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Proceedings Paper

Accurate invariant pattern recognition for perspective camera model
Author(s): Mariya G. Serikova; Ekaterina N. Pantyushina; Vadim V. Zyuzin; Valery V. Korotaev; Joel J. P. C. Rodrigues
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Paper Abstract

In this work we present a pattern recognition method based on geometry analysis of a flat pattern. The method provides reliable detection of the pattern in the case when significant perspective deformation is present in the image. The method is based on the fact that collinearity of the lines remains unchanged under perspective transformation. So the recognition feature is the presence of two lines, containing four points each. Eight points form two squares for convenience of applying corner detection algorithms. The method is suitable for automatic pattern detection in a dense environment of false objects. In this work we test the proposed method for statistics of detection and algorithm's performance. For estimation of pattern detection quality we performed image simulation process with random size and spatial frequency of background clutter while both translational (range varied from 200 mm to 1500 mm) and rotational (up to 60°) deformations in given pattern position were added. Simulated measuring system included a camera (4000x4000 sensor with 25 mm lens) and a flat pattern. Tests showed that the proposed method demonstrates no more than 1% recognition error when number of false targets is up to 40.

Paper Details

Date Published: 22 June 2015
PDF: 6 pages
Proc. SPIE 9530, Automated Visual Inspection and Machine Vision, 95300O (22 June 2015); doi: 10.1117/12.2184823
Show Author Affiliations
Mariya G. Serikova, ITMO Univ. (Russian Federation)
Ekaterina N. Pantyushina, ITMO Univ. (Russian Federation)
Vadim V. Zyuzin, ITMO Univ. (Russian Federation)
Valery V. Korotaev, ITMO Univ. (Russian Federation)
Joel J. P. C. Rodrigues, Univ. of Beira Interior (Portugal)

Published in SPIE Proceedings Vol. 9530:
Automated Visual Inspection and Machine Vision
Jürgen Beyerer; Fernando Puente León, Editor(s)

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