An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consist of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsup...

Soft computing can be used to build hybrid intelligent systems for achieving different goals in real-world applications. Soft Computing techniques include, at the moment, fuzzy logic, neural networks, genetic algorithms, chaos theory methods, and similar techniques that have been proposed in recent years. Each of these techniques has advantages and disadvantages, and several real-world problems...

one of the most fundamental features of digital image and the basic steps in image processing, analysis, pattern recognition and computer vision is the edge of an image where the preciseness and reliability of its results will affect directly on the comprehension machine system made objective world. several edge detectors have been developed in the past decades, although no single edge detector...

Image thresholding and edge detection are crucial in image processing and understanding. In this chapter, the authors propose a hierarchical multilevel image thresholding method for edge information extraction based on the maximum fuzzy entropy principle. In order to realize multilevel thresholding, a tree structure is used to express the histogram of an image. In each level of the tree structu...

2. Obtain the gray-level gradient magnitude. 3. Derive the threshold surface by deforming the original image gray-level surface. 4. The threshold surface interpolation. 5. Segmentation based on the threshold surface. However, incomplete cells are still not detected reliably with this method. Double counting is sometimes occurred. In this paper, a Fuzzy Edge Detection Method is proposed. It is b...

in this paper, we present an edge detection method based on wavelet transform and hessian matrix of image at each pixel. many methods which based on wavelet transform, use wavelet transform to approximate the gradient of image and detect edges by searching the modulus maximum of gradient vectors. in our scheme, we use wavelet transform to approximate hessian matrix of image at each pixel, too. ...

Vehicle Class is an important parameter in road traffic management. With the help of vehicle classification the computation of percentage of state-aid streets, highways becomes simpler and it is also used in automated toll bridges. In this paper we classify the vehicles into three main broad categories: A) Light vehicle B) Medium Vehicle C) Heavy Vehicle. Firstly, the image pre-processing is do...

A binary image is a digital image that has just two possible values destined for each pixel. In general two colors are used for a binary image i. e. black and white. Binarization is one of the most important pre-processing step which consists to divide foreground and background of document images. Image binarization is the method of division of pixel values into double collections, black as for...

This study presents a novel method to detect edges that clusters, thresholds, and then detects edges of tumour/ hemorrhage region using intuitionistic fuzzy set theory. Clustering segments image into several clusters and histogram thresholding eliminates unwanted clusters that are not related to tumour/hemorrhage region. Finally, image is edge detected, where a clear boundary is obtained. Propo...

This paper is to introduce a multistage vision system to detect relevant information for road driving. The system applies fuzzy theory in order to handle less information per image that classical methods use to do. The image processing is based on the fact that the image is splitted up into six horizontal bands and consists of three basic stages: local, regional and global analysis. Firstly, pa...