Download e-book for kindle: A Pyramid Framework for Early Vision: Multiresolutional by Jean-Michel Jolion

By Jean-Michel Jolion

ISBN-10: 1461362075

ISBN-13: 9781461362074

ISBN-10: 1461527929

ISBN-13: 9781461527923

Biological visible platforms hire hugely parallel processing to accomplish real-world visible initiatives in actual time. A key to this striking functionality appears that organic structures build representations in their visible photograph information 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 photo processing, item detection, texture discrimination, contour detection and processing, characteristic detection and outline, and movement detection and monitoring. It additionally exhibits how those modules will be carried out 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 approach designers; it offers a common method of imaginative and prescient structures layout in addition to a collection of strong, effective imaginative and prescient modules.

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Extra resources for A Pyramid Framework for Early Vision: Multiresolutional Computer Vision

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The new signal is thus characterized by a triple (N',dx'4u'). du / k. dx. However, N' and du' are still underconstrained and we get two possible representations for the new frequency domain. The first is (N'4u') = (N4u/k). This representation means that we have more information about the signal (the spatial period is smaller than the previous one). This is impossible without coming back to the continuous version of the signal. The other solution is (N',du') = (N/k , du). Lh). dx4u). Thus by reducing the size of the frequency domain, we end up with a smaller representation of the original spatial domain by means of a bigger spatial interval and a smaller number of samples, yielding a compression of the signal as a consequence of the subsampling.

In the context of multiresolution there exists more and more powerful specialized hardware, especially for fast convolution and sampling. This hardware takes advantage of the nature of the computation which is the same at the high and low levels.

Moreover, this decomposition has been proved to be used in the human visual system. Indeed, one of the widely used tools in human visual system studies is the visual grating [Gordon-89, p. 110]. It is an image made of light and dark parallel stripes. 2 Hierarchical multiresolution 33 and a phase. Using this tool, many neurophysiological studies have shown that the visual system conveys information about spatial frequencies in (between four and six) tuned channels. These channels can be approximated by linear spatial filters whose characteristic functions are described as differences of two Gaussians, and so are closely related to the Laplacian operator [Wilson-79].

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


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