Showing papers by "Malay K. Kundu published in 2001"

Adaptive basis selection for multi texture segmentation by M-band wavelet packet frames

Abstract: We propose an approach for texture feature extraction based on M-band wavelet packet frames. The features so extracted are used for segmentation of multi texture images. Standard dyadic wavelets are not suitable for the analysis of high frequency signals with relatively narrow bandwidth and also are not translation invariant. Also, since most significant information of a texture often lies in the intermediate frequency bands, the present work employs an overcomplete wavelet decomposition scheme called discrete M-band wavelet packet frame (DM-bWPF), which yields improved segmentation accuracies. Wavelet packets represent a generalization of the method of multiresolution decomposition and comprise all possible combinations of subband tree decomposition. We propose a computationally efficient search procedure to find the optimal basis based on some maximum criterion of textural measures derived from the statistical parameters of each of the subbands, to locate dominant information in each subband (frequency channel) and decide further decomposition.

Wavelet-based texture segmentation of remotely sensed images

Abstract: A texture feature extraction scheme based on M-band wavelet packet frames is investigated. The features so extracted are used for segmentation of satellite images which usually have complex and overlapping boundaries. The underlying principle is based on the fact that different image regions exhibit different textures. Since most significant information of a texture often lies in the intermediate frequency bands, the present work employs an overcomplete wavelet decomposition scheme called discrete M-band wavelet packet frame (DM-bWPF), which yields improved segmentation accuracies. Wavelet packets represent a generalization of the method of multiresolution decomposition and comprise all possible combinations of subband tree decomposition. We propose a computationally efficient search procedure to find the optimal basis based on some maximum criterion of textural measures derived from the statistical parameters of each of the subbands, to locate dominant information in each subbands (frequency channels) and decide further decomposition.

On-chip computation of Euler number of a binary image for efficient database search

Abstract: The Euler number is a fundamental topological feature of an image, which remains invariant under translation, rotation, scaling, and rubber-sheet transformation of the image. A novel algorithm for computing the Euler number of a binary image is proposed which is based on the properties of runs of 0's and 1's present in the pixel matrix. The algorithm outperforms significantly the existing techniques in terms of both the number of pixel accesses and CPU time. It can be easily parallelized, and a simple on-chip implementation is reported here. Results on a database consisting of 1039 logo images reveal that the Euler number has a strong discriminatory power, and hence can be used for efficient database searching or matching of binary images. The proposed algorithm is very fast and easy to implement, and has potential of wide applicability in image processing.

Multiscale Segmentation of Document Images Using M -Band Wavelets

Abstract: In this work we propose an algorithm for segmentation of the text and non-text parts of document image using multiscale feature vectors. We assume that the text and non-text parts have different textural properties. M-band wavelets are used as the feature extractors and the features give measures of local energies at different scales and orientations around each pixel of the M×M bandpass channel outputs. The resulting multiscale feature vectors are classified by an unsupervised clustering algorithm to achieve the required segmentation, assuming no a priori information regarding the font size, scanning resolution, type layout etc. of the document.

Fractal image compression using iterated function system with probabilities

Abstract: Deals with a new technique of fractal image compression based on the theory of iterated function systems (IFS) with probabilities. The theory of IFS with probabilities, in the context of image compression, is a relatively unexplored area. The rationale behind using this approach stems from the fact that it is possible to define a Markov operator associated with the probability measure whose support is the support of the given image. A new technique of fractal image compression is proposed using IFS with probabilities. The technique is found to be extremely fast in computing both the coefficients of maps and the probabilities. Thus, the proposed technique provides a very fast fractal-based image compression encoding.