Malay K. Kundu

Genetic algorithms for optimal image enhancement

Genetic algorithms represent a class of highly parallel adaptive search processes for solving a wide range of optimization and machine learning problems. The present work is an attempt to demonstrate their adaptivity and effectiveness for searching global optimal solutions in selecting an appropriate image enhancement operator automatically.

Technique for fractal image compression using genetic algorithm

A new method for fractal image compression is proposed using genetic algorithm (GA) with an elitist model. The self transformation property of images is assumed and exploited in the fractal image compression technique. The technique described utilizes the GA, which greatly decreases the search space for finding the self similarities in the given image. This article presents theory, implementation, and an analytical study of the proposed method along with a simple classification scheme. A comparison with other fractal-based image compression methods is also reported.

Thresholding for edge detection using human psychovisual phenomena

An algorithm based on the facts of the human visual system is presented here whereby it is possible to select automatically (without human intervention) the thresholds for detecting the significant edges as perceived by human beings. The threshold value adapts with the background intensity according to the criterion governed by a characteristics of one of the De Vries-Rose, Webers and saturated regions. The algorithm is found to provide a satisfactory improvement in the performance in the conventional edge detection process.

BRAIN MR IMAGE CLASSIFICATION USING MULTISCALE GEOMETRIC ANALYSIS OF RIPPLET

We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an e-cient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 £ 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classiflcation accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is e-cient in brain MR image classiflcation.

Soft computing for image processing

S.K. Pal, A. Ghosh, M.K. Kundu: Soft Computing and Image Analysis: Features, Relevance and Hybridization.- Preprocessing and Feature Extraction: F.Russo: Image Filtering Using Evolutionary Neural Fuzzy Systems.- T. Law, D. Shibata, T. Nakamura, L. He, H. Itoh: Edge Extraction Using Fuzzy Reasoning.- S.K. Mitra, C.A. Murthy, M.K. Kundu: Image Compression and Edge Extraction Using Fractal Technique and Genetic Algorithms.- S. Mitra, R. Castellanos, S.-Y. Yang, S. Pemmaraju: Adaptive Clustering for Efficient Segmentation and Vector Quantization of Images.- B. Uma Shankar, A. Ghosh, S.K. Pal: On Fuzzy Thresholding of Remotely Sensed Images.- W. Skarbek: Image Compression Using Pixel Neural Networks.- L He, Y. Chao, T. Nakamura, H. Itho: Genetic Algorithm and Fuzzy Reasoning for Digital Image Compression Using Triangular Plane Patches.- N B. Karayiannis, T.C. Wang: Compression of Digital Mammograms Using Wavelets and Fuzzy Algorithms for Learning Vector Quantization.- V.D. Gesu: Soft Computing and Image Analysis.- J.H. Han, T.Y. Kim, L.T. Koczy: Fuzzy Interpretation of Image Data.- Classification: M. Grabisch: New Pattern Recognition Tools Based on Fuzzy Logic for Image Understanding.- N.K. Kasabov, S.I. Israel, B.J. Woodford: Adaptive, Evolving, Hybrid Connectionist Systems for Image Pattern Recognition.- P.A. Stadter, N.K Bose: Neuro-Fuzzy Computing: Structure, Performance Measure and Applications.- K. D. Bollacker, J. Ghosh: Knowledge Reuse Mechanisms for Categorizing Related Image Sets.- K. C. Gowda, P. Nagabhushan, H.N. Srikanta Prakash: Symbolic Data Analysis for Image Processing.- Applications: N.M. Nasrabadi, S. De, L.-C. Wang, S. Rizvi, A. Chan: The Use of Artificial Neural Networks for Automatic Target Recognition.- S. Gutta, H. Wechsler:Hybrid Systems for Facial Analysis and Processing Tasks.- V. Susheela Devi, M. Narasimha Murty: Handwritten Digit Recognition Using Soft Computing Tools.- T.L. Huntsburger, J.R. Rose, D. Girard: Neural Systems for Motion Analysis: Single Neuron and Network Approaches.- H.M. Kim, B. Kosko: Motion Estimation and Compensation with Neural Fuzzy Systems.

Document image segmentation using wavelet scale-space features

An efficient and computationally fast method for segmenting text and graphics part of document images based on textural cues is presented. We assume that the graphics part have different textural properties than the nongraphics (text) part. The segmentation method uses the notion of multiscale wavelet analysis and statistical pattern recognition. We have used M-band wavelets which decompose an image into M/spl times/M bandpass channels. Various combinations of these channels represent the image at different scales and orientations in the frequency plane. The objective is to transform the edges between textures into detectable discontinuities and create the feature maps which give a measure of the local energy around each pixel at different scales. From these feature maps, a scale-space signature is derived, which is the vector of features at different scales taken at each single pixel in an image. We achieve segmentation by simple analysis of the scale-space signature with traditional k- means clustering. We do not assume any a priori information regarding the font size, scanning resolution, type of layout, etc. of the document in our segmentation scheme.

Content-based image retrieval using visually significant point features

This paper presents a new image retrieval scheme using visually significant point features. The clusters of points around significant curvature regions (high, medium, and weak type) are extracted using a fuzzy set theoretic approach. Some invariant color features are computed from these points to evaluate the similarity between images. A set of relevant and non-redundant features is selected using the mutual information based minimum redundancy-maximum relevance framework. The relative importance of each feature is evaluated using a fuzzy entropy based measure, which is computed from the sets of retrieved images marked relevant and irrelevant by the users. The performance of the system is evaluated using different sets of examples from a general purpose image database. The robustness of the system is also shown when the images undergo different transformations.

A multi-scale morphologic edge detector

In this paper we present a morphologic edge detection methods using multi-scale approach for detecting edges of various fineness under noisy condition. It is shown that the proposed edge detector has the desirable properties that a good edge detector should have. Comparative study reveals its superiority over other morphologic edge detectors.

A blind CDMA image watermarking scheme in wavelet domain

The paper proposes a blind spread spectrum watermarking scheme where watermark information is embedded redundantly in the multilevel wavelet coefficients of the cover image. It has been shown that for a given embedding distortion, data embedding in LL and HH bands offers higher resiliency through better spectrum spreading compared to LH and HL band embedding, although the security of the hidden data is better in the later case. High resiliency of the scheme is supported by the good visual quality of the watermark images extracted from the several distorted watermarked images. Data hiding capacity is increased using code division multiple access (CDMA) technique and the mutual information values for the different watermark images are able to detect the degree of distortion already occurred in the watermarked image.

Automatic selection of object enhancement operator with quantitative justification based on fuzzy set theoretic measures

An algorithm for automatic selection of a nonlinear function appropriate for object enhancement of a given image is described. The algorithm does not need iterative visual interaction and prior knowledge of image statistics in order to select the transformation function for its optimal enhancement. A quantitative measure for evaluating enhancement equality has been provided based on fuzzy geometry. The concept of minimizing fuzziness (ambiguity) in both grayness and in spatial domain, as used by Pal and Rosenfeld [4], has been adopted. The selection criteria are further justified from the point of bounds of the membership function. The effectiveness of the algorithm is demonstrated for unimodal and right skewed images when possible nonlinear transformation functions are taken into account.