In this paper, we present a set of heterogeneous algorithms for computer vision tasks using the Image Understanding Architecture [IUA]. The full-scale IUA developed jointly by Hughes Research Labs and University of Massachusetts at Amherst is a multiple level heterogeneous architecture. Each lcvel is constructed to perform tasks most suitable to its mode of processing. The lowest level called CAAPP is an SIMD bit-serial mesh. The second level is an MIMD organization of numerically powerful digital signal processing chips. At the top level there are fewer number of MIMD general purpose processors. We propose a set of algorithms utilizing multiple levels of this organization, concurrently. The problems studied include Hough Transform-line detection, finding geometric properties of images, and high level image understanding tasks such as object matching.