Smart-sensing VLSI architecture for the embedded extraction of dominant points along 2D contours

This paper presents a special-purpose VLSI architecture for dominant point extraction along 2-D contours. It is designed to be integrated as part of a machine vision system with real-time edge-extraction and edge-tracking capabilities in order to allow the creation of a high-level database representation of the observed scene. Such dominant points carry useful information for shape analysis and pattern recognition applications since they represent a local shape property and segment object contours into piecewise linear segments and circular arcs. The proposed architecture implements an algorithm based on the curvature primal sketch. It consists of a set of 1-D systolic FIR filters performing a multiresolution analysis of the scene's object contours, a set of finite state machines extracting zero- crossings and extrema of the filtered data, and a set of scale-space integration cells combining the accurate locations provided by the finest filters with the noise rejection properties of the coarsest ones in order to reliably extract relevant dominant points with accurate localization. The overall architecture has been successfully simulated using real edge images. Some of these results are presented and discussed.