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

Authors

  • M. P. Mukhina National Aviation University, Kyiv, Ukraine
  • I. V. Barkulova National Aviation University, Kyiv, Ukraine

DOI:

https://doi.org/10.18372/1990-5548.55.12785

Keywords:

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

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.

Author Biographies

M. P. Mukhina, National Aviation University, Kyiv, Ukraine

Aviation Computer-Integrated Complexes Department, Educational & Research Institute of Information and Diagnostic Systems

Doctor of Engineering Science. Professor

I. V. Barkulova, National Aviation University, Kyiv, Ukraine

Aviation Computer-Integrated Complexes Department, Educational & Research Institute of Information and Diagnostic Systems

Post-graduate student

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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Issue

Vol. 1 No. 55 (2018)

Section

MATHEMATICAL MODELING OF PROCESSES AND SYSTEMS

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