Parallel Polygon Approximation Algorithm Targeted at Reconfigurable Multi-Ring Hardware

The paper presents a parallel algorithm for polygon approximation targeted at reconfigurable multi-ring hardware. The proposed algorithm grows the edges of polygon approximation that is based on principle of merging. The edge/s are grown simultaneously at point/s where the minimum merging error is produced. The merging process is made faster by carrying it out in two stages: i) During first stage it uses templates, generated during an off line process, to carry out fast initial polygon approximation, ii) The segments of the initial polygon approximation are further merged during the second stage. The simultaneous growing of edges makes the algorithm suitable for a parallel processing hardware. The paper outlines a parallel algorithm for polygon approximation. It discusses three broadcasting mechanisms for utilizing the multi-ring hardware. The mapping of the polygon approximation algorithm on the multi-ring topology using various broadcasting mechanisms is discussed. Index Terms Parallel algorithm for polygon approximation, Local and global error for polygon approximation, Reconfigurable multi-ring network, broadcasting mechanisms for multi-ring network.

[1]  Jack Sklansky,et al.  Fast polygonal approximation of digitized curves , 1980, Pattern Recognit..

[2]  Hamid R. Arabnia,et al.  A parallel numerical algorithm on a reconfigurable multi-ring network , 1998, Telecommun. Syst..

[3]  Urs Ramer,et al.  An iterative procedure for the polygonal approximation of plane curves , 1972, Comput. Graph. Image Process..

[4]  M. A. Wani,et al.  SAFARI: A Structured Approach for Automatic Rule Induction , 2001 .

[5]  Hamid R. Arabnia,et al.  Parallel Edge-Region-Based Segmentation Algorithm Targeted at Reconfigurable MultiRing Network , 2003, The Journal of Supercomputing.

[6]  Theodosios Pavlidis,et al.  Segmentation of Plane Curves , 1974, IEEE Transactions on Computers.

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

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

[9]  Hamid R. Arabnia,et al.  Parallel Computer Vision on a Reconfigurable Multiprocessor Network , 1997, IEEE Trans. Parallel Distributed Syst..

[10]  Hamid R. Arabnia,et al.  A distributed stereocorrelation algorithm , 1995, Proceedings of Fourth International Conference on Computer Communications and Networks - IC3N'95.

[11]  Roland T. Chin,et al.  On the Detection of Dominant Points on Digital Curves , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Karin Wall,et al.  A fast sequential method for polygonal approximation of digitized curves , 1984, Comput. Vis. Graph. Image Process..

[13]  Yung-Nien Sun,et al.  Genetic Algorithms for Error-Bounded Polygonal Approximation , 2000, Int. J. Pattern Recognit. Artif. Intell..

[14]  Charles M. Williams,et al.  An Efficient Algorithm for the Piecewise Linear Approximation of Planar Curves , 1978 .

[15]  Duc Truong Pham,et al.  Feature-based control chart pattern recognition , 1997 .

[16]  Duc Truong Pham,et al.  Efficient control chart pattern recognition through synergistic and distributed artificial neural networks , 1999 .

[17]  Hamid R. Arabnia,et al.  A Reconfigurable Architecture for Image Processing and Computer Vision , 1995, Int. J. Pattern Recognit. Artif. Intell..

[18]  Bruce G. Batchelor,et al.  Edge-Region-Based Segmentation of Range Images , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Li-De Wu A Piecewise Linear Approximation Based on a Statistical Model , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  T. Pavlidis Algorithms for Graphics and Image Processing , 1981, Springer Berlin Heidelberg.

[21]  Theodosios Pavlidis,et al.  Waveform Segmentation Through Functional Approximation , 1973, IEEE Transactions on Computers.

[22]  David G. Lowe,et al.  Organization of smooth image curves at multiple scales , 1988, International Journal of Computer Vision.