A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three partition refinement algorithms
SIAM Journal on Computing
CCS expressions finite state processes, and three problems of equivalence
Information and Computation
Operational and algebraic semantics of concurrent processes
Handbook of theoretical computer science (vol. B)
2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deciding bisimilarity and similarity for probabilistic processes
Journal of Computer and System Sciences
An entropic estimator for structure discovery
Proceedings of the 1998 conference on Advances in neural information processing systems II
Gene Discovery in DNA Sequences
IEEE Intelligent Systems
Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
EMMCVPR '99 Proceedings of the Second International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Bounded Hyperset Theory and Web-like Data Bases
KGC '97 Proceedings of the 5th Kurt Gödel Colloquium on Computational Logic and Proof Theory
Belief bisimulation for hidden markov models: logical characterisation and decision algorithm
NFM'12 Proceedings of the 4th international conference on NASA Formal Methods
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Hidden Markov Models (HMMs) are an useful and widely utilized approach to the modeling of data sequences. One of the problems related to this technique is finding the optimal structure of the model, namely, its number of states. Although a lot of work has been carried out in the context of the model selection, few work address this specific problem, and heuristics rules are often used to define the model depending on the tackled application. In this paper, instead, we use the notion of probabilistic bisimulation to automatically and efficiently determine the minimal structure of HMM. Bisimulation allows to merge HMM states in order to obtain a minimal set that do not significantly affect model performances. The approach has been tested on DNA sequence modeling and 2D shape classification. Results are presented in function of reduction rates, classification performances, and noise sensitivity.