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A viseme recognition system using lip curvature and neural networks to detect Bangla vowels
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This thesis report is submitted in partial fulfilment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2016.Abstract:
This thesis report is submitted in partial fulfilment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2016.read more
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Proceedings ArticleDOI
The Lip Position Analysis of the Main Consonant /w/ in Tibetan Xiahe Dialect
Dongxu Zhang,Hongzhi Yu,Ning Ma +2 more
TL;DR: This paper aims at systematically analyzing the labial positions of these syllables of consonant clusters that end with /w/ in Xiahe dialect with the help of the combination of MATLAB, Audition and VirtualDub software.
References
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Snakes : Active Contour Models
TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Book
Introduction to artificial neural systems
TL;DR: Jacek M. Zurada is a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky and has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits.
Book
Inference in Hidden Markov Models
TL;DR: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory, and builds on recent developments to present a self-contained view.
Reference EntryDOI
An introduction to hidden Markov models.
TL;DR: In this paper, the concept of hidden Markov models in computational biology is introduced and described using simple biological examples, requiring as little mathematical knowledge as possible, and an overview of their current applications are presented.