|dc.description.abstract||Automated target recognition (ATR) using a computer vision system is a problem of extremely high complexity. A 3-D object recognition scheme involves many image analysis and enhancement techniques, including image processing, image segmentation, image registration, and object modeling and projection. This dissertation addresses the problem of 3-D object recognition using five distinct methods of matching image data with model projection-derived data.
In analyzing each digitized video image, a variety of techniques, including an optimal gray level map for correlating binary line drawings with gradient images, were used to enhance the visibility of particular features and to increase signal to noise ratios in images. The shapes extracted from these enhanced images were then analyzed in a number of fashions, including the statistical descriptors of Karhunen-Loeve transformation.
The first of the five object identification methods which were tested compared descriptors of the object to be analyzed with those of model projections meeting certain criteria. The second compared the object descriptors to those of a precalculated series of model projections. The third method used the descriptions of the second method as a starting point for a neural network, and then proceeded to learn the differences between these model projections and actual data. The neural net as realized demonstrated a great reduction in training time over conventional implementations. Calibration difficulties of other methods were greatly reduced by the learning capability of the neural net. The fourth method cross-correlated the optimally mapped gradient of the object image with a series of model projections. Finally, ways of combining these methods to utilize the strengths of each were investigated. Superior accuracy was obtained for cross-correlation. Optimal techniques which significantly reduced the number of required correlations and hence the computational load were also found to give very accurate results.||