A multiple instruction multiple data (MIMD) parallel computing platform built upon a network of TMS320C40/44s (C40/C44) for real-time image processing of a hierarchical foveal machine vision (HFMV) system is described in this paper. The architecture of the system, the parallel algorithm development environment, and strategies to map tasks into the computing platform are described. The platform supports both static and dynamic computing resource allocation. The performance of the computing platform is illustrated by examples.

The design of a robot head for active computer vision tasks is described. The stereo head/eye platform uses a common elevation configuration and has four degree-of-freedom. The joints are driven by DC servo motors coupled with incremental optical encoders and backlash minimizing gearboxes. The details of mechanical design, head controller design, the architecture of the system, and the design criteria for various specifications are presented.

A new technique for the calibration of the intrinsic parameters of cameras for active vision systems is presented. By making deliberate camera motions, the intrinsics of the cameras can be calibrated based either on the positional difference of optical flow field (PDOFF) or the trajectories of features (TOF) on the image plane. A way to detect the distortion of a camera lens is also presented. The calibration method is simple, fast, reliable, and very easy to combine with task performing processes. The performance of the technique is illustrated. The method can be used for general calibration of camera intrinsics.>