Image Feature Extraction on Connection Machine CM-5

In this paper, we present a parallel implementation of an image feature extraction task on Connection Machine CM-5. We show that, given a 2048 2048 grey level image as input, the extraction of image features, which includes edge detection, thinning, linking, and linear approximation, can be performed in less than 1.2 seconds on a partition of CM-5 having 512 processing nodes. A serial implementation on a Sun Sparc 400 takes more than 8 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. Our technique starts by modeling the communication and computation features of the machine. Using this model, scalable algorithms are designed.

[1]  James George Dunham,et al.  Optimum Uniform Piecewise Linear Approximation of Planar Curves , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Viktor K. Prasanna,et al.  Scalable parallel implementations of perceptual grouping on connection machine CM-5 , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5).

[3]  Viktor K. Prasanna,et al.  Scalable data parallel algorithms and implementations for object recognition , 1993 .

[4]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[5]  Frank Thomson Leighton,et al.  Tight Bounds on the Complexity of Parallel Sorting , 1985, IEEE Trans. Computers.

[6]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[7]  Vipin Kumar,et al.  Introduction to Par-allel Computing: Design and Analysis of Parallel Algorithms , 1994 .

[8]  Viktor K. Prasanna,et al.  Scalable Data Parallel Implementations of Object Recognition Using Geometric Hashing , 1994, J. Parallel Distributed Comput..

[9]  Charles E. Leiserson,et al.  Fat-trees: Universal networks for hardware-efficient supercomputing , 1985, IEEE Transactions on Computers.

[10]  James Robergé A data reduction algorithm for planar curves , 1985, Comput. Vis. Graph. Image Process..

[11]  Charles E. Leiserson,et al.  The Networks of the Connection Machine CM-5 , 1992, Heinz Nixdorf Symposium.

[12]  T. T. Kwan,et al.  Communication and computation performance of the CM-5 , 1993, Supercomputing '93.

[13]  Robert A. Hummel,et al.  Massively parallel model matching: geometric hashing on the Connection Machine , 1992, Computer.