Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface reconstruction by Voronoi filtering
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Hierarchical Discriminant Analysis for Image Retrieval
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Design and Use of Linear Models for Image Motion Analysis
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Multiobject Behavior Recognition by Event Driven Selective Attention Method
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Shape dimension and approximation from samples
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Efficient Simplicial Reconstructions of Manifolds from Their Samples
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Non-linear Bayesian Image Modelling
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A Temporal Network of Support Vector Machine Classifiers for the Recognition of Visual Speech
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Recognition of Visual Speech Elements Using Hidden Markov Models
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Manifold reconstruction from point samples
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Nonlinear manifold learning for dynamic shape and dynamic appearance
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A support vector machine-based dynamic network for visual speech recognition applications
EURASIP Journal on Applied Signal Processing
Dynamic Bayesian networks for audio-visual speech recognition
EURASIP Journal on Applied Signal Processing
A two-channel training algorithm for hidden Markov model and its application to lip reading
EURASIP Journal on Applied Signal Processing
A new manifold representation for visual speech recognition
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Lip contour segmentation using kernel methods and level sets
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A technique for representing and learning smooth nonlinear manifolds is presented and applied to several lip reading tasks. Given a set of points drawn from a smooth manifold in an abstract feature space, the technique is capable of determining the structure of the surface and of finding the closest manifold point to a given query point. We use this technique to learn the "space of lips" in a visual speech recognition task. The learned manifold is used for tracking and extracting the lips, for interpolating between frames in an image sequence and for providing features for recognition. We describe a system based on hidden Markov models and this learned lip manifold that significantly improves the performance of acoustic speech recognizers in degraded environments. We also present preliminary results on a purely visual lip reader.