Algorithm of invariant pattern recognition using redundant Hough transform

Michael A. Popov; Sergey Ju. Markov

doi:10.1117/12.222718

3 October 1995 Algorithm of invariant pattern recognition using redundant Hough transform

Michael A. Popov, Sergey Ju. Markov

Proceedings Volume 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling; (1995) https://doi.org/10.1117/12.222718
Event: Photonics East '95, 1995, Philadelphia, PA, United States

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.

Citation Download Citation

Michael A. Popov and Sergey Ju. Markov "Algorithm of invariant pattern recognition using redundant Hough transform", Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); https://doi.org/10.1117/12.222718

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KEYWORDS

Detection and tracking algorithms

Hough transforms

Pattern recognition

Image segmentation

Imaging arrays

Signal to noise ratio

Feature extraction

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