Determining three-dimensional shape from orientation and spatial frequency disparities
- David G. JonesAffiliated withDept. of Electrical Engineering, McGill UniversityComputer Science Division, University of California
- , Jitendra MalikAffiliated withDept. of Electrical Engineering, McGill UniversityComputer Science Division, University of California
Binocular differences in orientation and foreshortening are systematically related to surface slant and tilt and could potentially be exploited by biological and machine vision systems. Indeed, human stereopsis may possess a mechanism that specifically makes use of these orientation and spatial frequency disparities, in addition to the usual cue of horizontal disparity. In machine vision algorithms, orientation and spatial frequency disparities are a source of error in finding stereo correspondence because one seeks to find features or areas which are similar in the two views when, in fact, they are systematically different. In other words, it is common to treat as noise what is useful signal.
We have been developing a new stereo algorithm based on the outputs of linear spatial filters at a range of orientations and scales. We present a method in this framework, making use of orientation and spatial frequency disparities, to directly recover local surface slant. An implementation of this method has been tested on curved surfaces and quantitative experiments show that accurate surface orientation can be recovered efficiently. This method does not require the explicit identification of oriented line elements and also provides an explanation of the intriguing perception of surface slant in the presence of orientation or spatial frequency disparities, but in the absence of systematic positional correspondence.
- Determining three-dimensional shape from orientation and spatial frequency disparities
- Book Title
- Computer Vision — ECCV'92
- Book Subtitle
- Second European Conference on Computer Vision Santa Margherita Ligure, Italy, May 19–22, 1992 Proceedings
- pp 661-669
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
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