Hierarchical searching in model-based LADAR ATR using statistical separability tests

Stephen DelMarco; Erik Sobel; Joel Douglas

doi:10.1117/12.662125

18 May 2006 Hierarchical searching in model-based LADAR ATR using statistical separability tests

Stephen DelMarco, Erik Sobel, Joel Douglas

Proceedings Volume 6234, Automatic Target Recognition XVI; 623403 (2006) https://doi.org/10.1117/12.662125
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

In this work we investigate simultaneous object identification improvement and efficient library search for model-based object recognition applications. We develop an algorithm to provide efficient, prioritized, hierarchical searching of the object model database. A common approach to model-based object recognition chooses the object label corresponding to the best match score. However, due to corrupting effects the best match score does not always correspond to the correct object model. To address this problem, we propose a search strategy which exploits information contained in a number of representative elements of the library to drill down to a small class with high probability of containing the object. We first optimally partition the library into a hierarchic taxonomy of disjoint classes. A small number of representative elements are used to characterize each object model class. At each hierarchy level, the observed object is matched against the representative elements of each class to generate score sets. A hypothesis testing problem, using a distribution-free statistical test, is defined on the score sets and used to choose the appropriate class for a prioritized search. We conduct a probabilistic analysis of the computational cost savings, and provide a formula measuring the computational advantage of the proposed approach. We generate numerical results using match scores derived from matching highly-detailed CAD models of civilian ground vehicles used in 3-D LADAR ATR. We present numerical results showing effects on classification performance of significance level and representative element number in the score set hypothesis testing problem.

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Stephen DelMarco, Erik Sobel, and Joel Douglas "Hierarchical searching in model-based LADAR ATR using statistical separability tests", Proc. SPIE 6234, Automatic Target Recognition XVI, 623403 (18 May 2006); https://doi.org/10.1117/12.662125

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KEYWORDS

Data modeling

3D modeling

Model-based design

Databases

Solid modeling

LIDAR

Chemical elements

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