Interpretation of image contents is one of the objectives in computer vision specifically in image processing. In this era it has received much awareness of researchers. In image interpretation the partition of the image into object and background is a severe step. Segmentation separates an image into its component regions or objects. Image segmentation t needs to segment the object from the background to read the image properly and identify the content of the image carefully. In this context, edge detection is a fundamental tool for image segmentation. In this paper an attempt is made to study the performance of most commonly used edge detection techniques for image segmentation and also the comparison of these techniques is carried out with an experiment by using MATLAB software.

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Edge detection techniques for image segmentation

Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Segmentation is an essential step in image processing since it conditions the quality of the resulting interpretation. Lots of approaches have been proposed and a dense literature is available In order to extract as much information as possible from an environment, multicomponent images can be used. In the last decade, multicomponent images segmentation has received a great deal of attention for soft computing applications because it significantly improves the discrimination and the recognition capabilities compared with gray-level image segmentation methods. In this paper, the main aim is to understand the soft computing approach to image segmentation.

Image Segmentation - A Survey of Soft Computing Approaches

http://www4.comp.polyu.edu.hk/~cslzhang/paper/PR_2012_review.pdf

A survey of graph theoretical approaches to image segmentation

Survey on Image Segmentation Techniques

In this paper the important problem is to understand the fundamental concepts of various filters and apply these filters in identifying a shark fish type which is taken as a case study. In this paper the edge detection techniques are taken for consideration. The software is implemented using MATLAB. The main two operators in image processing are Gradient and Laplacian operators. The case study deals with observation of Shark Fish Classification through Image Processing using the various filters which are mainly gradient based Roberts, Sobel and Prewitt edge detection operators, Laplacian based edge detector and Canny edge detector. The advantages and disadvantages of these filters are comprehensively dealt in this study.

A Comparison of various Edge Detection Techniques used in Image Processing

Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Edge is a boundary between two homogeneous regions. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. In this paper, the main aim is to survey the theory of edge detection for image segmentation using soft computing approach based on the Fuzzy logic, Genetic Algorithm and Neural Network.

Edge Detection Techniques for Image Segmentation - A Survey of Soft Computing Approaches

Medical image processing plays an essential role in providing information in wide area for such advanced images. Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. MRI of the brain is an invaluable tool to help physicians to diagnose and treat various brain diseases including stroke, cancer, and epilepsy. The specific information to evaluate the diseases. Histogram equalization is one of the important steps in image enhancement technique for MRI. There are several methods of image enhancement and each of them is needed for a different type of analysis. In this paper study and compare different Techniques like Global Histogram Equalization (GHE), Local histogram equalization (LHE), Brightness preserving Dynamic Histogram equalization (BPDHE) and Adaptive Histogram Equalization (AHE) using different objective quality measures for MRI brain image Enhancement.

Histogram Equalization for Image Enhancement Using MRI Brain Images

Genetic Algorithm is an optimization solver, which does an analogy to Darwin evolution by combining mutation, crossover and selection step. One of the biggest advantages of Genetic Algorithm is its ability to find a global optimum. The X-ray data set, which consists of an image and its expected edge features, is used for training by the GA. Image edge detection refers to the extraction of the edges in a digital image. An edge is a boundary between the object and its background. Edge detection is most common approach to detect discontinuity in an image.  Edge detection is a process to identify points in an image where discontinuities or sharp changes in intensity occur. This process is crucial to understanding the content of an image and has its applications in image analysis and machine vision. Edge detection is usually applied in initial stages of computer vision applications. In this paper, the main aim is to study the edge detection method for Dental X-ray image segmentation based on a genetic algorithm approach.

Genetic Algorithm Approach to Edge Detection for Dental X-ray Image Segmentation

Neural Networks are based on the parallel architecture and inspired from human brains. Neural networks are a form of multiprocessor computer system, with simple processing elements, a high degree of interconnection, simple scalar messages and adaptive interaction between elements. One such application is image compression. Image compression is a process which minimizes the size of an image file without degrading the quality of the image to an unacceptable level. It also reduces the time required for images to be sent over the internet or downloaded from web pages. This paper proposes an Improved Backpropagation Neural Network Technique, for lossless image compression. The system also proves that the improved Backpropagation Neural Network Technique works better than the existing Huffman Coding Technique for lossless image compression by considering X-Ray images based on three metrics such as compression ratio, transmission time and compression performance. Experimental results are presented and compared.

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N. Senthilkumaran

Papers

Image Segmentation - A Survey of Soft Computing Approaches

Edge Detection Techniques for Image Segmentation - A Survey of Soft Computing Approaches

Histogram Equalization for Image Enhancement Using MRI Brain Images

Genetic Algorithm Approach to Edge Detection for Dental X-ray Image Segmentation

Neural Network Technique for Lossless Image Compression Using X-Ray Images