Direct Estimation of Shape from Texture

Authors:
J. Garding
Affiliations:
-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1993

Citing 4
Cited 7

Detection of surface orientation and motion from texture by a stereoogical technique.

Artificial Intelligence
Shape from texture: estimation, isotropy and moments

Artificial Intelligence
Shape from texture and contour by weak isotropy

Artificial Intelligence
Numerical Methods

Numerical Methods

Bidirectional Texture Contrast Function

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
3D particle tracking using an active vision

Pattern Recognition Letters
Material identification by surface reflection analysis in combination with bundle adjustment technique

Pattern Recognition Letters
Bidirectional Texture Contrast Function

International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Defect detection on inclined textured planes using the shape from texture method and the Delaunay triangulation

EURASIP Journal on Applied Signal Processing
3D shape recovery by the use of single image plus simple pattern illumination

ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Nonstructured light-based sensing for 3D reconstruction

Pattern Recognition

Quantified Score

Hi-index	0.14

Visualization

Abstract

Witkin (1981) has proposed a maximum likelihood (ML) estimator of surface orientation based on the observed directional bias of projected texture elements. However, a drawback of this procedure is that the estimate is only defined indirectly in terms of a set of nonlinear equations. An alternative method is proposed, which allows an estimate of the surface orientation to be computed directly in a single step from certain simple statistics of the image data. We also show that this direct estimate allows Witkin's ML estimate to be computed to within 0.05 degrees in only two or three iterative steps. The performance of the new estimator is demonstrated experimentally and compared to that of the ML estimator, using both synthetic data and real gray-level images.