Real Time Shape Description Using Gaussian Wavelet Groups with Parallel Implementation

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This is the first year of a two-year continuing award. The research focuses on the development of novel image description operators based on the notion of Gaussian Wavelet Groups (GWG). These groups are synthesized in clusters that have high cojoint spatial-frequency resolution and are also efficient in detection and localization of shapes directly from gray-scale images. Specialized GWGs will be developed that are capable of measuring various shape descriptors such as location, orientation, size and boundary curvature. The GWGs will be generated in real time by novel lattice networks. To describe whole shapes, a novel topological structure called a scale-spinal-graph (SSG) will be developed, along with a network that can generate the SSG with parallel operations. The project will also develop an efficient image representation scheme using clusters of GWGs, for real time image compression.