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A Computational Model for Shape from Texture

2007; Wiley; Linguagem: Inglês

10.1002/9780470514610.ch14

1935-4657

Jitendra Malik, Ruth Rosenholtz,

Optical measurement and interference techniques

A Computational Model for Shape from Texture Jitendra Malik, Jitendra Malik Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA 94720, USA Isaac Newton Institute of Mathematical Sciences, Cambridge University, 20 Clarkson Road, Cambridge CB3 0EH, UKSearch for more papers by this authorRuth Rosenholtz, Ruth Rosenholtz Isaac Newton Institute of Mathematical Sciences, Cambridge University, 20 Clarkson Road, Cambridge CB3 0EH, UKSearch for more papers by this author Jitendra Malik, Jitendra Malik Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA 94720, USA Isaac Newton Institute of Mathematical Sciences, Cambridge University, 20 Clarkson Road, Cambridge CB3 0EH, UKSearch for more papers by this authorRuth Rosenholtz, Ruth Rosenholtz Isaac Newton Institute of Mathematical Sciences, Cambridge University, 20 Clarkson Road, Cambridge CB3 0EH, UKSearch for more papers by this author Book Editor(s):Gregory R. Bock, Gregory R. Bock OrganizerSearch for more papers by this authorJamie A. Goode, Jamie A. GoodeSearch for more papers by this author First published: 28 September 2007 https://doi.org/10.1002/9780470514610.ch14Citations: 1Book Series:Novartis Foundation Symposia AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Shape from texture is best analysed in a two-stage framework analogous to stereopsis and structure from motion: (a) computing the 'texture distortion' from the image; and (b) interpreting the 'texture distortion' to infer the orientation and shape of the scene surface in 3D. We model the texture distortion at a point in any particular direction on the image plane as an affine transformation and derive the relationship between the parameters of the affine transformation and the surface shape and orientation. We have developed a technique for estimating affine transforms between nearby image patches which is based on solving a system of linear constraints derived from a differential analysis. It is not necessary explicitly to identify texels or make restrictive assumptions about the nature of the image texture like isotropy. We have developed two different models for recovering surface orientation (slant and tilt) and shape (principal curvatures and directions) based on the estimated affine transforms in a number of directions. Experimental results are presented on images of planar and curved surfaces under perspective projection. Refernces Aloimonos J 1988 Shape from texture. Biol Cybern 58: 345–360 10.1007/BF00363944 CASPubMedWeb of Science®Google Scholar Bajcsy R, Lieberman L 1976 Texture gradient as a depth cue. Comput Graph & Image Processing 5: 52–67 10.1016/S0146-664X(76)80005-6 PubMedGoogle Scholar Blake A, Marinos C 1990 Shape from texture: estimation, isotropy and moments. 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