Prithwijit Guha

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Occlusions are a central phenomenon in multi-object computer vision. However, formal analyses (LOS14, ROC20) proposed in the spatial reasoning literature ignore many distinctions crucial to computer vision, as a result of which these algebras have been largely ignored in vision applications. Two distinctions of relevance to visual computation are (a)(More)
Computational models of grounded language learning have been based on the premise that words and concepts are learned simultaneously. Given the mounting cognitive evidence for concept formation in infants, we argue that the availability of pre-lexical concepts (learned from image sequences) leads to considerable computational efficiency in word acquisition.(More)
Background subtraction is an essential task in several static camera based computer vision systems. Background modeling is often challenged by spatio-temporal changes occurring due to local motion and/or variations in illumination conditions. The background model is learned from an image sequence in a number of stages, viz. preprocessing, pixel/region(More)
—Commercial detection in news broadcast videos involves judicious selection of meaningful audiovisual feature combinations and efficient classifiers. And, this problem becomes much simpler if these combinations can be learned from the data. To this end, we propose an Multiple Kernel Learning based method for boosting successful kernel functions while(More)