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We evaluate the robustness of five regression techniques for monocular 3D pose estimation. While most of the discriminative pose estimation methods focus on overcoming the fundamental problem of insufficient training data, we are interested in characterizing performance improvement for increasingly large training sets. Commercially available rendering software allows us to efficiently generate large numbers of realistic images of poses from diverse actions. Inspired by recent work in human detection, we apply PLS and kPLS regression to pose estimation. We observe that kPLS regression incrementally approximates GP regression using the strongest nonlinear correlations between image features and pose. This provides robustness, and our experiments show kPLS regression is more robust than two GP-based state-of-the-art methods for pose estimation. We address the ambiguity problem of pose estimation by random partitioning of the pose space and report results on the HumanEva dataset. | We evaluate the robustness of five regression techniques for monocular 3D pose estimation. While most of the discriminative pose estimation methods focus on overcoming the fundamental problem of insufficient training data, we are interested in characterizing performance improvement for increasingly large training sets. Commercially available rendering software allows us to efficiently generate large numbers of realistic images of poses from diverse actions. Inspired by recent work in human detection, we apply PLS and kPLS regression to pose estimation. We observe that kPLS regression incrementally approximates GP regression using the strongest nonlinear correlations between image features and pose. This provides robustness, and our experiments show kPLS regression is more robust than two GP-based state-of-the-art methods for pose estimation. We address the ambiguity problem of pose estimation by random partitioning of the pose space and report results on the HumanEva dataset. | ||
| + | |||
| + | ===Regularization and Localization for Prediction on Manifolds=== | ||
| + | Speaker: David Shaw -- Date: September 22, 2011 | ||
| + | |||
| + | In data analysis, one is interested in using the information about the response variable contained in the predictors in the best way possible. This can lead to problems when the predictors are highly collinear, as it implies an inherent lower-dimensional structure in the data. One method of analyzing data of this form is to make the assumption that these structured dependencies arise due to the predictors lying on some implicit lower-dimensional manifold. This assumption helps solve the problem of reducing the dimension of the predictors in the interest of removing some redundant information, but it introduces the problem of analyzing the transformed data. In particular, making accurate predictions with the lower-dimensional data that can be interpreted in the higher-dimensional space can be difficult. The technique of weighted regression with regularization on the model parameters can help to overcome these issues. | ||
Revision as of 00:59, 16 September 2011
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 the mailing list by following the instructions here.
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 Fall 2011[edit]
All talks take place Thursdays at 4pm in AVW 3450.
| Date | Speaker | Title |
|---|---|---|
| September 8 | Vishal Patel | Wavelets with Composite Dilations |
| September 15 | Radu Dondera | Kernel PLS Regression for Robust Monocular Pose Estimation |
| September 22 | Dave Shaw | Regularization and Localization for Prediction on Manifolds |
| September 29
(room 3165) |
Douglas Summerstay | |
| October 6 | Arpit Jain | |
| October 13 | Yi-Chen Chen | |
| October 20 | Anne Jorstad | |
| October 27 | Garrett Warnell | |
| November 3 | Abhishek Sharma | |
| November 10 | (ICCV, meeting TBD) | |
| November 17 | (no meeting, CVPR deadline 11/21) | |
| November 24 | (no meeting, Thanksgiving) | |
| December 1 | Nitesh Shroff | |
| December 8 | Ming-Yu Liu | |
| December 15 | (no meeting, final exams) |
Talk Abstracts Fall 2011[edit]
Wavelets with Composite Dilations[edit]
Speaker: Vishal Patel -- Date: September 8, 2011
Sparse representation of visual information lies at the foundation of many image processing applications, such as image restoration and compression. It is well known that wavelets provide a very sparse representation for a large class of signals and images. For instance, from a continuous perspective, wavelets can be shown to sparsely represent one-dimensional signals that are smooth away from point discontinuities. Unfortunately, separable wavelet transforms have some limitations in higher dimensions. For this reason, in recent years there has been considerable interest in obtaining directionally-oriented image decompositions. Wavelets with composite dilations offer a general and especially effective framework for the construction of such representations. In this talk, I will discuss the theory and implementation of several recently introduced multiscale directional transforms. Then, I will present a new general scheme for creating an M-channel directional filter bank. An advantage of an M-channel directional filter bank is that it can project the image directly onto the desired basis. Applications in image denoising, deconvolution and image enhancement will be presented.
Kernel PLS Regression for Robust Monocular Pose Estimation[edit]
Speaker: Radu Dondera -- Date: September 15, 2011
We evaluate the robustness of five regression techniques for monocular 3D pose estimation. While most of the discriminative pose estimation methods focus on overcoming the fundamental problem of insufficient training data, we are interested in characterizing performance improvement for increasingly large training sets. Commercially available rendering software allows us to efficiently generate large numbers of realistic images of poses from diverse actions. Inspired by recent work in human detection, we apply PLS and kPLS regression to pose estimation. We observe that kPLS regression incrementally approximates GP regression using the strongest nonlinear correlations between image features and pose. This provides robustness, and our experiments show kPLS regression is more robust than two GP-based state-of-the-art methods for pose estimation. We address the ambiguity problem of pose estimation by random partitioning of the pose space and report results on the HumanEva dataset.
Regularization and Localization for Prediction on Manifolds[edit]
Speaker: David Shaw -- Date: September 22, 2011
In data analysis, one is interested in using the information about the response variable contained in the predictors in the best way possible. This can lead to problems when the predictors are highly collinear, as it implies an inherent lower-dimensional structure in the data. One method of analyzing data of this form is to make the assumption that these structured dependencies arise due to the predictors lying on some implicit lower-dimensional manifold. This assumption helps solve the problem of reducing the dimension of the predictors in the interest of removing some redundant information, but it introduces the problem of analyzing the transformed data. In particular, making accurate predictions with the lower-dimensional data that can be interpreted in the higher-dimensional space can be difficult. The technique of weighted regression with regularization on the model parameters can help to overcome these issues.
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) |
