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Towards a robust physics-based object recognition system

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Object Representation in Computer Vision (ORCV 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 994))

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Abstract

A successful 3D object recognition system must take into account imperfections in the input data, due for example to fragmentation or sensor noise. In this paper we propose a methodology for robust 3D object recognition using uncertain image data. In particular, we present a method capable of achieving acceptable performance in the presence of both segmentation problems and sensor uncertainty, thus eliminating the need for ad hoc heuristics. The proposed method is based upon the use of probabilistic models suggested by the underlying physics processes. These models are statistically validated and tested under controlled experimentation.

This work was supported in part by NSF grant IRI9309100 and in part by a Pennsylvania State University Research Initiation Grant.

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Author information

Authors and Affiliations

  1. Dept. of Electrical Engineering Dept. of Computer Science and Engineering, The Pennsylvania State University, 16802, University Park, PA

    Octavia I. Camps

Authors
  1. Octavia I. Camps
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Editor information

Martial Hebert Jean Ponce Terry Boult Ari Gross

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© 1995 Springer-Verlag Berlin Heidelberg

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Camps, O.I. (1995). Towards a robust physics-based object recognition system. In: Hebert, M., Ponce, J., Boult, T., Gross, A. (eds) Object Representation in Computer Vision. ORCV 1994. Lecture Notes in Computer Science, vol 994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60477-4_21

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  • DOI: https://doi.org/10.1007/3-540-60477-4_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60477-8

  • Online ISBN: 978-3-540-47526-2

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