A. K. Ray

Investigations on fuzzy thresholding based on fuzzy clustering

Thresholding, the problem of pixel classification is attempted here using fuzzy clustering algorithms. The segmented regions are fuzzy subsets, with soft partitions characterizing the region boundaries. The validity of the assumptions and thresholding schemes are investigated in the presence of distinct region proportions. The hard k means and fuzzy c means algorithms have been found useful when object and background regions are well balanced. Fuzzy thresholding is also formulated as extraction of normal densities to provide optimal partitions. Regional imbalances in gray distributions are taken care of in region normalized histograms.

Color image segmentation: Rough-set theoretic approach

A new color image segmentation algorithm using the concept of histon, based on Rough-set theory, is presented in this paper. The histon is an encrustation of histogram such that the elements in the histon are the set of all the pixels that can be classified as possibly belonging to the same segment. In rough-set theoretic sense, the histogram correlates with the lower approximation and the histon correlates with upper approximation. The roughness measure at every intensity level is calculated and then a thresholding method is applied for image segmentation. The proposed approach is compared with the histogram-based approach and the histon based approach. The experimental results demonstrate that the proposed approach yields better segmentation.

A new measure using intuitionistic fuzzy set theory and its application to edge detection

In this paper, a new attempt has been made using Attanassovs intuitionistic fuzzy set theory for image edge detection. Intuitionistic fuzzy set takes into account the uncertainty in assignment of membership degree known as hesitation degree. Also a new distance measure, called intuitionistic fuzzy divergence, has been proposed. With this proposed distance measure, edge detection is carried out, and the results are found better with respect to the previous methods.

Texture classification using a novel, soft-set theory based classification algorithm

In this paper, we have presented a new algorithm for classification of the natural textures. The proposed classification algorithm is based on the notions of soft set theory. The soft-set theory was proposed by D. Molodtsov which deals with the uncertainties. The choice of convenient parameterization strategies such as real numbers, functions, and mappings makes soft-set theory very convenient and practicable for decision making applications. This has motivated us to use soft set theory for classification of the textures. The proposed algorithm has very low computational complexity when compared with Bayes classification technique and also yields very good classification accuracy. For feature extraction, the textures are decomposed using standard dyadic wavelets. The feature vector is obtained by calculating averaged L1-norm energy of each decomposed channel. The database consists of 25 texture classes selected from Bordatz texture Album. Experimental results show the superiority of the proposed approach compared with some existing methods.

Segmentation using fuzzy divergence

A new image thresholding method using fuzzy divergence is proposed here. Gamma distribution is used for determining the membership function of the pixels of an image. The proposed technique minimizes the fuzzy divergence or the separation between the actual and the ideal thresholded image.

Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images

Abstract The fuzzy ARTMAP has been applied to the supervised classification of multi-spectral remotely-sensed images. This method is found to be more efficient, in terms of classification accuracy, compared to the conventional maximum likelihood classifier and also multi-layer perceptron with back propagation learning. The results have been discussed.

Rough set theory based segmentation of color images

The theory of rough sets has gained importance and applicability in diverse areas of research, especially in data mining, knowledge discovery, artificial intelligence and information systems analysis. Rough sets have also been used in imaging, however, the application of rough sets for color image analysis has yet to be fully investigated. An interesting strategy for color image segmentation using rough set theory has been presented. A new concept of encrustation of the histogram, called histon, has been proposed for the visualization of multi-dimensional color information in on integrated fashion and its applicability in boundary region analysis has been shown. The histon correlates with the upper approximation of a set such that all elements belonging to this set are clarified as possibly belonging to the same segment or segments showing similar color value. The proposed encrustation provides a direct means of segregating pool of inhomogeneous regions into its components. Experimental results for various images have been presented.

Fuzzy statistics of digital images

The notion of first- and second-order fuzzy statistics of digital images is presented. Owing to the inherent imprecision in the gray values, fuzzy statistics have been observed to behave better in representing the spatial gray distribution in a digital image.

Analysis of fuzzy thresholding schemes

Abstract Fuzzy thresholding schemes preserve the structural details embedded in the original gray distribution. In this paper, various fuzzy thresholding schemes are analysed in detail. Thresholding scheme based on fuzzy clustering has been extended to a possibilistic framework. The characteristic difference for assignment of membership of fuzzy algorithms and their correspondence with conventional hard thresholding schemes have been investigated. A possible direction towards unifying a number of hard and fuzzy thresholding schemes has been presented.

Fuzzy measures for color image retrieval

In this paper we present a scheme for fuzzy similarity based strategy to retrieve an image from a library of color images. Color features are among the most important features used in image database retrieval. Due to its compact representation and low complexity, direct histogram comparison is the most commonly used technique in measuring color similarity of images. A gamma membership function, derived from the Gamma distribution, has been proposed to find the membership values of the gray levels of the histogram. We present here an image retrieval scheme with some popular vector fuzzy distance measures using a gamma membership function for finding the membership values of the gray levels and evaluate the matching function to select the appropriate retrieval mechanism.