EDGE DETECTION FOR IMAGE USING CELLULAR AUTOMATA

In digital image processing, understanding the scene is one of the most important and challenging tasks. Along with many features of an image like texture, color, line, point etc. edges are most vital feature that carries the information about the structure and overall geometry of objects the image is composed of. Edge detection is based on finding meaningful discontinuities in gray levels. This brief study is aimed towards exploring different edge detection techniques, the theories behind them and distinguishing their efficiency in finding suitable, meaningful edges in digital medical images.

AEU - International Journal of Electronics and Communications, 2015

In this paper, we propose a cellular edge detection (CED) algorithm which utilizes cellular automata (CA) and cellular learning automata (CLA). The CED algorithm is an adaptive, intelligent and learnable algorithm for edge detection of binary and grayscale images. Here, we introduce a new CA local rule with adaptive neighborhood type to produce the edge map of image as opposed to CA with fixed neighborhood. The proposed adaptive algorithm uses the von Neumann and Moore neighborhood types. Experimental results demonstrate that the CED algorithm has superior accuracy and performance in contrast to other edge detection methods such as Sobel, Prewitt, Robert, LoG and Canny operators. Moreover, the CED algorithm loses fewer details while extracting image edges compare to other edge detection methods.

2017

Cellular automata can be significantly applied in image processing tasks. In this paper, a novel method to train two dimensional cellular automata for detection of edges in digital images has been proposed and experiments have been carried out for the same. Training of two dimensional cellular automata means selecting the optimum rule set from the given set of rules to perform a particular task. In order to train the cellular automata first, the size of rule set is reduced on the basis of symmetry. Then the sequential floating forward search method for rule selection is used to select the best rule set for edge detection. The misclassification error has been used as an objective function to train the cellular automata for edge detection. The whole experiment has been divided in two parts. First the training was performed for binary images then it is performed for gray scale images. A novel method of thresholding the image by Otsu’s algorithm and then applying the cellular automata r...

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY, 2014

Edges characterize boundaries and are therefore considered for prime importance in image processing. Edge detection filters out useless data, noise and frequencies while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection methods. In this paper the comparative analysis of various Image Edge Detection methods is presented. The evidence for the best detector type is judged by studying the edge maps relative to each other through statistical evaluation. Upon this evaluation, an edge detection method can be employed to characterize edges to represent the image for further analysis and implementation. It has been shown that the Canny's edge detection algorithm performs better than all these operators under almost all scenarios. eywor

— Edge is the boundary between an object and the background, and identifies the boundary between overlapping and non-over lapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Here fuzzy logic based image processing is used for accurate and noise free edge detection and Cellular Learning Automata (CLA) is used for enhance the previously-detected edges with the help of the repeatable and neighborhood-considering nature of CLA. The different result of edge detection technique is compared with fuzzy edge detected and resulting edge is enhanced using CLA. In this paper, all the algorithms and result are prepared in MATLAB.

In the field of image processing, edge detection is an important step for extracting relevant and meaningful information from digital images. The main goal of edge detection techniques is to obtain and detect thin edges of the objects present in the image, so that the result is more suitable for further processing and analysis such as boundary detection, image segmentation, motion detection/estimation, texture analysis, object identification, feature detection, implementing various transformations and so on. We tested six edge detection algorithms that use different methods for detecting edges and compared their results under a variety of situations to determine a generally preferable technique under different sets of conditions. This data could then be used to create a multi-edge-detector system, which analyses the scene and runs the edge detector best suited for the current set of data. For each of these edge detectors we considered two different ways of implementation, the one using intensity only and the other coupling to it, the colour information. We also considered one additional edge detector which takes a different philosophy to edge detection. Rather than trying to find the ideal edge detector to apply to traditional photographs, it would be more efficient to merely change the method of photography to one which is more conducive to edge detection. It makes use of a camera that takes multiple images in rapid succession under different lighting conditions. It has been observed that the Canny"s edge detection algorithm performs better than all these operators under almost all scenarios. Evaluation of the images showed that under noisy conditions Canny, LoG(Laplacian of Gaussian), Robert, Prewitt, Sobel exhibit better performance, respectively. It has been observed that Canny"s edge detection algorithm is computationally more expensive compared to LoG(Laplacian of Gaussian), Sobel, Prewitt and Robert"s cross operator.

International Journal of Soft Computing and Engineering, 2019

Tumor growth or, growth of cancerous cells is a big challenge in today’s medical word. When dealing with human life, the detection of tumors through computers has to be highly accurate. Thus we require the assistance of computer in medical examinations, so that we will get very low rate of false cases. Brain tumor, in today’s world, is seen as most threatening and life taking disease. In order to detect brain tumor more accurately in lesser time, many techniques have already been proposed using image segmentation and edge detection. In our paper we propose a technique which is more efficient to detect brain tumor where edge detection through cellular automata have been used from Magnetic Resonance Imaging (MRI) scan images. It processes these images, and determines the area affected by using segmentation and edge detection with cellular automata. Simulated work is completed with the help of Simulink in MATLAB. Regarding this particular topic there are many studies, however our propo...

This paper depicts three methods for edge detection. The first method is one of the promising method for edge detection based on canny edge detection. In the second method neural network has been used for edge detection. Third method is inspired by the behavior of biological receptive fields and the human visual system. A network model based on spiking neuron is proposed to detect edges in visual image. SNN is able to perform edge detection within a processing time which is consistent with human visual system. SNNs improve the representation capacity and the processing abilities of neural networks. Successful computer simulation results are obtained.

Edge detection is the most common preprocessing step in many image processing algorithms such as image enhancement, image segmentation, tracking and image/video coding. Edge detection is the name for a set of mathematical methods that aims at identifying points in a digital image at which the image brightness changes sharply and it has discontinuities. The points in which image brightness changes sharply are typically organized into a set of curved line segments termed as edges. The step detection is the problem of finding discontinuities in 1D signals and change detection is the problem of finding signal discontinuities over time. Edge detection is a fundamental tool particularly in the areas of feature detection and feature extraction. There are different algorithm based edge detectors like Canny, Sobel, Iverson and they depend on the input parameters. The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world. In an image formation model, discontinuities in image brightness are likely to correspond to four assumptions: discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. Mamdani method is widely accepted for capturing expert knowledge. It allows us to describe the expertise in more intuitive, more human-like manner. This article discusses the recent development of edge detection algorithms available in image processing fields.

In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional for segmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one closed boundary per actual region in the image. KEYWORDS Watershed, difference in strength map, K-means, edge detection, image segmentation. Active counters, Level set method, Markov Random Field