Visual speech recognition is an emerging research field. In this paper, we examine the suitability of support vector machines for visual speech recognition. Each word is modeled as a temporal sequence of visemes corresponding to the different phones realized. One support vector machine is trained to recognize each viseme and its output is converted to a posterior probability through a sigmoidal mapping. To model the temporal character of speech, the support vector machines are integrated as nodes into a Viterbi lattice. We test the performance of the proposed approach on a small visual speech recognition task, namely the recognition of the first four digits in English. The word recognition rate obtained is at the level of the previous best reported rates

In this paper we proposed a visual speech recognition network based on Support Vector Machines. Each word of the dictionary is modeled by a set of temporal sequences of visemes. Each viseme is described by a support vector machine, and the temporal character of speech is modeled by integrating the support vector machines as nodes into Viterbi decoding lattices. Experiments conducted on a small visual speech recognition task show a word recognition rate on the level of the best rates previously reported, even without training the state transition probabilities in the Viterbi lattices and using very simple features. This proves the suitability of support vector machines for visual speech recognition.

This paper introduces the vanishing points to self-calibrate a structured light system. The vanishing points permit to automatically remove the projector's keystone effect and then to self-calibrate the projector-camera system. The calibration object is a simple planar surface such as a white paper. Complex patterns and 3D calibrated objects are not required any more. The technique is compared to classic calibration and validated with experimental results.

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The gold standard for the quantitative evaluation of steatosis is liver biopsy, but this is an invasive method. The recent trend is to investigate and develop novel non-invasive hepatic tissue evaluation methods, able to give performances close to biopsy, one of the possible solutions being ultrasound image analysis. Finding the most suitable descriptors of the histological tissue changes (invariant to the ultrasound device and patient) in this imaging modality is an important issue. Several such descriptors are reported in the literature, starting from simple intensity parameters to more sophisticated ones. Here we investigate the discrimination ability of the hepatic steatosis as opposed to healthy tissue by a set of computationally simple features, extracted from the gray level histogram of a small sized region of interest (ROI) positioned at three depths in the ultrasound hepatic image. The advantage of finding computationally simple features, as intensity histogram extracted features, can be the possibility to include their computation directly in ultrasound devices, provided they offer good tissue discrimination. The in-depth variations of some of the features investigated in this paper show a good discrimination in steatosis evaluation and quantification, close to the state of the art in the field.