Mixed-signal VLSI Architecture for Real-Time Computer Vision

Abstract

This paper presents the architecture of a computer vision system targeted for real-time robot vision and pattern recognition applications. The proposed mixed-signal very large scale integration (VLSI) architecture integrates photo-transduction with low- and medium-level processing such as multi-resolution edge extraction, scale-space integration, edge tracking, dominant point extraction, and database generation. Its high performance stems from a custom CMOS smart image sensor providing parallel access to illuminance data and a set of parallel analog filters performing multi-resolution edge extraction. We have also developed a digital controller which manages data flow between the processing modules of the system and which constructs a database of the observed scene under the supervision of a digital signal processor (DSP) unit. This database describes relevant object contours as a linked list of linear segments and circular arcs with precomputed local and global properties. Such a token description of the scene is suitable for robot vision and pattern recognition applications, since it significantly compresses the amount of data to be processed by further high-level algorithms. Experimental results obtained with the current prototype of the system are very promising, with the complete process, from image acquisition to scene database creation, performed in less than a second.