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Hits 81 – 92 of 92

81	Video Content Classification
	Phillip Decamp; Deb Roy
	In: http://www.ismll.uni-hildesheim.de/lehre/semML-09s/script/p183-fleischman.pdf
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82	Video Content Classification
	Phillip Decamp; Deb Roy
	In: http://web.media.mit.edu/~dkroy/papers/pdf/fleischman_decamp_2006.pdf
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83	Mining Temporal Patterns of Movement for Video Event Recognition Michael Fleischman Cognitive Machines Group
	In: http://www.media.mit.edu/cogmac/publications/MIR_06.pdf
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84	Manuscript statistics: Words: 14,000
	Peter Ford Dominey; Jean-david Boucher
	In: http://www.isc.cnrs.fr/dom/Dominey-AIJR5.pdf
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85	ERP Evidence for an Interaction between Phonological and Semantic Processes in Masked Priming Tasks
	Caroline Jacquier; Aïcha Rouibah
	In: http://www.ddl.ish-lyon.cnrs.fr/fulltext/Jacquier/Jacquier_2005_CogSci.pdf
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86	ERP Evidence for an Interaction between Phonological and Semantic Processes in Masked Priming Tasks
	Caroline Jacquier; Aïcha Rouibah
	In: http://www.psych.unito.it/csc/cogsci05/frame/poster/1/f718-jacquier.pdf
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87	ERP Evidence for an Interaction between Phonological and Semantic Processes in Masked Priming Tasks
	Caroline Jacquier; Aïcha Rouibah
	In: http://csjarchive.cogsci.rpi.edu/Proceedings/2005/docs/p1030.pdf
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88	Invited Paper A Hierarchical Framework for Understanding Human-Human
	Interactions In Video Surveillance
	In: http://www.cs.rochester.edu/u/spark/papers/Park_EI121_2005_invited.pdf
	Abstract: Understanding human behavior in video is essential in numerous applications including smart surveillance, video annotation/retrieval, and human- computer interaction. However, recognizing human interactions is a challenging task due to ambiguity in body articulation, variations in body size and appearance, loose clothing, mutual occlusion, and shadows. In this paper we present a framework for recognizing human actions and interactions in color video, and a hierarchical graphical model that unifies multiple-level processing in video computing: pixel level, blob level, object level, and event level. A mixture of Gaussian (MOG) model is used at the pixel level to train and classify individual pixel colors. A relaxation labeling with attribute relational graph (ARG) is used at the blob level to merge the pixels into coherent blobs and to register inter-blob relations. At the object level, the poses of individual body parts are recognized using Bayesian networks (BNs). At the event level, the actions of a single person are modeled using a dynamic Bayesian network (DBN). The results of the object-level descriptions for each person are juxtaposed along a common timeline to identify an interaction between two persons. The linguistic ‘verb argument structure ’ is used to represent human action in terms of triplets. A meaningful semantic description in terms of is obtained. Our system achieves semantic descriptions of positive, neutral, and negative interactions between two persons including hand-shaking, standing handin-hand, and hugging as the positive interactions, approaching, departing, and pointing as the neutral interactions, and pushing, punching, and kicking as the negative interactions.
	Keyword: event semantics; graphical model; human action; recognition; video surveillance
	URL: http://www.cs.rochester.edu/u/spark/papers/Park_EI121_2005_invited.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.9288
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89	Processing Visual Words With Numbers: Electrophysiological Evidence for Semantic Activation
	Lien, Mei-Ching; Allen, Philip A.; Martin, Nicole. - : Springer
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90	Electrophysiological Evidence of Different Loci for Case Mixing and Word Frequency Effects in Visual Word Recognition
	Lien, Mei-Ching; Allen, Philip A.; Crawford, Caitlin. - : Springer
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91	Visual information constrains early and late stages of spoken-word recognition in sentence context
	Brunellière, Angèle; Sánchez García, Carolina, 1984-; Ikumi Montserrat, Nara, 1986-. - : Elsevier
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92	Unraveling the mystery about the negative valence bias: does arousal account for processing differences in unpleasant words?
	Ferré Romeu, María del Pilar; Fraga Carou, Isabel; Vieitez Portas, Lucía. - : MDPI
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