article

Computing Local Surface Orientation and Shape from Texture forCurved Surfaces

Authors:

Jitendra Malik,

Ruth RosenholtzAuthors Info & Claims

International Journal of Computer Vision, Volume 23, Issue 2

Pages 149 - 168

https://doi.org/10.1023/A:1007958829620

Published: 01 June 1997 Publication History

Abstract

Shape from texture is best analyzed in two stages, 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 surface in the scene. We model the texture distortion for a given point and direction on the image plane as an affine transformation and derive the relationship between the parameters of this transformation and the shape parameters. 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. One need not explicitly identify texels or make restrictive assumptions about the nature of the texture such as isotropy. We use non-linear minimization of a least squares error criterion to recover the surface orientation (slant and tilt) and shape (principal curvatures and directions) based on the estimated affine transforms in a number of different directions. A simple linear algorithm based on singular value decomposition of the linear parts of the affine transforms provides the initial guess for the minimization procedure. Experimental results on both planar and curved surfaces under perspective projection demonstrate good estimates for both orientation and shape. A sensitivity analysis yields predictions for both computer vision algorithms and human perception of shape from texture.

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Cited By

Wang YZhang QAubuchon CKemp JDomini FTompkin J(2023)On Human-like Biases in Convolutional Neural Networks for the Perception of Slant from TextureACM Transactions on Applied Perception10.1145/361345120:4(1-18)Online publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1145/3613451
Kang ZKim K(2018)Multimodal perception study on virtual 3D curved textures with vision and touch for interactive multimedia systemsMultimedia Tools and Applications10.1007/s11042-017-4392-877:2(2209-2223)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s11042-017-4392-8
Wiles OZisserman A(2018)3D Surface Reconstruction by PointillismComputer Vision – ECCV 2018 Workshops10.1007/978-3-030-11015-4_21(263-280)Online publication date: 8-Sep-2018
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Index Terms

Computing Local Surface Orientation and Shape from Texture forCurved Surfaces
1. Computing methodologies

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Published In

cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 23, Issue 2

June 1997

81 pages

ISSN:0920-5691

Editors:
Takeo Kanade
Carnegie Mellon Univ.
,
Olivier Faugeras
INRIA

Issue’s Table of Contents

Copyright © Copyright © 1997 Kluwer Academic Publishers.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 1997

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Cited By

Wang YZhang QAubuchon CKemp JDomini FTompkin J(2023)On Human-like Biases in Convolutional Neural Networks for the Perception of Slant from TextureACM Transactions on Applied Perception10.1145/361345120:4(1-18)Online publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1145/3613451
Kang ZKim K(2018)Multimodal perception study on virtual 3D curved textures with vision and touch for interactive multimedia systemsMultimedia Tools and Applications10.1007/s11042-017-4392-877:2(2209-2223)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s11042-017-4392-8
Wiles OZisserman A(2018)3D Surface Reconstruction by PointillismComputer Vision – ECCV 2018 Workshops10.1007/978-3-030-11015-4_21(263-280)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-11015-4_21
Hua YTian H(2016)Depth estimation with convolutional conditional random field networkNeurocomputing10.1016/j.neucom.2016.06.029214:C(546-554)Online publication date: 19-Nov-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.06.029
Cimpoi MMaji SKokkinos IVedaldi A(2016)Deep Filter Banks for Texture Recognition, Description, and SegmentationInternational Journal of Computer Vision10.1007/s11263-015-0872-3118:1(65-94)Online publication date: 1-May-2016
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Shuai FTong QYang CFengyun CChua TLu KMei TWu X(2013)Depth recovery from a single defocused image based on depth locally consistencyProceedings of the Fifth International Conference on Internet Multimedia Computing and Service10.1145/2499788.2499836(56-61)Online publication date: 17-Aug-2013
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Aiger DCohen-Or DMitra N(2012)Repetition Maximization based Texture RectificationComputer Graphics Forum10.5555/2318896.231890331:2pt2(439-448)Online publication date: 1-May-2012
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Cole FIsola PFreeman WDurand FAdelson E(2012)ShapecollageProceedings of the 12th European conference on Computer Vision - Volume Part III10.1007/978-3-642-33712-3_48(665-678)Online publication date: 7-Oct-2012
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