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Titlebook: Gaussian Scale-Space Theory; Jon Sporring,Mads Nielsen,Peter Johansen Book 1997 Springer Science+Business Media Dordrecht 1997 Diffusion.S

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發(fā)表于 2025-3-21 19:16:31 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Gaussian Scale-Space Theory
編輯Jon Sporring,Mads Nielsen,Peter Johansen
視頻videohttp://file.papertrans.cn/381/380958/380958.mp4
叢書名稱Computational Imaging and Vision
圖書封面Titlebook: Gaussian Scale-Space Theory;  Jon Sporring,Mads Nielsen,Peter Johansen Book 1997 Springer Science+Business Media Dordrecht 1997 Diffusion.S
描述Gaussian scale-space is one of the best understood multi-resolution techniques available to the computer vision and image analysis community. It is the purpose of this book to guide the reader through some of its main aspects. During an intensive weekend in May 1996 a workshop on Gaussian scale-space theory was held in Copenhagen, which was attended by many of the leading experts in the field. The bulk of this book originates from this workshop. Presently there exist only two books on the subject. In contrast to Lindeberg‘s monograph (Lindeberg, 1994e) this book collects contributions from several scale- space researchers, whereas it complements the book edited by ter Haar Romeny (Haar Romeny, 1994) on non-linear techniques by focusing on linear diffusion. This book is divided into four parts. The reader not so familiar with scale-space will find it instructive to first consider some potential applications described in Part 1. Parts II and III both address fundamental aspects of scale-space. Whereas scale is treated as an essentially arbitrary constant in the former, the latter em- phasizes the deep structure, i.e. the structure that is revealed by varying scale. Finally, Part IV i
出版日期Book 1997
關(guān)鍵詞Diffusion; Stereo; algorithms; calculus; image analysis; remote sensing/photogrammetry
版次1
doihttps://doi.org/10.1007/978-94-015-8802-7
isbn_softcover978-90-481-4852-3
isbn_ebook978-94-015-8802-7Series ISSN 1381-6446
issn_series 1381-6446
copyrightSpringer Science+Business Media Dordrecht 1997
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Optic Flow and Stereocities or for computing disparities. We will approach these types of problems using the well-known . which assumes that corresponding image features in different frames have equal luminance values (Horn and Schunck, 1981).
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On the Axiomatic Foundations of Linear Scale-Spacee formulations have been stated, based on different types of assumptions (usually referred to as scale-space axioms). The main subject of this chapter is to provide a synthesis between these linear scale-space formulations and to show how they are related. Another aim is to show how the scale-space
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Invariance Theorymally modeled with respect to a fixed Cartesian frame a Euclidean movement of the frame within the image plane induces a local transformation of the linear scale space preserving the metrical relations. If the linear scale space is subjected to a similarity transformation, which is a particular scal
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Stochastic Analysis of Image Acquisition and Scale-Space Smoothingof one image with parts in another. Methods for doing this have been developed for some time, see (Canny, 1986; Michelli et al., 1989; Deriche, 1987; Faugeras, 1994; Hildreth and Marr, 1980; Marr, 1982; Nalwa and Binford, 1986; Torre and Poggio, 1986). However, the stochastic analysis of these algor
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