On a cyclic string-to-string correction problem
Information Processing Letters
2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov Models with Spectral Features for 2D Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Shape Descriptor Based on Circular Hidden Markov Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic time warping of cyclic strings for shape matching
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Shape retrieval based on dynamic programming
IEEE Transactions on Image Processing
A multiscale representation method for nonrigid shapes with a single closed contour
IEEE Transactions on Circuits and Systems for Video Technology
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In classification tasks, shape descriptions, combined with matching techniques, must be robust to noise and invariant to transformations. Most of these distortions are relatively easy to handle, particularly if we represent contours by sequences. However, starting point invariance seems to be difficult to achieve. The concept of cyclic sequence, a sequence that has no initial/final point, can be of great help. We propose a new methodology to use HMMs to classify contours represented by cyclic sequences. Experimental results show that our proposal significantly outperforms other methods in the literature.