The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentat...

Edge detection has been a long standing topic in image processing, generating hundreds of papers and algorithms over the last 50 years. Likewise, the topic has had a fascination for researchers in cellular automata, who have also developed a variety of solutions, particularly over the last ten years. CA based edge detection has potential benefits over traditional approaches since it is computat...

Edge detection process has a widespread usage in computer vision applications. But it has a different output when its input image changes from color to grayscale. This changeability of results make us add modification on edge detection process procedures to correctly detect all those edges in color images that can’t be detected in gray ones. This research reviews the proposed solution of Dutta ...

Cellular Automata (CA) are common and most simple models of parallel computations. Edge detection is one of the crucial task in image processing, especially in processing biological and medical images. CA can be successfully applied in image processing. This paper presents a new method for edge detection of binary images based on two dimensional twenty five neighborhood cellular automata. The m...

Synthetic aperture radar (SAR) images due to the usage of coherent imaging systems are affected by speckle. So lots of despeckling filters have been introduced up to now to suppress the speckle. Hence, objective and subjective evaluation of the denoised SAR images becomes a necessity. Thereby lots of objective evaluating estimators are introduced to evaluate the performance of despeckling filte...

In this Paper an efficient method of FPGA based design and implementation of area efficient Sobel Edge detection filter is presented using a combination of hardware and software components. The FPGA provides the necessary hardware for image processing algorithms with flexibility to support Sobel edge detection algorithm. A pipelined method is used to implement the edge detection filter. This ap...

Gradient calculation and edge detection are well-known problems in image processing and the fundament for many approaches for line detection, segmentation, contour extraction, or model fitting. A large variety of algorithms for edge detection already exists but strong image noise is still a challenge. Especially in automatic surveillance and reconnaissance applications with visual-optical, infr...

Digital image processing is a subset of the electronic domain, wherein the image is converted to an array of small integers, called pixels, representing a physical quantity. Edges characterize boundaries and edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. The problem of edge detection although it is funda...

Clustering is the task of grouping a set of objects in such a way that objects are more similar to each other than those in the other groups. Various clustering algorithms were developed, but it ignores the spatial relationship between pixel values then noise can be added to the image and it does not provide edge detection accuracy. Fuzzy local information C-means is the best image clustering m...

Image preprocessing and image segmentation are two important and broad research areas. Image denoising is one of the research fields in the research area of image preprocessing which is used to remove noise from the images. This paper proposed a method to identify the best filter and best edge detection algorithm for digital x-ray bone images. Comparisons of various filters such as Mean, Median...