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In the early stages of visual information processing one of the most fundamental and complex task is segmentation. This process is used to divide images or image sequences into meaningful regions or objects. Such a representation is definitely needed to go ...
We present a new statistical model for characterizing texture images based on wavelet-domain hidden Markov models. With a small number of parameters, the new model captures both the subband marginal distributions and the dependencies across scales and orie ...
Random textures differ from natural textures because they lack structure. Structure is a concept that is difficult to formalize, however, we generally observe that it is associated to spatial dependency between adjacent pixels. Random textures, in fact, ar ...
We investigated the spectral properties of color detection mechanisms via a noise masking paradigm. Contrast detection thresholds were measured for equiluminant chromatic modulations in the (L-M,S-(L+M))-plane within a random texture. Each texture image wa ...
The thesis studies the optimization of a specific type of computer graphic representation: polygon-based, textured models. More precisely, we focus on meshes having 4-8 connectivity. We study a progressive and adaptive representation for textured 4-8 meshe ...
This thesis investigates advanced signal processing concepts and their application to geometric processing and transformations of images and volumes. In the first part, we discuss the class of transformations that project volume data onto a plane using par ...
We define texture mapping as an optimization problem for which the goal of preserving the maximum amount of information in the mapped texture. We derive a solution that is optimal in the least-squares sense and that corresponds to the pseudo-inverse of a ...
We define texture mapping as an optimization problem for which the goal of preserving the maximum amount of information in the mapped texture. We derive a solution that is optimal in the least-squares sense and that corresponds to the pseudo-inverse of a r ...
