ALGORITHM OF VARIATIVE FEATURE DETECTION AND PREDICTION IN CONTEXT-DEPENDENT RECOGNITION

M. P. Mukhina, I. V. Barkulova

Abstract


Application of context-dependent classification for recognition tasks is proposed. In the context-free classification, the starting point was the Bayesian classifier. Morphological features such as object form, area, and eccentricity were considered through context-dependent classification. As result, dependences which can be used for object recognition have been obtained, and further they can be used together with interesting point detectors. The procedure of prediction of object variative features was developed.

Keywords


Object recognition; context-dependent classification; (binary large object) blob analysis

References


D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, 60(2): pp. 91–100, 2004.

H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robust features,” in Proceedings of the 9th European Conference on Computer Vision, 2006.

Berthold K. P Horn, Robot Vision. MIT Press, 1986.

Theodoridis S. Konstantinos Koutroumbas, Pattern recognition, Elsevier, 2003.


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