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Yin, Pei Criminisi, Antonio Winn, John Essa, Irfan (2007), Tree-based Classifiers for Bilayer Video Segmentation In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR ’07, 17-22 June 2007, page(s): 1 – 8, Location: Minneapolis, MN, USA, ISBN: 1-4244-1180-7, Digital Object Identifier: 10.1109/CVPR.2007.383008 Abstract This paper presents an algorithm for the automatic segmentation of monocular videos […]
Yifan Shi, Bobick, A. Essa, I. (2006), “Learning Temporal Sequence Model from Partially Labeled Data” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006 Volume: 2, page(s): 1631 – 1638, ISSN: 1063-6919, ISBN: 0-7695-2597-0, Digital Object Identifier: 10.1109/CVPR.2006.174 [IEEEXplore] Abstract Graphical models are often used to represent and recognize activities. Purely […]
Huang, Y and Essa, I. (2005) “Tracking multiple objects through occlusions”, In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR 2005), Volume: 2 page(s): 1051 – 1058 vol. 2, ISSN: 1063-6919, ISBN: 0-7695-2372-2, INSPEC Accession Number:8633324 DOI: 10.1109/CVPR.2005.350, [IEEEXplore#] 20-25 June 2005 ABSTRACT We present an approach for tracking […]
Pei Yin Essa, I. Rehg, J.M. (2004) “Asymmetrically boosted HMM for speech reading,”, In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004 (CVPR 2004). Publication Date: 27 June-2 July 2004, Volume: 2, On page(s): II-755 – II-761 Vol.2 ISSN: 1063-6919, ISBN: 0-7695-2158-, INSPEC Accession Number:8161546, Digital Object Identifier: 10.1109/CVPR.2004.1315240 […]
Depth Layers from Occlusions [bibtex file=IrfanEssaWS.bib key=2001-Schodl-DLFO] Abstract We present a method to extract relative depth information from an uncalibrated monocular video sequence. Our method detects occlusions caused by an object moving in a static scene to infer relative depth relationships between scene parts. Our approach does not rely on any strong assumptions about the […]
Abstract We describe a computer vision system for observing the “action units” of a face using video sequences as input. The visual observation (sensing) is achieved by using an optimal estimation optical flow method coupled with a geometric and a physical (muscle) model describing the facial structure. This modeling results in a time-varying spatial patterning […]