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Over the years, the spatial resolution of cameras has steadily increased but the temporal resolution has remained the same. In this talk, I will present my work on converting a regular slow camera into a faster one. We capture and accurately reconstruct fast events using our slower prototype camera by exploiting the temporal redundancy in videos. First, I will show how by fluttering the shutter during the exposure duration of a slow 25fps camera we can capture and reconstruct a fast periodic video at 2000fps. Next, I will present its generalization where we show that per-pixel modulation during exposure, in combination with brightness constancy constraints allows us to capture a broad class of motions at 200fps using a 25fps camera. In both these techniques we borrow ideas from compressive sensing theory for acquisition and recovery. | Over the years, the spatial resolution of cameras has steadily increased but the temporal resolution has remained the same. In this talk, I will present my work on converting a regular slow camera into a faster one. We capture and accurately reconstruct fast events using our slower prototype camera by exploiting the temporal redundancy in videos. First, I will show how by fluttering the shutter during the exposure duration of a slow 25fps camera we can capture and reconstruct a fast periodic video at 2000fps. Next, I will present its generalization where we show that per-pixel modulation during exposure, in combination with brightness constancy constraints allows us to capture a broad class of motions at 200fps using a 25fps camera. In both these techniques we borrow ideas from compressive sensing theory for acquisition and recovery. | ||
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===Exploring Context in Unsupervised Object Identification Scenarios=== | ===Exploring Context in Unsupervised Object Identification Scenarios=== | ||
Revision as of 22:33, 18 July 2011
Computer Vision Student Seminar
The Computer Vision Student Seminar at the University of Maryland College Park is a student-run series of talks given by current graduate students for current graduate students.
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.
Format[edit]
- 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.
Subscribe to the Mailing List[edit]
To receive regular information about the Computer Vision Student Seminar, subscribe to the mailing list by following the instructions here.
Schedule Summer 2011[edit]
All talks take place Thursdays at 4pm in AVW 3450.
| Date | Speaker | Title |
|---|---|---|
| June 9 | Vlad Morariu | Multi-Agent Event Recognition in Structured Scenarios |
| June 16 | Ajay Mishra | A Vision System to Extract "Simple" Objects in a Purely Bottom-Up Fashion |
| June 23 | (no meeting, CVPR) | |
| June 30 | Dikpal Reddy | Fast Imaging with Slow Cameras |
| July 7 | Raghuraman Gopalan | Exploring Context in Unsupervised Object Identification Scenarios |
| July 14 | Behjat Siddiquie | Utilizing Contextual Information for Scene Understanding and Image Retrieval |
| July 21 | Kaushik Mitra | Robust Regression Using Sparse Learning |
| July 28 | ||
| August 4 | Carlos Castillo | |
| August 11 | ||
| August 18 | ||
| August 25 |
Talk Abstracts[edit]
Multi-Agent Event Recognition in Structured Scenarios[edit]
Speaker: Vlad Morariu -- Date: June 9, 2011
I will present a framework for the automatic recognition of complex multi-agent events in settings where structure is imposed by rules that agents must follow while performing activities. Given semantic spatio-temporal descriptions of what generally happens (i.e., rules, event descriptions, physical constraints), and based on video analysis, the framework determines the events that occurred. Knowledge about spatio-temporal structure is encoded using first-order logic using an approach based on Allen's Interval Logic, and robustness to low-level observation uncertainty is provided by Markov Logic Networks (MLN). The main contribution is that the framework integrates interval-based temporal reasoning with probabilistic logical inference, relying on an efficient bottom-up grounding scheme to avoid combinatorial explosion. Applied to one-on-one basketball, the framework detects and tracks players, their hands and feet, and the ball, generates event observations from the resulting trajectories, and performs probabilistic logical inference to determine the most consistent sequence of events.
A Vision System to Extract "Simple" Objects in a Purely Bottom-Up Fashion[edit]
Speaker: Ajay Mishra -- Date: June 16, 2011
Human perception, being active, is inextricably linked to visual fixation. Despite the obvious importance of fixation, it has not become an integral part of computer vision/robotics algorithms so far. To incorporate fixation and attention in a computer vision framework, we have proposed a new segmentation framework that takes a fixation point (i.e a single point) inside a "simple" object as its input and outputs the region corresponding to that object. We have also designed a new attentional mechanism that utilizes the concept of neural border-ownership to automatically select the fixation points inside different "simple" objects in the scene. All of this together creates a fully automatic system that outputs only the regions corresponding to the "simple" objects without knowing the actual number or the size of the objects in the scene.
Using these regions, instead of rectangular patches of fixed sizes, to analyze the content of a scene will result in better performance (in terms of accuracy and robustness to noise) for high-level vision algorithms such as object recognition, object manipulation, and action analysis. A variety of experimental results will conclude the talk.
Also, to understand the role of fixation in perception, Ajay recommends taking the psychophysical test available at http://www.umiacs.umd.edu/~mishraka/fixationExperiment.php
Fast Imaging with Slow Cameras[edit]
Speaker: Dikpal Reddy -- Date: June 30, 2011
Over the years, the spatial resolution of cameras has steadily increased but the temporal resolution has remained the same. In this talk, I will present my work on converting a regular slow camera into a faster one. We capture and accurately reconstruct fast events using our slower prototype camera by exploiting the temporal redundancy in videos. First, I will show how by fluttering the shutter during the exposure duration of a slow 25fps camera we can capture and reconstruct a fast periodic video at 2000fps. Next, I will present its generalization where we show that per-pixel modulation during exposure, in combination with brightness constancy constraints allows us to capture a broad class of motions at 200fps using a 25fps camera. In both these techniques we borrow ideas from compressive sensing theory for acquisition and recovery.
Exploring Context in Unsupervised Object Identification Scenarios[edit]
Speaker: Raghuraman Gopalan -- Date: July 7, 2011
The utility of context for supervised object recognition has been well acknowledged from the early seventies, and has been practically demonstrated by many systems in the last few years. The goal of this talk is to understand the role of context in unsupervised pattern identification scenarios. We consider two problems of clustering a set of unlabelled data points using maximum margin principles, and adapting a classifier trained on a specific domain to identify instances across novel domain shifting transformations, and propose contextual sources that provide pertinent information on the identity of the unlabelled data.
Utilizing Contextual Information for Scene Understanding and Image Retrieval[edit]
Speaker: Behjat Siddiquie -- Date: July 14, 2011
In many vision tasks, contextual information can often help disambiguate confusions arising from appearance information. In this talk, I will discuss two different works, which deal with effective utilization of contextual information to improve the performance of active learning for scene understanding and multi-attribute based image retrieval.
First, I will propose an active learning framework to simultaneously learn appearance and contextual models for scene understanding tasks (multi-class classification). Current multi-class active learning approaches ignore the contextual interactions between different regions of an image and the fact that knowing the label for one region provides information about the labels of other regions. We explicitly model the contextual interactions between regions and select the question which leads to the maximum reduction in the combined entropy of all the regions in the image (image entropy).
Next, I will present a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multi-word queries. Moreover, these approaches ignore the interdependencies among the query words. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval.
Robust Regression Using Sparse Learning[edit]
Speaker: Kaushik Mitra -- Date: July 21, 2011
Robust regression is a combinatorial optimization problem. Hence, algorithms such as RANSAC and least median squares (LMedS), which are successful in solving low-dimensional problems, can not be used for solving high-dimensional problems. We show that under certain conditions the robust linear regression problem can be solved accurately using polynomial-time algorithms such as a modified version of basis pursuit and a sparse Bayesian algorithm. We then extend our robust formulation to the case of kernel regression, specifically to propose a robust version for relevance vector machine (RVM) regression.
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) |
Wiki Editing[edit]
Consult the User's Guide for information on using the wiki software.
