Statistical methods for speech recognition
Statistical methods for speech recognition
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Approximate Viterbi decoding for 2D-hidden Markov models
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Generalized hidden Markov models. I. Theoretical frameworks
IEEE Transactions on Fuzzy Systems
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This paper presents a novel multi-dimensional hidden Markov model approach to tackle the complex issue of image modeling. We propose a set of efficient algorithms that avoids the exponential complexity of regular multidimensional HMMs for the most frequent algorithms (Baum-Welch and Viterbi) due to the use of a random dependency tree (DT-HMM). We provide the theoretical basis for these algorithms, and we show that their complexity remains as small as in the uni-dimensional case. A number of possible applications are given to illustrate the genericity of the approach. Experimental results are also presented in order to demonstrate the potential of the proposed DTHMM for common image analysis tasks such as object segmentation, and tracking.