الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The objective of modelling in image analysis is to capture the visual characteristics of images in a few parameters so as to understand the nature of the phenomenon generating the images. In this dissertation, novel approaches for modelling the intensity distribution of the gray levels and spatial interaction between the pixels in the observed image will be introduced. In this dissertation, a novel approach to align an image of a textured object with a given prototype will be proposed. Visual appearance of the images, after equalizing their signals, is modelled with a Markov–Gibbs random field with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically vii from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. A novel technique for unsupervised maximum a posteriori (MAP) based segmentation of multi-modal gray levels images is proposed. The segmentation approach for multi-modal gray levels images assumes that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of gray levels. Most conventional approaches suggest that, initial images and desired maps of regions are described by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. In this dissertation, the goal is to provide more accurate model identification, to better specify region borders, each empirical distribution of image signals is precisely approximated by a linear combination of discrete Gaussians (LCDGs) with positive and negative components. Initial segmentation based on the LCDGs models is then iteratively refined by using the MGRF with analytically estimated potentials. The convergence of the overall segmentation algorithm at each stage will be explained in detail.