A fuzzy rule-based colour image segmentation algorithm

Most fuzzy rule-based image segmentation techniques to date have been primarily developed for gray level images. In this paper, a new algorithm called fuzzy rule-based colour image segmentation (FRCIS) is proposed by extending the generic fuzzy rule-based image segmentation (GFFUS) algorithm G.C. Karmakar, L.S. Dooley [2002] and integrating a novel algorithm for averaging hue angles. Qualitative and quantitative analysis of the performance of FRCIS is examined and contrasted with the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms for both the hue-saturation-value (HSV) and RGB colour models. Overall, FRCIS provides considerable improvement for many different image types

An implementation of novel genetic based clustering algorithm for color image segmentation

The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application

Colour Text Segmentation in Web Images Based on Human Perception

There is a significant need to extract and analyse the text in images on Web documents, for effective indexing, semantic analysis and even presentation by non-visual means (e.g., audio). This paper argues that the challenging segmentation stage for such images benefits from a human perspective of colour perception in preference to RGB colour space analysis. The proposed approach enables the segmentation of text in complex situations such as in the presence of varying colour and texture (characters and background). More precisely, characters are segmented as distinct regions with separate chromaticity and/or lightness by performing a layer decomposition of the image. The method described here is a result of the authors’ systematic approach to approximate the human colour perception characteristics for the identification of character regions. In this instance, the image is decomposed by performing histogram analysis of Hue and Lightness in the HLS colour space and merging using information on human discrimination of wavelength and luminance

Segmenting colour images on the basis of a fuzzy hierarchical approach

In this paper we deal with two problems related to imprecision in colour image segmentation processes: to decide whether a set of pixels verify the property "to be homogeneously coloured", and to represent the set of possible segmentations of an image at different precision levels. In order to solve the first problem we introduce a measure of distance between colours in the CIE L*a*b* space, that allows us to measure the degree of homogeneity of two pixels p and q on the basis of the maximum distance between the colours of consecutive pairs of pixels in any path linking p and q . Since homogeneity is a matter of degree, we define a (fuzzy) segmentation of an image as a set of fuzzy regions, each of them being a fuzzy subset of pixels, that we obtain by using a region growing technique. The membership degree of each pixel to each region is calculated on the basis of our homogeneity measure. The second problem is solved by introducing a fuzzy similarity relation between the fuzzy regions in this initial segmentation. The different α-cuts of the similarity relation define the set of precision levels, from which a nested hierarchy of fuzzy segmentations is finally obtained

Segmentation floue d'images couleur

- Nous proposons dans cet article un algorithme permettant d'obtenir des régions floues à partir d'images monochromes ou couleur. Les régions floues se chevauchent, ont des coeurs déterminés automatiquement dans les minimums locaux des normes du gradient ; les degrés d'appartenance, fondés sur la distance topographique décroissent fortement dès qu'un contour est rencontré. Outre la segmentation nette, qu'on peut obtenir en « defuzzifiant » la segmentation floue, la principale application de cet algorithme réside dans les régions floues elles-même qui pourront être employées pour de la reconnaissance de formes ou de l'indexation d'images

Efficient color image segmentation using Multi-Elitist Particle Swarm Optimization Algorithm

Summary Image color classification and Image segmentation using comprehensive learning particle swarm optimization (CLPSO) technique was developed by Parag Puranik, Dr. P.R. Bajaj, Prof. P.M. Palsodkar[1],The aim was to produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. In this paper we propose MultiElitist Particle Swarm Optimization Algorithm (MEPSO) for Image cluster classification and segmentation. The proposed method is based on a modified version of classical Particle Swarm Optimization (PSO) algorithm, known as the MultiElitist PSO (MEPSO) model. It also employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original input space into homogeneous groups in a transformed high-dimensional feature space. A new particle representation scheme has been adopted for selecting the optimal number of clusters from several possible choices. The MEPSO is used to find optimal fuzzy rules and membership functions. The best fuzzy rule is selected for image segmentation.MEPSO give best rule set than standard PSO

Colour Texture analysis

This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using low-level image descriptors (such as the Local Binary Patterns (LBP)) and colour information by using colour space partitioning. The main advantage of this approach is the analysis of the textured images at a micro-level using the local distribution of the LBP values, and in the colour domain by analysing the local colour distribution obtained after colour segmentation. The use of the colour and texture information separately has proven to be inappropriate for natural images as they are generally heterogeneous with respect to colour and texture characteristics. Thus, the main problem is to use the colour and texture information in a joint descriptor that can adapt to the local properties of the image under analysis. We will review existing approaches to colour and texture analysis as well as illustrating how our approach can be successfully applied to a range of applications including the segmentation of natural images, medical imaging and product inspection