A Pyramid Framework for Early Vision: Multiresolutional - download pdf or read online

By Jean-Michel Jolion

ISBN-10: 1461362075

ISBN-13: 9781461362074

ISBN-10: 1461527929

ISBN-13: 9781461527923

Biological visible structures hire hugely parallel processing to accomplish real-world visible projects in actual time. A key to this notable functionality appears to be like that organic platforms build representations in their visible snapshot facts at a number of scales. APyramid Framework for Early Vision describes a multiscale, or `pyramid', method of imaginative and prescient, together with its theoretical foundations, a collection of pyramid-based modules for picture processing, item detection, texture discrimination, contour detection and processing, function detection and outline, and movement detection and monitoring. It additionally exhibits how those modules could be applied very successfully on hypercube-connected processor networks.
A Pyramid Framework for Early Vision is meant for either scholars of imaginative and prescient and imaginative and prescient procedure designers; it offers a common method of imaginative and prescient platforms layout in addition to a suite of sturdy, effective imaginative and prescient modules.

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Additional info for A Pyramid Framework for Early Vision: Multiresolutional Computer Vision

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3 GAM The GAM pyramid was designed at George Mason University (Fairfax, Virginia, 1984). It consists of five levels made of general processors and specialized adders (341 processing elements). The base is composed of 16 x 16 cells. Each element has 9 links (one parent, four children and four brothers) and 8K bits of RAM memory. It is thus a 4-connected non overlapped quad-pyramid. The chip is the MPP (Massively Parallel Processor) chip designed by NASA. The pyramid operates in an SIMD mode except for inter-level transfers.

2 Hierarchical multiresolution 35 merging of edges are of interest in order to facilitate the detection and interpretation of these phenomena in real applications [Lu-89, Lu-92]. However, it must be noticed that most of these studies have been done using a ID continuous framework. The extension to two dimensions is not obvious in either the continuous and or discrete domain. Moreover, this framework does not assume the sampling process of hierarchical multiresolutioll. Another theoretical limit of classical multiresolution techniques arises when the observed image is not a linear function of the parameter to be computed.

Burt-83b, Toet-89] are applications of hierarchical multiresolution to the field of image synthesis. [Excoffier-92] is a generalization to image sequence fusion. The main references for scale-space theory are [Witkin-86, Badaud-86, Yuille-86, Hummel-87, Brunnstrom90]. [Mokhtarian-86, -92, Lowe-88] present its application to curve representation and analysis. The theory of the construction of a discrete scale-space for discrete images is presented in [Lindeberg-90b]. [Haralick-87] is an introduction to mathematical morphology based multiresolution theory.

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A Pyramid Framework for Early Vision: Multiresolutional Computer Vision by Jean-Michel Jolion

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