Multiscale stochastic approach to object detection

Daniel R. Tretter; Charles A. Bouman

doi:10.1117/12.157878

22 October 1993 Multiscale stochastic approach to object detection

Daniel R. Tretter, Charles A. Bouman

Proceedings Volume 2094, Visual Communications and Image Processing '93; (1993) https://doi.org/10.1117/12.157878
Event: Visual Communications and Image Processing '93, 1993, Cambridge, MA, United States

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.

Citation Download Citation

Daniel R. Tretter and Charles A. Bouman "Multiscale stochastic approach to object detection", Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); https://doi.org/10.1117/12.157878

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