JOURNAL ARTICLE

<title>Algorithm of invariant pattern recognition using redundant Hough transform</title>

М. А. ПоповSergey J. Markov

Year: 1995 Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Vol: 2588 Pages: 646-652   Publisher: SPIE

Abstract

The invariant pattern recognition and distortions parameters estimation using redundant Hough transform (RHT) is considered. Authors offer to use RHT which differs from classical Hough transform (HT) by replacement of procedure of `voting' on procedure of `double voting'. The RHT-array has following property (in difference from HT-array): translate and rotation distortions in image plane can be reduced to cyclic shifts along coordinate axes in HT-plane for all values of distortions. Classical HT provides the completion of this property not for all significances or distortions. This property permits to construct invariant pattern recognition algorithm simply enough. The algorithm for recognition and distortions estimation is presented. The algorithm is based on consecutive completion of RHT and modified Walsh- Hadamard transform. As a result the set of features is formed. The features are invariant to image noise and affine distortions of rotation and translation. On the basis of these features the classification of object is executed and the values of distortions are defined. The structural scheme of automatic recognition system that uses the algorithm is presented. The experimental researches of algorithm on aircraft imagery shows its good performance even with noisy images.

Keywords:
Hough transform Algorithm Invariant (physics) Affine transformation Artificial intelligence Mathematics Pattern recognition (psychology) Rotation (mathematics) Computer vision Computer science Image (mathematics)

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Topics

Optics and Image Analysis
Social Sciences →  Business, Management and Accounting →  Management Information Systems
Advanced Scientific Research Methods
Life Sciences →  Agricultural and Biological Sciences →  Food Science
Image and Object Detection Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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