation 푡.
[Step 3] Compute gravitational constant G:
Compute gravitational constant G at iteration t using the following equation: 퐺 푡 =퐺표 (−훼푡/푇) In this problem, 퐺0 is set to 100, 훼 푖s set to 20 and 푇 is the total number of iterations.
[Step 4] Calculate the mass of the agents:
Calculate gravitational and inertia masses for each agents at iteration 푡 by the following equations: 푀푎푖=푀푝푖=푀푖푖=푀푖, 푖=1,2,…,푁 푚푖 푡 = 푓푖푡푖 푡 −푤표푟푠푡푖 푡 푏푒푠푡 푡 −푤표푟푠푡(푡) 푀푖 푡 = 푚푖 푡 푚푗(푡)푁푗 =1 Where, 푀푎푖 is the active gravitational mass of the i-th agent,푀푝푖 is the passive gravitational mass of the i-th agent,푀푖푖 is the inertia mass of the i-th agent.
[Step 5] Calculate accelerations of the agents:
Compute the acceleration of the i-th agents at iteration t as below: 푎푖 푑 푡 = 퐹푖 푑 푡 푀푖푖(푡) Where, 퐹푖 푑(푡) 푖s the total force acting on i-th agent calculated as, 퐹푖 푑 푡 = 푟푎푛푑푗퐹푖푗푑 (푡) 푗휖퐾푏푒푠푡,푗≠푖 퐾푏푒푠푡 is the set of first 퐾 agents with the best fitness value and biggest mass.퐾푏푒푠푡 is computed in such a manner that it decreases linearly with time and at last iteration the value of 퐾푏푒푠푡 becomes 2% of the initial number of agents.퐹푖푗푑 (푡) is the force acting on agent 'i' from agent 'j' at d-th dimension and t-th iteration is computed as below: 퐹푖푗푑 푡 = 퐺 푡 푀푝푖(푡)×푀푎푗(푡) 푅푖푗 푡 +휀 푥푗 푑 푡 −푥푖 푑(푡) Where,푅푖푗 푡 is the Euclidian distance between two agents 'i' and 'j' at iteration t and 퐺(푡) is the computed gravitational constant at the same iteration. 휀 is a small constant
[Step 6] Update velocity and positions of the agents:
Compute velocity and the position of the agents at next iteration (t + 1) using the following equations:
5. A Singh Bhandari et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 9( Version 1), September 2014, pp.05-11
www.ijera.com 9 | P a g e
푣푖
푑 푡 + 1 = 푟푎푛푑푖 × 푣푖
푑 푡 + 푎푖
푑 (푡)
푥푖
푑 푡 = 푥푖
푑 푡 + 푣푖
푑 푡 + 1
[Step 7] Repeat from Steps 2 to 6 until iterations
reaches their maximum limit.
Return the best fitness computed at final iteration
as a global fitness of the problem and the positions of
the corresponding agent at specified dimensions as
the global solution of that problem.
IV. SIMULATION RESULTS
Figure 2: Original Image Figure 3 : Segmented Image
We take a general purpose image shown in figure 2
for comparison to base paper images and calculate
the threshold value (167), Time (0.119), and fitness
of an image, and compare the results with the
previous work taken in the Table below show the
comparison of ant colony, Genetic, Artificial Fish
Swarm algorithms. Image Fitness is 1.0 × 103
*2.5775.
Table 1: Comparison over different Algorithms using image (figure 2)
Algorithms Fitness Function Threshold Time (s)
PSO-GSA Variance Grey entropy 167 0.119
ACS Improved two-dimensional grey entropy 205 5.821
GA Two-dimensional entropy 207 14.391
GA Two-dimensional grey entropy 206 17.640
AFS Two-dimensional entropy 187 12.015
Figure 4: Original Image Figure 5: Segmented Image
We take a general purpose image shown in
figure 4 for comparison to base paper images and
calculate the threshold value (165), Time (0.123), and
fitness of an image, and compare the results with the
6. A Singh Bhandari et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 9( Version 1), September 2014, pp.05-11
www.ijera.com 10 | P a g e
previous work taken in the Table below show the comparison of ant colony, Genetic, Artificial Fish Swarm algorithms. Image Fitness is 1.0×1003 *2.4103. Table 2: Comparison over different Algorithms using image (figure 4)
Algorithms
Fitness Function
Threshold
Time (s)
PSO-GSA
Variance Grey entropy
165
0.123
ACS
Improved two-dimensional grey entropy
204
6.669
GA
Two-dimensional entropy
163
15.358
GA
Two-dimensional grey entropy
207
19.152
AFS
Two-dimensional entropy
162
12.546
Figure 6:Original Image# Case 3
Figure 7: Segmented Image# Case 3
We take an SAR image 5.4 and calculate the threshold value (61), Time (0.115), and fitness of an image, and compare the results with the previous work taken in the Table below show the comparison of ant colony, Genetic, Artificial Fish Swarm algorithms. Image Fitness is 883.960. Table 3: Comparison over different Algorithms using image (figure 6)
Algorithms
Fitness Function
Threshold
Time (s)
PSO-GSA
Variance Grey entropy
61
0.115
ACS
Improved two-dimensional grey entropy
95
4.835
GA
Two-dimensional entropy
131
13.460
GA
Two-dimensional grey entropy
94
17.740
AFS
Two-dimensional entropy
62
6.441
V. CONCLUSION
We proposed a fast segmentation method on SAR images. The technique regards threshold estimation as a search process and employs PSO- GSA algorithm to optimize it. In order to provide PSO-GSA algorithm with an efficient fitness function, we integrate the concept of grey number in Grey theory, maximum conditional entropy to get improved two-dimensional grey entropy. In essence, the fast segmentation speed of our method owes to PSO-GSA algorithm, which has an outstanding convergence performance. On the other hand, the segmentation quality of our method is benefit from the improved two-dimensional grey entropy, for the fact that noise almost completely disappears. Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in terms of segmentation accuracy, segmentation time, and Thresholding.
REFERENCES [1] R. Wilson and M. Spann, Image segmentation and uncertainty, John Wiley & Sons, Inc., 1988. [2] A. R. Weeks, Fundamentals of electronic image processing, SPIE Optical Engineering Press Bellingham, 1996. [3] E. R. Dougherty, R. A. Lotufo and T. I. S. for Optical Engineering SPIE, Hands-on morphological image processing, vol. 71, SPIE press Bellingham, 2003. [4] Y.-J. Zhang, Advances in image and video segmentation, IGI Global, 2006.
[5] L. Zhang and Q. Ji, "A Bayesian Network Model for Automatic and Interactive Image Segmentation," Image Processing, IEEE
7. A Singh Bhandari et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 9( Version 1), September 2014, pp.05-11
www.ijera.com 11 | P a g e
Transactions on, vol. 20, no. 9, pp. 2582- 2593, Sept 2011. [6] J. Tang, "A color image segmentation algorithm based on region growing," in Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, 2010. [7] W. Tao, H. Jin and Y. Zhang, "Color Image Segmentation Based on Mean Shift and Normalized Cuts," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 37, no. 5, pp. 1382- 1389, Oct 2007. [8] S. Wang and J. Siskind, "Image segmentation with ratio cut," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 25, no. 6, pp. 675-690, June 2003. [9] H. Zhang, Q. Zhu and X. feng Guan, "Probe into Image Segmentation Based on Sobel Operator and Maximum Entropy Algorithm," in Computer Science Service System (CSSS), 2012 International Conference on, 2012. [10] A. Lahouhou, E. Viennet and A. Beghdadi, "Combining and selecting indicators for image quality assesment," in Information Technology Interfaces, 2009. ITI '09. Proceedings of the ITI 2009 31st International Conference on, 2009. [11] S. Usai, "A least squares database approach for SAR interferometric data," Geoscience and Remote Sensing, IEEE Transactions on, vol. 41, no. 4, pp. 753-760, 2003. [12] S. C. Zhu and A. Yuille, "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 18, no. 9, pp. 884-900, 1996. [13] K. Haris, S. N. Efstratiadis, N. Maglaveras and A. K. Katsaggelos, "Hybrid image segmentation using watersheds and fast region merging," Image Processing, IEEE Transactions on, vol. 7, no. 12, pp. 1684- 1699, 1998. [14] J. Shi and J. Malik, "Normalized cuts and image segmentation," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, 2000. [15] A. Tsai, A. Yezzi Jr and A. S. Willsky, "Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification," Image Processing, IEEE Transactions on, vol. 10, no. 8, pp. 1169- 1186, 2001.
[16] S. Wang and J. M. Siskind, "Image segmentation with ratio cut," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 25, no. 6, pp. 675-690, 2003. [17] Y.-C. Lin, Y.-P. Tsai, Y.-P. Hung and Z.-C. Shih, "Comparison between immersion- based and toboggan-based watershed image segmentation," Image Processing, IEEE Transactions on, vol. 15, no. 3, pp. 632-640, 2006. [18] W. Tao, H. Jin and Y. Zhang, "Color image segmentation based on mean shift and normalized cuts," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 37, no. 5, pp. 1382- 1389, 2007. [19] S. Mirjalili and S. Z. M. Hashim, "A new hybrid PSOGSA algorithm for function optimization," in Computer and information application (ICCIA), 2010 international conference on, 2010. [20] M. Ma, J. Liang, M. Guo, Y. Fan and Y. Yin, "SAR image segmentation based on Artificial Bee Colony algorithm," Applied Soft Computing, vol. 11, no. 8, pp. 5205- 5214, 2011. [21] H. Zhang, Q. Zhu and X.-f. Guan, "Probe into Image Segmentation Based on Sobel Operator and Maximum Entropy Algorithm," in Computer Science & Service System (CSSS), 2012 International Conference on, 2012. [22] C. Liu, Y. Zheng, Z. Pan, J. Duan and G. Wang, "SAR image segmentation based on fuzzy region competition method and Gamma model," Journal of Software, vol. 8, no. 1, pp. 228-235, 2013.