Exploiting task and data parallelism in parallel Hough and Radon transforms

Edge detection and shape detection in digital images are very computationally intensive problems. Parallel algorithms can potentially provide significant speedups while preserving the quality of the result obtained. Hough and Radon Transforms are projection-based transforms which are commonly used for edge detection and shape detection respectively. We propose in this paper various new parallel algorithms which exploit both task and data parallelism available in Hough and Radon transforms algorithms. A memory scalable aggressive task parallel algorithm is shown to be the most optimal algorithm in terms of memory scalability and performance on an IBM SP2.

[1]  Ian Foster,et al.  A compilation system that integrates High Performance Fortran and Fortran M , 1994, Proceedings of IEEE Scalable High Performance Computing Conference.

[2]  Prithviraj Banerjee,et al.  Compiling MATLAB programs to ScaLAPACK: exploiting task and data parallelism , 1996, Proceedings of International Conference on Parallel Processing.

[3]  Jorge L. C. Sanz,et al.  Radon and Projection Transform-Based Computer Vision: Algorithms, A Pipeline Architecture, and Industrial Applications , 1988 .

[4]  Mary Jane Irwin,et al.  Edge detection using fine-grained parallelism in VLSI , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  K. Mani Chandy,et al.  Integrating Task and Data Parallelism in UC , 1995, ICPP.

[6]  Thomas R. Gross,et al.  Exploiting task and data parallelism on a multicomputer , 1993, PPOPP '93.

[7]  Sachin S. Sapatnekar,et al.  A Convex Programming Approach for Exploiting Data and Functional Parallelism on Distributed Memory Multicomputers , 1994, 1994 Internatonal Conference on Parallel Processing Vol. 2.

[8]  William Gropp,et al.  Skjellum using mpi: portable parallel programming with the message-passing interface , 1994 .

[9]  Prithviraj Banerjee,et al.  Parallel Algorithms for VLSI Layout Verification , 1996, J. Parallel Distributed Comput..

[10]  John A. Chandy,et al.  The Paradigm Compiler for Distributed-Memory Multicomputers , 1995, Computer.

[11]  Prithviraj Banerjee,et al.  Integrating task and data parallelism in an irregular application: a case study , 1996, Proceedings of SPDP '96: 8th IEEE Symposium on Parallel and Distributed Processing.

[12]  Prithviraj Banerjee,et al.  Task scheduling for exploiting parallelism and hierarchy in VLSI CAD algorithms , 1993, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[13]  Jorge L. C. Sanz,et al.  The Hough Transform has O(N) Complexity on N x N Mesh Connected Computers , 1990, SIAM J. Comput..

[14]  Anthony Skjellum,et al.  Using MPI - portable parallel programming with the message-parsing interface , 1994 .

[15]  Sartaj Sahni,et al.  Hypercube Algorithms: with Applications to Image Processing and Pattern Recognition , 1990 .

[16]  Azriel Rosenfeld,et al.  Hough transform algorithms for mesh-connected SIMD parallel processors , 1988, Comput. Vis. Graph. Image Process..

[17]  E. R. Davies,et al.  A skimming technique for fast accurate edge detection , 1992, Signal Process..

[18]  Jorge L. C. Sanz,et al.  Radon and projection transform-based computer vision , 1988 .

[19]  Susanne E. Hambrusch,et al.  Parallel Algorithms for Line Detection on a Mesh , 1989, J. Parallel Distributed Comput..

[20]  Milind Girkar,et al.  Automatic Extraction of Functional Parallelism from Ordinary Programs , 1992, IEEE Trans. Parallel Distributed Syst..