Introduction Feature extraction and shape representation are important problems in computer vision, especially in robot vision and pattern recognition applications. Such applications usually impose difficult real-time requirements, as the complete understanding of the relevant features of a scene has to be performed in real-time, i.e. less than a second. Until now, however, these requirements have only been fulfilled by imposing several simplifying assumptions: the position and orientation of objects must be tightly constrained, the con- trast between the background and the foreground must be sufficient to allow trivial segmentation using thresholding, and the objects must not overlap. Although our understand- ing of computer vision is sufficiently advanced to address the general problems of feature extraction and shape repre- sentation in unstructured environments, their real-time implementation is difficult to achieve because of the large amount of information to process. Typically, extensive arrays of special-purpose units have been required, with corresponding consequences in cost and bulk. A promising research area in computer vision seeks to exploit special-purpose architectures embedded in the photo-detection plane in order to provide the processing capabilities required for real-time applications. These 1077-2014/97/050307 + 11 $25.00/ri960066 © 1997 Academic Press Limited Mixed-signal VLSI Architecture for Real-Time Computer Vision his 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 Tintegration (VLSI) architecture integrates photo-transduction with low- and medium-level processing such as multi-resolution edge extraction, scale-space integration, edge tracking, domi- nant 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 precom- puted 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 proto- type of the system are very promising, with the complete process, from image acquisition to scene database creation, performed in less than a second. © 1997 Academic Press Limited Stéphane Dallaire, Marc Tremblay and Denis Poussart Computer Vision and Systems Laboratory, Department of Electrical and Computer Engineering, Laval University, Québec, Canada, G1K 7P4 Email: stefdal@gel.ulaval.ca Real-Time Imaging 3, 307–317 (1997)