Robot vision
Biological Cybernetics
Shape from texture: general principle
Artificial Intelligence
Surface Orientation from Projective Foreshortening of Isotropic Texture Autocorrelation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from texture using the Wigner distribution
Computer Vision, Graphics, and Image Processing
Shape from texture: estimation, isotropy and moments
Artificial Intelligence
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Shape from Texture Using Local Spectral Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from Texture for Smooth Curved Surfaces
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Texture segmentation and shape in the same image
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Obtaining a 3-D orientation of projective textures using a morphological method
Pattern Recognition
Model of Frequency Analysis in the Visual Cortex and the Shape from Texture Problem
International Journal of Computer Vision
Recovering the shape from texture using lognormal filters
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Learning basic patterns from repetitive texture surfaces under non-rigid deformations
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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This paper shows how the local slant and tilt angles of regularly textured curved surfaces can be estimated directly, without the need for iterative numerical optimization. We work in the frequency domain and measure texture distortion using the affine distortion of the pattern of spectral peaks. The key theoretical contribution is to show that the directions of the eigenvectors of the affine distortion matrices can be used to estimate local slant and tilt angles of tangent planes to curved surfaces. In particular, the leading eigenvector points in the tilt direction. Although not as geometrically transparent, the direction of the second eigenvector can be used to estimate the slant direction. The required affine distortion matrices are computed using the correspondences between spectral peaks, established on the basis of their energy ordering. We apply the method to a variety of real-world and synthetic imagery.