CISE Experimental Partnerships: High Performance Systems for Shape and Action Modeling

99-01249 Davis，马里兰大学拉里·塞斯实验伙伴关系：形状和动作建模的高性能系统正在进行关于设计和开发并行算法的研究，以获得自然物体和动作的准确模型。这项工作的目标是开发一套在个人计算机网络上执行的并行、分布式算法，该算法可以整合来自多个视点的视频图像，以构建复杂、刚性对象的准确几何模型，或运动、关节和非刚性对象--特别是人--的详细动态行为模型。这项工作整合和扩展了以前在计算机视觉、分层数据结构和高性能软件系统方面的研究。用于从单个和多个视频流中提取形状和动作描述的几何和统计算法与构成所构造的形状和动作表示的基本数据结构的分层空间数据结构(例如八叉树和四叉树)一起使用。将这些技术和结构应用于大量视频流所涉及的海量数据和计算使得视觉科学家不可能单独设计、构造和评估这些分布式算法和系统的计算性能。视觉研究人员正在与并行和分布式计算研究人员合作，阐明一个包括几何、算法和面向系统的观点的解决方案。

99-01249Davis, Larry SUniversity of MarylandCISE Experimental Partnerships: High Performance Systems for Shape andAction ModelingResearch is being performed on the design and development of parallel algorithms for obtaining accurate models of natural objects and actions. The goal of the work is to develop a suite of parallel, distributed algorithms executing on a network of personal computers that can integrate video images from multiple viewpoints to construct an accurate geometric model of a complex, rigid object, or detailed dynamic action models of moving, articulated and non-rigid objects - specifically people. The work integrates and extends previous research in computer vision, hierarchical data structures and high performance software systems. Geometric and statistical algorithms for extracting shape and action descriptions from single and multiple video streams are being used along with hierarchical spatial data structures (e.g. oct-trees and quadtrees) which constitute the basic data structures for the shape and action representations being constructed. The enormous quantity of data and computation associated with applying such techniques and structures to a large number of video streams makes it impossible for vision scientists alone to design, construct and evaluate the omputational performance of these distributed algorithms and systems. Vision researchers are working together with parallel and distributed computing researchers to articulate a solution that includes geometric, algorithmic, and systems-oriented perspectives.