VLSI architecture for the embedded extraction of dominant points on object contours

This paper presents a special-purpose VLSI architecture for dominant point extraction along 2D contours. 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 1D 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 implemented and integrated to a custom machine vision system with real-time edge-extraction and edge-tracking capabilities. Some experimental results obtained using this system are presented and discussed. Performance issues are also addressed.