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An Extension of Separable Lattice 2-D HMMs for Rotational Data Variations
Akira TAMAMORI Yoshihiko NANKAKU Keiichi TOKUDA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2012/08/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
image recognition, hidden Markov models, variational method,
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This paper proposes a new generative model which can deal with rotational data variations by extending Separable Lattice 2-D HMMs (SL2D-HMMs). In image recognition, geometrical variations such as size, location and rotation degrade the performance. Therefore, the appropriate normalization processes for such variations are required. SL2D-HMMs can perform an elastic matching in both horizontal and vertical directions; this makes it possible to model invariance to size and location. To deal with rotational variations, we introduce additional HMM states which represent the shifts of the state alignments among the observation lines in a particular direction. Face recognition experiments show that the proposed method improves the performance significantly for rotational variation data.