Paper
7 March 1989 Structural Stereo Matching Of Laplacian-Of-Gaussian Contour Segments For 3D Perception
K. L. Boyer, G. E. Sotak Jr.
Author Affiliations +
Proceedings Volume 1005, Optics, Illumination, and Image Sensing for Machine Vision III; (1989) https://doi.org/10.1117/12.949048
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States
Abstract
We solve the stereo correspondence problem using Lapla-cian of Gaussian (LoG) zero-crossing contours as a source of primitives for structural stereopsis, as opposed to traditional point-based algorithms. For each image in the stereo pair, we apply the LoG operator, extract and link zero crossing points, filter and segment the contours into meaningful primitives, and compute a parametric structural description over the resulting primitive set. We then apply a variant of the inexact structural matching technique of Boyer and Kak Ill to recover the optimal interprimitive mapping (correspon-dence) function. Since an extended image feature conveys more information than a single point, its spatial and photometric behavior may be exploited to advantage; there are also fewer features to match, resulting in a smaller combinatorial problem. The structural approach allows greater use of spatial relational constraints, which allows us to eliminate (or reduce) the coarse-to-fine tracking of most point-based algorithms. Solving the correspondence problem at this level requires only an approximate probabilistic characterization of the image-to-image structural distortion, and does not require detailed knowledge of the epipolar geometry.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
K. L. Boyer and G. E. Sotak Jr. "Structural Stereo Matching Of Laplacian-Of-Gaussian Contour Segments For 3D Perception", Proc. SPIE 1005, Optics, Illumination, and Image Sensing for Machine Vision III, (7 March 1989); https://doi.org/10.1117/12.949048
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Cited by 4 scholarly publications.
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KEYWORDS
Nanoimprint lithography

Image segmentation

Electronic filtering

Image filtering

Machine vision

Silicon

Convolution

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