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To receive regular information about the Computer Vision Student Seminar, subscribe to the mailing list by following the instructions [https://mailman.cs.umd.edu/mailman/listinfo/cvss here].
To receive regular information about the Computer Vision Student Seminar, subscribe to the mailing list by following the instructions [https://mailman.cs.umd.edu/mailman/listinfo/cvss here].
==Current Seminar Series Coordinators==
Emails are at umiacs.umd.edu.
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| Anne Jorstad, jorstad@
| (student of Professor David Jacobs)
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| Sameh Khamis, sameh@
| (student of Professor Larry Davis)
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| Sima Taheri, taheri@
| (student of Professor Rama Chellappa)
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| Ching Lik Teo, cteo@
| (student of Professor Yiannis Aloimonos)
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==Schedule Fall 2011==
All talks take place Thursdays at 4pm in AVW 3450.
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| September 8
| Vishal Patel
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| September 15
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| September 22
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| September 29
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| October 6
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| October 13
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| October 20
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| October 27
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| November 3
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| November 10
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| November 17
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| November 24
| (no meeting, Thanksgiving)
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| December 1
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| December 8
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| December 15
| (no meeting, final exams)
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==Talk Abstracts Fall 2011==
| September 1
| September 1
| (no meeting)
| (no meeting)
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==Talk Abstracts==
==Talk Abstracts Summer 2011==
===Multi-Agent Event Recognition in Structured Scenarios===
Speaker: [http://www.umiacs.umd.edu/~morariu/ Vlad Morariu] -- Date: June 9, 2011
Speaker: [http://www.umiacs.umd.edu/~morariu/ Vlad Morariu] -- Date: June 9, 2011
for computer vision applications. Recently, new class of models, Conditional Random Fields (CRF), has been introduced. Although CRFs were first introduced for labeling 1D sequences, they have also been incorporated for 2D images for applications such as labeling and object recognition. Another model, Discriminative Random Field (DRF) model, inspired by CRF, has been applied successfully for image denoising and labeling. In this talk, the key differences between MRF and CRF/DRF will be highlighted. The main diff erence between the two classes of models can be best understood on the basis of generative vs .discriminative probabilistic models based on graphs. Hence, graphical models will also be briefly discussed in the talk.
for computer vision applications. Recently, new class of models, Conditional Random Fields (CRF), has been introduced. Although CRFs were first introduced for labeling 1D sequences, they have also been incorporated for 2D images for applications such as labeling and object recognition. Another model, Discriminative Random Field (DRF) model, inspired by CRF, has been applied successfully for image denoising and labeling. In this talk, the key differences between MRF and CRF/DRF will be highlighted. The main diff erence between the two classes of models can be best understood on the basis of generative vs .discriminative probabilistic models based on graphs. Hence, graphical models will also be briefly discussed in the talk.