Multiresolution image processing techniques with applications in texture segmentation and nonlinear filtering

Mary L Comer, Purdue University

Abstract

We present a new algorithm for segmentation of textured images using a multiresolution Bayesian approach. The algorithm uses a multiresolution Gaussian autoregressive (MGAR) model for the pyramid representation of the observed image, and assumes a multiscale Markov random field model for the class label pyramid. Unlike other approaches, which have either used a single-resolution representation of the observed image or implicitly assumed independence between different levels of a multiresolution representation of the observed image, the models used in this thesis incorporate correlations between different levels of both the observed image pyramid and the class label pyramid. The criterion used for segmentation is the minimization of the expected value of the number of misclassified nodes in the multiresolution lattice. The estimate which satisfies this criterion is referred to as the "multiresolution maximization of the posterior marginals" (MMPM) estimate, and is a natural extension of the single-resolution maximization of the posterior marginals (MPM) estimate. The parameters of the MGAR model--the means, prediction coefficients, and prediction error variances of the different textures--are unknown. The expectation-maximization (EM) algorithm is used to estimate these parameters while simultaneously performing the segmentation. Analysis and experimental results demonstrating the performance of the algorithm are presented. We also propose new approaches for the extension of binary and grayscale morphological operations to color imagery. We investigate two approaches for "color morphology"--a vector approach and a component-wise approach. New vector morphological filtering operations are defined, and a set-theoretic analysis of these vector operations is presented. We also present experimental results comparing the performance of the vector approach and the component-wise approach for multiscale color image analysis and for noise suppression in color images. Finally, we describe a video coding technique which generates an embedded bit stream that provides complete data rate scalability. This video coding scheme is based on the Embedded Zerotree Wavelet (EZW) algorithm for still image compression. We present experimental results demonstrating the performance of the algorithm at various data rates.

Degree

Ph.D.

Advisors

Delp, Purdue University.

Subject Area

Electrical engineering

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