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Proceedings Paper

Multiscale stochastic approach to object detection
Author(s): Daniel R. Tretter; Charles A. Bouman
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Paper Abstract

We present a method for object detection based on a novel multiscale stochastic model together with Bayesian estimation techniques. This approach results in a fast, general algorithm which may be easily trained for specific objects. The object model is based on a stochastic tree structure in which each node is an important subassembly of the three dimensional object. Each node or subassembly is modeled using a Gaussian pyramid decomposition. The objective of the algorithm is then to estimate the unknown position of each subassembly, and to determine on the presence of the object. We use a fast multiscale search technique to compute the sequential MAP (SMAP) estimate of the unknown position, scale factor, and 2-D rotation for each subassembly. The search is carried out in a manner similar to a sequential likelihood ratio test, where the process advances in scale rather than time. We use a similar search to estimate the model parameters for a given object from a set of training images.

Paper Details

Date Published: 22 October 1993
PDF: 12 pages
Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); doi: 10.1117/12.157878
Show Author Affiliations
Daniel R. Tretter, Purdue Univ. (United States)
Charles A. Bouman, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 2094:
Visual Communications and Image Processing '93
Barry G. Haskell; Hsueh-Ming Hang, Editor(s)

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