Image sequence classification using a neural network based active contour model and a hidden Markov model

Abstract

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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>