Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Dennis Ruck, PhD


Based on an autoregressive model, Complex Partial Correlation CPARCOR features are known to provide exceptional Position, Scale, and Rotation Invariant PSRI properties for planar 2-Dimensional 2-D object recognition. Although autogressive models have been successfully applied to numerous spatio-temporal recognition tasks, the effects of out-of-plane image rotations were never considered. This study investigates application of the CPAR-COR model to a five class problem of nonplanar 2-D views of 3-D objects. Recognition based on CPAR-COR features is evaluated using a Template Matching algorithm, two K-Nearest-Neighbor KNN classifiers, and a Hidden Markov Model HMM. Direct comparisons to recognition based on Fourier features are made. Results indicate that the CPAR-COR model parameters provide useful shape- features for recognition of out-of-plane rotations. Displaying exceptional PSRI properties, the features are shown capable of classification by simple nonadaptive recognition schemes. Relatively successful results are obtained for a variety of tests. The advantage of classification by a multiple-look technique over the traditional single-look method is clearly demonstrated. Feature space crowding is noted as the cause of unusual recognition rates for occluded-view tests. Although general trends are noted, optimal model order and selection of CPARCOR versus Fourier features are considered application dependent.

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The author's Vita page is omitted.