Optimal Expected-Time Algorithms for Closest Point Problems
ACM Transactions on Mathematical Software (TOMS)
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In this paper an approach for storing astd employing local image features in video processing is presented. The approach is based on the usage of memory cells representing local image features and (nonfixed) spatial positions, which are organized in memory layers. By assigning frame-based recall function and learning procedure to the cells, the memory layers establish a content-based auto-associative memory. Thus, they cast be applied to solve several event detection tasks, as it is exemplified by dynamic background supression in a traffic scene, and counting of persons halting before a shopping window in an indoor scene. The case studies suggest that information gathered from the cells (like, cell history based scoring values) cast be used in various manners for video processing tasks circumventing the need for object segmentation aad tracking, typical in many conventional background-differencing methods.