Accession Number : ADA332432
Title : Information Theoretic Investigation of Automatic Object Recognition and Image Fusion
Descriptive Note : Final rept. 25 Sep 95-24 May 97
Corporate Author : WORCESTER POLYTECHNIC INST MA
Personal Author(s) : Cyganski, David ; Vaz, Richard F.
PDF Url : ADA332432
Report Date : 24 AUG 1997
Pagination or Media Count : 28
Abstract : This work involves development of an information theoretic viewpoint for the model-based ATR problem, bringing the problems of ATR and pose estimation (PE) into the framework of signal detection/demodulation theory. Specific goals are to predict maximum achievable ATR/PE performance for a given object/scene scenario, and to quantify the tradeoff between performance and system complexity. Progress has been made toward investigating the feasibility of developing useful system performance bounds. Extensions of classical results by Blabut and Fano have shown that tight upper performance bounds can be developed; however, the bounds thus far produced require more information and computation than is practical. Progress has been made toward the long-term goal of designing ATR strategies based directly on information theoretic considerations. A Hilbert Space Decision Tree approach has been developed and tested. This ATR algorithm consists of a series of binary decisions, each of which occurs in the native object image space and maximizes the amount of object discrimination information gained by the decision. In this way, heuristic assumptions regarding the importance of model and image information are avoided. This technique shows great promise for guiding the design of ATR systems.
Descriptors : *TARGET RECOGNITION, *DATA FUSION, *INFORMATION THEORY, ALGORITHMS, IMAGE PROCESSING, TARGET DISCRIMINATION, HEURISTIC METHODS, PATTERN RECOGNITION, COMPUTER VISION.
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE