A pipeline architecture that performs near-real-time passive range detection using correlation-based binocular stereo vision is presented. A prototype system based on Datacube image processing boards running on a Sun-4 host has been configured specifically for this application. Using a disparity range of 64 pixels, the system matches 256*256 pixel stereo image pairs in approximately one second. Currently the system processes gray-scale images; modifications for processing color images at the same speed are underway. As the stereo system is running, the range information generated by it is processed and displayed on a high-resolution color monitor in various forms, using a TAAC-1 application accelerator board attached to the Sun-4 host system. The stereo matching algorithm, its hardware implementation, and show results are presented. In addition, extensions to the system for processing color images and research into future directions are discussed.>

A novel technique to analyze stereo images generated from a scanning electron microscope (SEM) for inspection of wafers for process control is presented. The two main features of this technique are that it uses a binary linear programming approach to set up and solve the correspondence problem, and that it uses constraints based on the physics of SEM image formation. Binary linear programming is a powerful tool with which to tackle constrained optimization problems, especially in the cases that involve matching between one data set and another. The authors also analyze the process of SEM image formation, and present constraints that are useful in solving the stereo correspondence problem. This technique has been solved on many images. Results for a few wafers are included.>