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
Direct computation of shape cues using scale-adapted spatial derivative operators
International Journal of Computer Vision - Special issue: machine vision research at the Royal Institute of Technology
New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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
Shape from texture: an aggregation transform that maps a class of textures into surface orientation
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
Obtaining a 3-D orientation of projective textures using a morphological method
Pattern Recognition
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This paper presents a simple approach to the recovery of dense orientation estimates for curved textured surfaces. We make two contributions. Firstly, we show how pairs of spectral peaks can be used to make direct estimates of the slant and tilt angles for local tangent planes to the textured surface. We commence by computing the affine distortion matrices for pairs of corresponding 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. 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 main practical benefit furnished by our analysis is that it allows us to estimate the orientation angles in closed form without recourse to numerical optimisation. Based on these theoretical properties we present an algorithm for the analysis of regularly textured curved surfaces. We apply the method to a variety of real-world imagery.