This paper presents a Bayesian network based multimodal fusion method for robust and real-time face tracking. The Bayesian network integrates a prior of second order system dynamics, and the likelihood cues from color, edge and face appearance. While different modalities have different confidence scales, we encode the environmental factors related to the confidences of modalities into the Bayesian network, and develop a Fisher discriminant analysis method for learning optimal fusion. The face tracker may track multiple faces under different poses. It is made up of two stages. First hypotheses are efficiently generated using a coarse-tofine strategy; then multiple modalities are integrated in the Bayesian network to evaluate the posterior of each hypothesis. The hypothesis that maximizes a posterior (MAP) is selected as the estimate of the object state. Experimental results demonstrate the robustness and real-time performance of our face tracking approach. 1.

A dynamic Bayesian network (DBN) is a probabilistic network that models interdependent entities that change over time. Given example sequences of multivariate data, we use a genetic algorithm to synthesize a network structure that models the causal relationships that explain the sequence. We use a multi-objective evaluation strategy with a genetic algorithm. The multi-objective criteria are a network’s probabilistic score and structural complexity score. Our use of Pareto ranking is ideal for this application, because it naturally balances the effect of the likelihood and structural simplicity terms used in the BIC network evaluation heuristic. We use a simple structural scoring formula, which tries to keep the number of links in the network approximately equivalent to the number of variables. We also use a simple representation that favours sparsely connected networks similar in structure to those modeling biological phenomenon. Our experiments show promising results when evolving networks ranging from 10 to 30 variables, using a maximal connectivity of between 3 and 4 parents per node. The results from the multi-objective GA were superior to those obtained with a single objective GA. Key words: dynamic Bayesian networks, multi-objective optimization, genetic algorithms

Abstract: As to the soccer video, the event is defined as the medium-level spatiotemporal entity interesting to users, having certain context cues corresponding to the specific domain knowledge model. As a medium-level entity, the inference of soccer event is based on the fusion of context cues and domain knowledge model. The shooting event is chosen as research target and the event analysis method is expected to be reusable for other soccer events. According to the analysis of shooting event, the following seven kinds of context cues are extracted, respectively including one kind of caption detection, two kinds of face detection, one kind of audience detection, one kind of goal detection, and two kinds of motion estimation. In the inference of soccer event Bayesian network is used to perform the fusion of context cues. In the experiments the event retrieval is performed based on the video data of World Cup 2002, and the results show that the key to event retrieval is the extraction of context cues related with the user-defined event closely. Key words: soccer video; event retrieval; context cues; Bayesian network 1.

Knowledge- based system (KBS) has been widely used in the detection and interpretation of EEG based neuropsychiatric diseases. Heuristic-based detection methods of EEG (Electroencephalography) parameters for a particular disease have been reported in the literature but little effort has been made by researchers to combine rule-based reasoning (RBR) and probabilistic method i.e Bayesian method. A combined method improves the computational and reasoning efficiency of the problem- solving strategy. We have hierarchically structured the neuropsychiatric diseases in terms of their physio-pyscho (physical, cognitive and psychological) parameters and EEG and FMRI (Functional magnetic resonance imaging) based parameters. RBR model use to create Bayesian network for each disease. The diseases considered are ADHD, Dementia, Mood Disorder, OCD and SI. The basic objective of this work is to develop an intelligent method of RBR and Bayesian model in which RBR is used to hierarchical correlate sign and symptom of the disease and also compute probabilities of diseases. Bayesian method is used for diagnosing the neuropsychiatric diseases and to find the probability of relative importance of sign and symptoms of diseases to other diseases.