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Hidden Markov Models with Spectral Features for 2D Shape Recognition
December 2001 (vol. 23 no. 12)
pp. 1454-1458
In this paper, we present a technique using Markov models with spectral features for recognizing 2D shapes. We will analyze the properties of Fourier spectral features derived from closed contours of 2D shapes and use these features for 2D pattern recognition. We develop algorithms for reestimating parameters of hidden Markov models. To demonstrate the effectiveness of our models, we have tested our methods on two image databases: hand-tools and unconstrained handwritten numerals. We are able to achieve high recognition rates of 99.4 percent and 96.7 percent without rejection on these two sets of image data, respectively.
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Index Terms:
Hidden Markov models, spectral features, 2D shape recognition, outer contours, handwritten numeral recognition
Citation:
J. Cai, Z.Q. Liu, "Hidden Markov Models with Spectral Features for 2D Shape Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 12, pp. 1454-1458, Dec. 2001, doi:10.1109/34.977569
