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Abstract: Bayesian statistics was applied to a small sample database of the tensile properties of five different brands of writing paper which were perceptibly similar. The measured parameters in the database were found to overlap for the five brands. This posed a limitation to the application of the classical approach for “match” or “no match”. It was found that using Bayesian statistics for source correspondence, a mere 2–3 measurements corresponding to a particular brand raised the probabilities associated with that brand to as high a 72% and eliminating a couple of brands. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the application of dynamic Bayesian networks (DBNs) to the recognition of degraded characters. DBNs are an extension of one-dimensional hidden Markov models (HMMs) which can handle several observation and state sequences. In our study, characters are represented by the coupling of two HMM architectures into a single DBN model. The interacting HMMs are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and image rows, respectively. Various couplings are proposed where interactions are achieved through the causal influence between state variables. We compare non-coupled and coupled models on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our models show that coupled architectures perform more accurately on degraded characters than basic HMMs, the linear combination of independent HMM scores, as well as discriminative methods such as support vector machines (SVMs). [Copyright &y& Elsevier]

Recognizing human interactions is a challenging task due to the multiple body parts of interacting persons and the concomitant occlusions. This paper presents a method for the recognition of two-person interactions using a hierarchical Bayesian network (BN). The poses of simultaneously tracked body parts are estimated at the low level of the BN, and the overall body pose is estimated at the high level of the BN. The evolution of the poses of the multiple body parts are processed by a dynamic Bayesian network (DBN). The recognition of two-person interactions is expressed in terms of semantic verbal descriptions at multiple levels: individual body-part motions at low level, single-person actions at middle level, and two-person interactions at high level. Example sequences of interacting persons illustrate the success of the proposed framework. [ABSTRACT FROM AUTHOR]