Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Needles: A Stereo Algorithm for Texture

Not Accessible

Your library or personal account may give you access

Abstract

This paper describes Needles, an edge based stereo algorithm designed to take advantage of the smoothness of many textured surfaces. The correspondence problem is not addressed explicitly. Rather, a simple two stage process extracts surface position and orientation directly. Firstly local disparity histograms over a large range are constructed. Maxima in the histograms correspond to the possible surface depths. A Hough transform is used to fit a plane to the ambiguous disparity points close to the histogram maxima. This confirms and makes more precise the estimates of disparity obtained from the histograms. Local surface disparity and orientation are calculated from the best planar fit after all the histogram maxima (above a threshold) have been tried. This is an extension of an algorithm described in (Pollard 1985) which uses a Hough transform to find local surface orientation without explicit matching. In his algorithm pairs of possible matches vote for the disparity gradient between them. When all pairs have voted the winning disparity gradient (and hence, surface orientation) has the highest Hough accumulator value.

© 1989 Optical Society of America

PDF Article
More Like This
A Closer Look at the Contribution of a Third Camera Towards Accuracy in Stereo Correspondence2

Umesh R. Dhond and J. K. Aggarwal
TuC1 Image Understanding and Machine Vision (IUMV) 1989

Cooperative Segmentation and Stereo Matching

Laurent Vinet, Peter Sander, Laurent Cohen, and André Gagalowicz
TuC2 Image Understanding and Machine Vision (IUMV) 1989

Direct measurement of volume of arbitrarily shaped piles from binocular stereo images

Neil Hunt and H. K. Nishihara
WD2 Image Understanding and Machine Vision (IUMV) 1989

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.