Characteristics and constraints of real-time geometric-feature extraction are discussed. Extracting geometric features from a digital image can be characterized as a computation-intensive task in the environment of a real-time automated vision system. Such tasks require algorithms with a high degree of parallelism and pipelining under the raster-scan I/O constraint. Using the divide-and-conquer technique, many feature extractions have been formulated as a pyramid structure and then mapped into a binary tree. An efficient mapping from a tree structure into a pipelined array of 2logN stages is presented for processing an N*N image. In the proposed mapping structure, the identification of the information growing property allows the exploitation of bit-level concurrency in the architecture design. Accordingly, the design of each staged pipelined processor is simplified containing only bit-serial arithmetic. A single VLSI chip that can generate (p+1)(q+1) moments concurrently in real-time applications is described. This chip has a hardware complexity of O(pq(p+q)log/sup 2/N) units, where p, q stand for the orthogonal orders of the moment. This hardware complexity is better than the O(pq(p+q)/sup 2/log/sup 2/N) units required by the other methods. A single VLSI chip to generate ten moments for a (512*512*8)/pixel image in real time is presented.
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