Accurate invariant pattern recognition for perspective camera model

Mariya G. Serikova; Ekaterina N. Pantyushina; Vadim V. Zyuzin; Valery V. Korotaev; Joel J. P. C. Rodrigues

doi:10.1117/12.2184823

22 June 2015 Accurate invariant pattern recognition for perspective camera model

Mariya G. Serikova, Ekaterina N. Pantyushina, Vadim V. Zyuzin, Valery V. Korotaev, Joel J. P. C. Rodrigues

Proceedings Volume 9530, Automated Visual Inspection and Machine Vision; 95300O (2015) https://doi.org/10.1117/12.2184823
Event: SPIE Optical Metrology, 2015, Munich, Germany

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.

Citation Download Citation

Mariya G. Serikova, Ekaterina N. Pantyushina, Vadim V. Zyuzin, Valery V. Korotaev, and Joel J. P. C. Rodrigues "Accurate invariant pattern recognition for perspective camera model", Proc. SPIE 9530, Automated Visual Inspection and Machine Vision, 95300O (22 June 2015); https://doi.org/10.1117/12.2184823

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