Low level vision processing on connection machine CM-5
Viktor K. Prasanna, Canran Wang, Ashfaq Khokhar · 2002
The authors study low level vision processing on connection machine CM-5. A parallel computing model to capture the architectural features of CM-5 is identified. In this model, given an n by n image, it is shown that, a low level vision system, which includes edge detection, thinning, linking, and linear approximation, can be performed in O(n/sup 2//P) time using P processors. These algorithms are scalable in the range 1 /spl les/ P /spl les/ n. Various experiments were conducted to fine tune the implementation to suit the communication and the computation capabilities of the machine. Based on these experiments, implementations were performed to efficiently utilize the architectural and programming features of the machine. The implementations show that, given a 2048 /spl times/ 2048 grey level image as input, linear features can be extracted in less than 1.1 seconds on a CM-5 partition having 512 processing nodes. A serial implementation on a Sun Sparc 400 takes more than eight minutes. Experimental results on various sizes of images using various partitions of CM-5 are also reported. The software has been developed in a modular fashion to permit various techniques to be employed for the individual steps of the processing.