Binarization is reduced to function approximation in terms of quantization which is overlaid either with a recursion scheme or with discrete scale-space filtering. Approximations are solved to optimality not requiring any parametric input except that there be two segment classes of the image.

We investigate the question of how the visual information of lip movement contributes to command-word recognition. The fusion of the acoustic and visual signal can be carried out either at the feature level or at the class level. Integration at the feature level means merging of the acoustic and visual features to yield a combined feature vector which is fed into a HMM-system. Fusion at the class level means separate classification of the two sources of information and combination of the classification results. An HMM classifier is used for the acoustic signal and three different classifiers (HMM, DTW and ClaRe) for the visual signal. The classification results are combined using the C4.5 decision tree classifier. The recognition rates of both fusion schemes are comparable. Both yield small improvements at high SNRs using the acoustic/visual system in comparison to the acoustic system alone. Larger improvements (up to 12%) result at low SNRs.

Two different approaches to the detection and representation of the mouth in real video images of the human face are investigated. The model-based approach presented is based on a technique known as "deformable templates", and tries to approximate the contours of the lips with a model consisting of four parabolas. An alternative to the model-based approach, referred to as the knowledge-guided approach, is proposed. The basic idea is not to try to capture all of the a priori knowledge about an object in a single global model that is adapted to the image, but rather to utilize the a priori knowledge in a step-by-step way, in order to refine rough initial hypotheses into a compact description of the object. This method may be interpreted as a gradual concentration on the relevant structures in the image. The combination of the resulting structures yields a compact description of the object. In the application, which is the basis for this investigation, the goal is to enhance speech recognition by using visual information about lip movements in addition to the acoustic signal. Only the problem of finding an accurate and robust representation of the lips in an image is addressed. Each of the methods were investigated for the same set of 15 faces. Our experiments indicate that the knowledge-guided approach performs more accurately and more robustly, than the model-based approach.>

Based on the paradigm of collective learning systems, ALIAS (adaptive learning image analysis system) is an adaptive image-processing engine specifically designed to detect anomalies in otherwise normal images and signals. To accomplish this, ALIAS requires only one pass through a training set, which typically consists of less than 100 samples. The original version of ALIAS

The presented multimicroprocessor system has been designed to treat certain problem classes such as large systems of differential equations or online process control. It consists of a main processor and an arbitrary number of modules, each with a microprocessor, a private memory and a communication memory. The most characteristic features of the SMS 101 organization are: