Titlebook: Computer Vision - ACCV 2010; 10th Asian Conferenc Ron Kimmel,Reinhard Klette,Akihiro Sugimoto Conference proceedings 2011 Springer Berlin H

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Stream-Based Active Unusual Event Detection methods that perform passive mining for unusual events, our approach automatically requests supervision for critical points to resolve ambiguities of interest, leading to more robust and accurate detection on subtle unusual events. The active learning strategy is formulated as a stream-based soluti

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Asymmetric Totally-Corrective Boosting for Real-Time Object Detectiontandard for this problem. In this framework, the learning goal for each node is asymmetric, which is required to achieve a high detection rate and a moderate false positive rate. We develop new boosting algorithms to address this asymmetric learning problem. We show that our methods explicitly optim

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The Application of Vision Algorithms to Visual Effects Productioneir early ages, it still requires constant innovation and in many cases a great amount of craftsmanship. Given these premises, most VFX companies (especially those at the leading edge) are hungry for novel and better solutions to the problems they encounter everyday. Many published machine vision al

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Finding Human Poses in Videos Using Concurrent Matching and Segmentationr image, the proposed method detects the poses of a specific human subject in long video sequences. Matching and segmentation support each other and therefore the simultaneous optimization enables more reliable results. However, efficient concurrent optimization is a great challenge due to its huge

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Deformable Object Modelling and Matchingve an overview of such models and of two efficient algorithms for matching such models to new images (Active Shape Models and Active Appearance Models). We also describe recent work on automatically constructing such models from minimally labelled training images.