PPT-Yuanlu Xu 20 12 Moving Object Segmentation by Pursuing Local Spatio-Temporal Manifolds

Problem Segmenting moving f oreground in a video Related work amp intuitions Dynamic background dynamic textures Image sequences of certain textures moving and changing under certain properties. Weakly labeled training videosTaggedw/ “dog”PositivesegmentsNegativeSegmentsSpatio-temporal segmentationoncept anking ccordingto egative xemplarsRanked positive segmentsEvaluate using precis CB.2 CB.1 CB.1 WR.1 WR.2 CB.1 WR.1 CB.2 WR.2 WR.1 WR.2 CB.1 CB.2 CB.1 WR.2 WR.1 Q.1 Q.1 Q.1 Q.1 Q.1 CB.1 WR.1 CB.2 Spatio-temporal Pattern Object Object-type Uniform Mixed Consecutive Discrete Flock Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother*, Philip H. S. Torr. Torr Vision Group, University of Oxford. *The . Technische Universität . Dresden. Traditional Goal. CB.2 CB.1 CB.1 WR.1 WR.2 CB.1 WR.1 CB.2 WR.2 WR.1 WR.2 CB.1 CB.2 CB.1 WR.2 WR.1 Q.1 Q.1 Q.1 Q.1 Q.1 CB.1 WR.1 CB.2 Spatio-temporal Pattern Object Object-type Uniform Mixed Consecutive Discrete Flock Oscar . Danielsson. (osda02@csc.kth.se). Stefan . Carlsson. (. stefanc@csc.kth.se. ). Josephine Sullivan (. sullivan@csc.kth.se. ). DICTA08. The Problem. Object categories are often modeled by collections (bag-of-features) or constellations (pictorial structures) of local features . Second-Order Pooling. João Carreira. 1,2. , Rui Caseiro. 1. , Jorge Batista. 1. , Cristian Sminchisescu. 2. 1. . Institute of Systems and Robotics. ,. . University of Coimbra. 2. . Faculty of Mathematics and Natural . René Vidal. Center for Imaging Science. Institute for Computational Medicine. Johns Hopkins University. Manifold Clustering with Applications to Computer Vision and Diffusion Imaging. René Vidal. Center for Imaging Science. Kaushik . Nandan. 1. Contents:. Introduction. Related . Work. Segmentation as Selective . Search. Object Recognition . System. Evaluation. Conclusions. References. 2. 1. Introduction. Object recognition: determining . Data Uncertainty: . Modeling and Querying. Mohamed F. Mokbel. Department of Computer Science and Engineering. University of Minnesota. www.cs.umn.edu/~mokbel. mokbel@cs.umn.edu. 2. Talk Outline. Introduction to Uncertain Data. Kaushik . Nandan. 1. Contents:. Introduction. Related . Work. Segmentation as Selective . Search. Object Recognition . System. Evaluation. Conclusions. References. 2. 1. Introduction. Object recognition: determining . -Temporal Data in. Massive Multiplayer Online Games. Matthias Schubert. joined. . work. . with. Hans-Peter Kriegel . and. Andreas . Züfle. Lehrstuhl für Datenbanksysteme. Institut für Informatik. in . different situations. Speed of . Movement. Measuring the speed of a moving object . in . different situations. Objective. The purpose of this activity is to analyze the change in the speed of an object in different situations, creating an hypothesis and proceeding to test it, using the . Erekle Shishniashvili. Seminar - Graph Deep Learning In Medical Imaging. Multi-head GAGNN: A Multi-head Guided Attention Graph Neural Network for Modeling . Spatio. -temporal Patterns of Holistic Brain Functional Networks. Raghu Machiraju. Firdaus. . Janoos. , Fellow, Harvard Medical. Istavan. (. Pisti. ) . Morocz. , . Instuctor. , Harvard . Medical. Premise. Understanding the mind not only requires a comprehension of the workings of low–level neural networks but also demands a detailed map of the brain’s functional architecture and a description of the large–scale connections between populations of neurons and insights into how relations between these simpler networks give rise to higher–level thought.