Image Understanding Software for Hybrid Hardware

The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official Agency position, policy, or decision, unless so designated by other official documentation. Abstract In this Phase I effort, we designed a hybrid image understanding system consisting of neural network software running on parallel hardware and symbolic processing software running on conventional hardware. Such a hybrid system exploits the inherent parallelism in neural systems without sacrificing the efficiency of symbolic processing on conventional hardware. We used automatic target recognition for laser-radar (LADAR) imagery as a specific image understanding problem to demonstrate algorithm feasibility. We demonstrated that segmentation can be done without neural methods, but we also determined that the Boundary Contour System neural model of low-level vision offers great potential for improved segmentation, and we performed an efficiency analysis on a massively parallel computer. Our research into the feature extraction process demonstrated that both neuromorphic (local receptive field) and standard statistical features are necessary for high recognition rates. Since these features can be computed independently, they map perfectly onto parallel hardware. Object classification is done by a hierarchy of Fuzzy-ARTMAP neural networks that performs recognition at multiple levels of discrimination for each image object. A hierarchical approach to recognition simplifies the task because each classifier has fewer possible outcomes, and it provides a natural mapping onto coarse-grain parallel hardware.