Paper
21 August 1987 Symbolic Surface Descriptors For 3-Dimensional Object Recognition
Ramesh Jain, Thawach Sripradisvarakul, Nancy O'Brien
Author Affiliations +
Proceedings Volume 0754, Optical and Digital Pattern Recognition; (1987) https://doi.org/10.1117/12.939971
Event: OE LASE'87 and EO Imaging Symposium, 1987, Los Angeles, CA, United States
Abstract
We are studying classification of symbolic surface de-scriptors in classes that will allow fast approaches for 3-D object recognition. In our approach for object recognition, we will use features to hypothesize objects using parallel distributed approach, and then use models of objects to find objects that are present in a scene. Symbolic surface descriptors represent global features of an object and do not change when the object is partially occluded, while local features (such as corners or edges) may disappear en-tirely. We have developed a technique to segment surfaces and compute their polynomial surface descriptors. In this paper we present results of our study to determine which different types of surface descriptors (such as cylindrical, spherical, elliptical, hyperbolic, etc) can be reliably recovered from biquadratic equation models of various surfaces.
© (1987) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ramesh Jain, Thawach Sripradisvarakul, and Nancy O'Brien "Symbolic Surface Descriptors For 3-Dimensional Object Recognition", Proc. SPIE 0754, Optical and Digital Pattern Recognition, (21 August 1987); https://doi.org/10.1117/12.939971
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Cited by 15 scholarly publications.
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KEYWORDS
Object recognition

Image segmentation

Optical pattern recognition

Aluminum

Data modeling

Detection and tracking algorithms

Spherical lenses

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