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In this tutorial we describe how several computer vision problems can be intuitively formulated as Markov Random Fields. Inference in such models can be transformed to an energy minimization problem. Under some conditions, graph cut methods can be used to find the minimum of the energy function and, in turn, the most probable assignment for its variables. In addition, we will briefly cover some of the recent advances in the application of graph cuts to a wider set of energy functions. | In this tutorial we describe how several computer vision problems can be intuitively formulated as Markov Random Fields. Inference in such models can be transformed to an energy minimization problem. Under some conditions, graph cut methods can be used to find the minimum of the energy function and, in turn, the most probable assignment for its variables. In addition, we will briefly cover some of the recent advances in the application of graph cuts to a wider set of energy functions. | ||
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==Past Semesters== | ==Past Semesters== | ||
Revision as of 15:12, 15 February 2012
Computer Vision Student Seminars
The Computer Vision Student Seminars at the University of Maryland College Park are a student-run series of talks given by current graduate students for current graduate students.
To receive regular information about the Computer Vision Student Seminars, subscribe to our mailing list or our talks list.
Description[edit]
The purpose of these talks is to:
- Encourage interaction between computer vision students;
- Provide an opportunity for computer vision students to be aware of and possibly get involved in the research their peers are conducting;
- Provide an opportunity for computer vision students to receive feedback on their current research;
- Provide speaking opportunities for computer vision students.
The guidelines for the format are:
- An hour-long weekly meeting, consisting of one 20-40 minute talk followed by discussion and food.
- The talks are meant to be casual and discussion is encouraged.
- Topics may include current research, past research, general topic presentations, paper summaries and critiques, or anything else beneficial to the computer vision graduate student community.
Schedule Spring 2012[edit]
All talks take place Thursdays at 4pm in AVW 3450.
| Date | Speaker | Title |
|---|---|---|
| February 2 | Ching Lik Teo | The Telluride Neuromorphic Workshop Experience |
| February 9 | Jonghyun Choi | A Complementary Local Feature Descriptor for Face Identification (CCS-POP) |
| February 16 | Sameh Khamis | Energy Minimization with Graph Cuts |
| February 23 | Leonardo Claudino | |
| March 1 | (ECCV week, no meeting) | |
| March 8 | Huimin Guo | |
| March 15 | Jie Ni | |
| March 22 | (Spring Break) | |
| March 29 | Daozheng Chen | |
| April 5 | Jaishanker Pillai | |
| April 12 | Jun-Cheng Chen | |
| April 19 | Sima Taheri | |
| April 26 | Sujal Bista | |
| May 3 | Nazre Batool | |
| May 10 | Stephen Xi Chen | |
| May 17 | (no meeting, final exams) |
Talk Abstracts Spring 2012[edit]
The Telluride Neuromorphic Workshop Experience[edit]
Speaker: Ching Lik Teo -- Date: February 2, 2012
In this talk, I will present what we did as a group at the Telluride Neuromorphic Workshop 2011. I will explain the challenges we faced, modules that we have used, and some results from experiments on activity description we have conducted on the robot.
A Complementary Local Feature Descriptor for Face Identification (CCS-POP)[edit]
Speaker: Jonghyun Choi -- Date: February 9, 2012
In many descriptors, spatial intensity transforms are often packed into a histogram or encoded into binary strings to be insensitive to local misalignment and compact. Discriminative information, however, might be lost during the process as a trade-off. To capture the lost pixel-wise local information, we propose a new feature descriptor, Circular Center Symmetric-Pairs of Pixels (CCS-POP). It concatenates the symmetric pixel differences centered at a pixel position along various orientations with various radii; it is a generalized form of Local Binary Patterns, its variants and Pairs-of-Pixels (POP). Combining CCS-POP with existing descriptors achieves better face identification performance on FRGC Ver. 1.0 and FERET datasets compared to state-of-the-art approaches.
Energy Minimization with Graph Cuts[edit]
Speaker: Sameh Khamis -- Date: February 16, 2012
In this tutorial we describe how several computer vision problems can be intuitively formulated as Markov Random Fields. Inference in such models can be transformed to an energy minimization problem. Under some conditions, graph cut methods can be used to find the minimum of the energy function and, in turn, the most probable assignment for its variables. In addition, we will briefly cover some of the recent advances in the application of graph cuts to a wider set of energy functions.
Past Semesters[edit]
Current Seminar Series Coordinators[edit]
Emails are at umiacs.umd.edu.
| Anne Jorstad, jorstad@ | (student of Professor David Jacobs) |
| Sameh Khamis, sameh@ | (student of Professor Larry Davis) |
| Sima Taheri, taheri@ | (student of Professor Rama Chellappa) |
| Ching Lik Teo, cteo@ | (student of Professor Yiannis Aloimonos) |
