Parallel regional projection transformation (RPT) and VLSI implementation

Abstract A new regional projection transformation (RPT) to recognize unconnected patterns and patterns with isolated noise is presented. This new approach simplifies the process of recognizing compound patterns by transforming them into an integral object. Two kinds of RPT transforms are described and analysed: (1) diagonal-diagonal regional projection transformation (DDRPT), and (2) horizontal-vertical regional projection transformation (HVRPT). The patterns transformed by these two methods have several important properties which can simplify contour processing. The essential parallelisms in DDRPT and HVRPT can also facilitate their parallel implementation and parallel algorithms for both DDRPT and HVRPT, and their VLSI implementation are also designed. They can speed up the recognition process considerably with a time complexity of O(N) for processing a pattern with size of N × N , compared with O ( N 2 ) using a uniprocessor.

[1]  Concettina Guerra.  A VLSI Algorithm for the Optimal Detection of a Curve , 1986, J. Parallel Distributed Comput..

[2]  King-Sun Fu,et al.  Shape Discrimination Using Fourier Descriptors , 1977, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Azriel Rosenfeld,et al.  Parallel processing of region boundaries , 1989, Pattern Recognit..

[4]  Richard A. Volz,et al.  Recognizing Partially Occluded Parts , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Theodosios Pavlidis,et al.  Algorithms for Shape Analysis of Contours and Waveforms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Hon Fung Li,et al.  Parallel algorithms for recognizing handwritten characters using shape features , 1989, Pattern Recognit..

[7]  Heng-Da Cheng,et al.  VLSI curve detector , 1990, Pattern Recognit..

[8]  Olivier D. Faugeras,et al.  HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Ching Y. Suen,et al.  Large Tree Classifier with Heuristic Search and Global Training , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  E. V. Krishnamurthy,et al.  Parallel processing - principles and practice , 1989, International computer science series.

[11]  L. W. Tucker,et al.  Object recognition using the Connection Machine , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Gad M. Landau,et al.  Parallel (pram erew) algorithms for contour-based 2D shape recognition , 1991, Pattern Recognit..

[13]  Robert B. McGhee,et al.  Aircraft Identification by Moment Invariants , 1977, IEEE Transactions on Computers.

[14]  Kenneth H. Rosen,et al.  Discrete Mathematics and its applications , 2000 .

[15]  Ching Y. Suen,et al.  ISOETRP - an interactive clustering algorithm with new objectives , 1984, Pattern Recognit..

[16]  Ralph Roskies,et al.  Fourier Descriptors for Plane Closed Curves , 1972, IEEE Transactions on Computers.