Fusion of symbolic and feature information for high-level object recognition

Neelima Shrikhande; Jim Getzinger

doi:10.1117/12.143060

20 April 1993 Fusion of symbolic and feature information for high-level object recognition

Neelima Shrikhande, Jim Getzinger

Proceedings Volume 1827, Model-Based Vision; (1993) https://doi.org/10.1117/12.143060
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

Abstract

In this paper, we present an algorithm which uses symbolic as well as physical labels on the edges and surfaces to constrain the scene-model matching process. Symbolic labels are used to distinguish between curved and planar objects, occluding edges, background surface, etc. These are used along with physical labels such as distances and angles to prune the matching graph. This paper describes a real time object recognition environment that integrates the pruning method described above with low level image processing and high level object recognition algorithms. Results are reported for synthetic and real range images. Our results show that inclusion of symbolic labels improves the accuracy and efficiency of matching.

Citation Download Citation

Neelima Shrikhande and Jim Getzinger "Fusion of symbolic and feature information for high-level object recognition", Proc. SPIE 1827, Model-Based Vision, (20 April 1993); https://doi.org/10.1117/12.143060

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