Real Time Shape Description Using Gaussian Wavelet Groups with Parallel Implementation

这是连续两年获奖的第一年。 的 研究的重点是小说形象描写的发展 基于高斯小波群（GWG）概念的算子。 这些基团在具有高共联合的簇中合成， 空间-频率分辨率，并且在检测中也是有效 以及直接从灰度图像定位形状。 将开发专门的GWG， 各种形状描述符，例如位置、方向、大小和 边界曲率 GWG将通过以下方式以真实的时间生成 新颖的晶格网络。 为了描述整体形状， 一种称为尺度-脊图（SSG）的拓扑结构将被 开发，沿着一个可以生成SSG的网络， 平行操作。 该项目还将开发一种高效的 用于真实的时间的使用GWG群的图像表示方案 图像压缩

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.