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By Simon Haykin
Major researchers in sign processing and neural computation current paintings geared toward selling the interplay and cross-fertilization among the 2 fields.
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4. I believe a deeper analysis of this category of models entails modeling directly, 32 Empirical Statistics and Stochastic Models for Visual Signals 20 20 40 40 60 60 80 80 100 100 120 120 140 140 160 160 180 180 200 200 50 100 150 200 50 100 150 200 Synthetic images illustrating the generic image models from the text. On the left, a sample dead leaves model using disks as primitives; on the right, a random wavelet model whose primitive are short ribbons. 4 not the objects in 2D projection, but their statistics in 3D.
There is no clear dividing line between distinct objects, texture, and noise. Even worse, some road patches which ought to be texture are larger than salient objects like the dog. 3 random model wavelet of this as clutter. The real world is made up of objects plus their parts and surface markings of all sizes and any camera resolves only so many of these. There is an ideal image of infinite resolution but any camera must use sensors with a positive point spread function. The theorem above says that this ideal image, because it carries all this detail, cannot even be a function.
This is in contrast with computers which, given only the grouping rules, find thousands of strange parses of both sentences and images. 1 grouping The Most Basic Grouping: Segmentation and Texture The simplest grouping rules are those of similar color (or brightness) and proximity. These two rules have been used to attack the segmentation problem. The most naive but direct approach to image segmentation is based on the assumption that images break up into regions on which their intensity values are relatively constant and across whose boundaries those values change discontinuously.