We have designed and implemented a lipreading system that recognizes isolated words using only color video of human lips (without acoustic data). The system performs video recognition using "snakes" to extract visual features of geometric space, Karhunen-Loeve transform (KLT) to extract principal components in the color eigenspace, and hidden Markov models (HMM's) to recognize the combined visual features sequences. With the visual information alone, we were able to achieve 94% accuracy for ten isolated words.

We have designed and implemented a lipreading system which recognises isolated words using only color motion video of human lips (without acoustic data). The lipreading system performs color motion video recognition using "snakes" (active contour models), principal component analysis (PCA), and hidden Markov models (HMM). The snake algorithm and PCA are used to extract two sets of visual features from every frame (image) in the video sequence. The snake algorithm looks for contour features in the geometric space, while PCA seeks principal components in the eigenspace. An HMM recognizer is used to train and recognise a sequence of the combined visual features. With the visual information alone, we were able to achieve 94% recognition accuracy for 10 isolated words of a single speaker without using any special marker or lipstick.

Contour finding of distinct features in 2-D/3-D images is essential for image analysis and computer vision. To overcome the potential problems associated with existing contour finding algorithms, we propose a framework, called the neural network-based stochastic active contour model (NNS-SNAKE), which integrates a neural network classifier for systematic knowledge building, an active contour model (also known as the "Snake") for automated contour finding using energy functions, and the Gibbs sampler to help the snake to find the most probable contour using a stochastic decision mechanism. Successful application of the NNS-SNAKE to extraction of several types of contours on magnetic resonance (MR) images is presented.

Contour finding of distinct features in 2D/3D images is essential for image analysis and computer vision. To overcome the potential problems associated with existing contour finding algorithms, the authors propose a neural network based active contour model (NN-SNAKE), which integrates a neural network classifier for systematic knowledge building, and an active contour model (also known as "Snake") for automated contour finding using energy functions. The paper describes work on image sequence classification using the proposed NN-SNAKE and hidden Markov models. The "snake" model was applied to extract visual features from a sequence of mouth images and a hidden Markov model was applied to perform word recognition on the visual features. With the visual information alone, the authors were able to achieve 93% recognition rate for 11 isolated words. The models performed lip-reading in a hand-free car audio system.>

Describes an implementation of a processor node on a Texas Instruments EVM16 microprogrammable machine. In addition, the authors give an algorithm for performing morphological image processing-a commonly used image processing tool today-on such a distributed control architecture. Finally, they give an overview of a software simulator being implemented for simulating the whole pyramid machine. A simulator is essential for testing the working of the architecture and the algorithms before building the hardware.>