Estimation of Occlusion and Dense Motion Fields in a Bidirectional Bayesian Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time people localization and tracking through fixed stereo vision
Applied Intelligence
A Bayesian approach for segmentation in stereo image sequences
EURASIP Journal on Applied Signal Processing
Background updating with the use of intrinsic curves
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
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We present a ternary hypothesis test for the detection of stationary, moving, and uncovered-background pixels between two image frames in a noisy image sequence using the Bayes decision criterion. Unlike many uncovered-background detection schemes, our scheme does not require motion estimation for the differentiation between moving pixels and uncovered-background pixels. We formulate the Bayes decision rule using a single intensity-difference measurement at each pixel and using multiple intensity-difference measurements in the neighborhood of each pixel. We quantitatively evaluate our detection algorithm on an image sequence which we have generated and qualitatively on the Trevor White image sequence