Efficient Methods for Automatic Recognition With Application to Target Identification

Purdue University, West Lafayette IN

Investigators

Abstract

The objective of the investigator's research is to learn how to encode the organization of complex structures for efficient classification, recognition and browsing. Complex structures such as objects, images, 3D scans and web documents are considered. The focus of the investigation is on very large datasets (e.g., several millions). Because of computational issues, such large sizes cannot be handled by existing browsing methods. As part of this research, theoretical tools and methods for addressing these issues are being developed; these tools and methods are then applied to the problem of automatically identifying a target from 3-D imaging Laser Radar (Ladar) measurements. The difficulty of indexing a large database for geometry/structure-based queries is mostly due to two factors: 1) The inherent conflict between speed and accuracy, and 2) The curse of dimensionality. The structure representations that are being developed by the investigator, which are based on invariant statistics, address these difficulties. First of all, they are fully invariant and so they allow fast data comparison by bypassing the problem of finding the best mapping between two structures. Secondly, for a generic structure, they are lossless, and so they do not compromise accuracy. Moreover, they allow for low complexity metrics, thus yielding fast comparison algorithms that are almost surely 100% accurate (as opposed to approximate algorithms, which are always approximate.) In addition, as they contain no ambiguity, it is possible to index them with high-dimensional indexing techniques that address the curse of dimensionality. Finally, they are floating-point arithmetic and low-level data friendly.