As an effective method, the embedded hidden Markov model (E-HMM) has been widely used in pattern recognition. On applying the E-HMM to face recognition, the performance heavily depends on the selection of model parameters. Aiming at the problem of model selection, a selective ensemble of multi E-HMMs based face recognition algorithm is proposed. Experimental results illustrate that compared wit...

In this study, a new hidden Markov model that integrates generalized dynamic feature parameters into the model structure is developed and evaluated using maximum-likelihood (ML) and minimum-classification-error (MCE) pattern recognition approaches. In addition to the motivation of direct minimization of error rate, the MCE approach automatically eliminates the necessity of artificial constraint...

Using social science methods to induce a state of frustration in users we collected physiologi cal video and behavioral data and developed a strategy for coupling these data with real world events The e ectiveness of the proposed strat egy was tested in a study with thirty six sub jects where the system was shown to reliably synchronize and gather data for a ect analysis Hidden Markov Models we...

In this paper, we propose a new method based on Hidden Markov Models to learn and recognize places in an indoor environment by a mobile robot. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (neural networks. . .) are their capabilities to modelize noisy temporal signals of variable length. We...

Hidden Markov models (HMMs) have become a standard tool for pattern recognition in computer vision. However, issues of parameter estimation and evaluation are rarely addressed though they play key roles in just how HMMs perform. Without addressing these issues it can be readily shown that a so-called HMM model may actually be a Bayesian classifer or Markov Chain. In this paper we develop method...

Time and again hidden Markov models have been demonstrated to be highly effective in one-dimensional pattern recognition and classification problems such as speech recognition. A great deal of attention is now focussed on 2-D and possibly 3-D applications arising from problems encountered in computer vision in domains such as gesture, face, and handwriting recognition. Despite their widespread ...

Processing text components in multimedia contents remains a challenging issue for document indexing and retrieval. More specifically, handwritten characters processing is a very active field of pattern recognition. This paper describes an innovative two-dimensional approach for character recognition and segmentation. The method proposed combines Markovian modeling, efficient decoding algorithm ...