Focal-Plane Dynamic Texture Segmentation by Programmable Binning and Scale Extraction

Abstract

Dynamic textures are spatially-repetitive time-varying visual patterns that present, however, some temporal stationarity within their constituting elements. In addition, their spatial and temporal extents are a priori unknown. This kind of patterns is very common in nature, therefore dynamic texture segmentation is an important task for surveillance and monitoring. Conventional methods employ optic flow computation though it represents a heavy computational load. Here we describe texture segmentation based on focal-plane space-scale generation. The programmable size of the subimages to be analyzed and the scales to be extracted encode sufficient information from the texture signature to warn its presence. A prototype smart imager has been designed and fabricated in 0.35µm CMOS, featuring a very lowpower scale-space representation of used-defined subimages.