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Titlebook: A Taxonomy for Texture Description and Identification; A. Ravishankar Rao Book 1990 Springer-Verlag New York, Inc. 1990 algorithms.compute

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期刊全稱A Taxonomy for Texture Description and Identification
影響因子2023A. Ravishankar Rao
視頻videohttp://file.papertrans.cn/143/142410/142410.mp4
學(xué)科分類Springer Series in Perception Engineering
圖書封面Titlebook: A Taxonomy for Texture Description and Identification;  A. Ravishankar Rao Book 1990 Springer-Verlag New York, Inc. 1990 algorithms.compute
影響因子A central issue in computer vision is the problem of signal to symbol transformation. In the case of texture, which is an important visual cue, this problem has hitherto received very little attention. This book presents a solution to the signal to symbol transformation problem for texture. The symbolic de- scription scheme consists of a novel taxonomy for textures, and is based on appropriate mathematical models for different kinds of texture. The taxonomy classifies textures into the broad classes of disordered, strongly ordered, weakly ordered and compositional. Disordered textures are described by statistical mea- sures, strongly ordered textures by the placement of primitives, and weakly ordered textures by an orientation field. Compositional textures are created from these three classes of texture by using certain rules of composition. The unifying theme of this book is to provide standardized symbolic descriptions that serve as a descriptive vocabulary for textures. The algorithms developed in the book have been applied to a wide variety of textured images arising in semiconductor wafer inspection, flow visualization and lumber processing. The taxonomy for texture can serve
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Persistent Cloaca – Clinical Aspectsinto this set of symbols. Researchers in computer vision have usually focussed on this part of the problem. In this chapter we analyze the symbolic description of oriented textures by first proposing an appropriate set of symbols and then solving the signal to symbol transformation problem.
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Alexander M. Hohlschneider,John M. Hustsonrtunately, such a parallel does not exist for texture — we do not know what the dimensions of texture are. A possible insight could be gained by analyzing the classes of texture presented in the earlier chapters and finding out ways in which these textures can be combined. The aim of this chapter is to define such compositional schemes.
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Book 1990roblem has hitherto received very little attention. This book presents a solution to the signal to symbol transformation problem for texture. The symbolic de- scription scheme consists of a novel taxonomy for textures, and is based on appropriate mathematical models for different kinds of texture. T
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Alexander M. Hohlschneider,John M. Hustsonvity of orientation, which can vary arbitrarily over the entire image. In other words, the texture is anisotropic. Every point in the image is associated with a dominant local orientation, and a local measure of the coherence or degree of anisotropy of the flow pattern. One way of visualizing orient
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