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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.

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Description

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 2012

All talks take place Thursdays at 4:30pm in AVW 3450.

Date	Speaker	Title
September 6	Angjoo Kanazawa	Face Alignment by Explicit Shape Regression
September 13	Sameh Khamis	Combining Per-Frame and Per-Track Cues for Multi-Person Action Recognition
September 20	Douglas Summerstay	Artificial Intelligence and Artificial Creativity Before 1900
September 27	Mohammad Rastegari	Attribute Discovery via Predictable and Discriminative Binary Codes
October 4	Xavier Gibert Serra	Anomaly Detection on Railway Components using Sparse Representations
October 11	(ECCV week, no meeting)
October 18	Ashish Srivastava
October 25	Yi-Chen Chen
November 1	Raviteja Vemulapalli
November 8	Sumit Shekhar
November 15	Ang Li
November 22	Arijit Biswas
November 29	Fatemeh Mir Rashed
December 6	Ejaz Ahmed
December 13	(Final exams, no meeting)

Talk Abstracts Fall 2012

Face Alignment by Explicit Shape Regression

Speaker: Angjoo Kanazawa -- Date: September 6, 2012

In this talk, we will go over CVPR 2012 paper "Face Alignment by Explicit Shape Regression". I will review the paper and discuss its key concepts: cascaded regression, random ferns, shape indexed image features, and correlation based feature selection. Then I will discuss our hypothesis on why this seemingly simple method works so well and how we can apply their method to similar problem domains such as dog and bird parts localization and their challenges.

Abstract from the paper: We present a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors over the training data. The inherent shape constraint is naturally encoded into the regressor in a cascaded learning framework and applied from coarse to fine during the test, without using a fixed parametric shape model as in most previous methods. To make the regression more effective and efficient, we design a two-level boosted regression, shape-indexed features and a correlation-based feature selection method. This combination enables us to learn accurate models from large training data in a short time (20 minutes for 2,000 training images), and run regression extremely fast in test (15 ms for a 87 landmarks shape). Experiments on challenging data show that our approach significantly outperforms the state-of-the-art in terms of both accuracy and efficiency.

Combining Per-Frame and Per-Track Cues for Multi-Person Action Recognition

Speaker: Sameh Khamis -- Date: September 13, 2012

We propose a model to combine per-frame and per-track cues for action recognition. With multiple targets in a scene, our model simultaneously captures the natural harmony of an individual's action in a scene and the flow of actions of an individual in a video sequence, inferring valid tracks in the process. Our motivation is based on the unlikely discordance of an action in a structured scene, both at the track level (e.g., a person jogging then dancing) and the frame level (e.g., a person jogging in a dance studio). While we can utilize sampling approaches for inference in our model, we instead devise a global inference algorithm by decomposing the problem and solving the subproblems exactly and efficiently, recovering a globally optimal joint solution in several cases. Finally, we improve on the state-of-the-art action recognition results for two publicly available datasets.

Artificial Intelligence and Artificial Creativity Before 1900

Speaker: Doug Summers-Stay -- Date: September 20, 2012

I will talk about various inventions such as the Eureka, which generated Latin poetry in hexameter while playing "God Save the Queen"; the Homeoscope, a mechanical search engine invented by a Russian police clerk in 1832; the Componium, an orchestra-in-a-box which composed random variations on a melody; and others along the same lines. I'll also talk about how we could go beyond these techniques to build something really creative. This is a presentation of material I found when I was doing research for the book I published in January, Machinamenta.

Attribute Discovery via Predictable and Discriminative Binary Codes

Speaker: Mohammad Rastegari -- Date: September 27, 2012

We present images with binary codes in a way that balances discrimination and learnability of the codes. In our method, each image claims its own code in a way that maintains discrimination while being predictable from visual data. Category memberships are usually good proxies for visual similarity but should not be enforced as a hard constraint. Our method learns codes that maximize separability of categories unless there is strong visual evidence against it. Simple linear SVMs can achieve state-of-the-art results with our short codes. In fact, our method produces state-of-the-art results on Caltech256 with only 128- dimensional bit vectors and outperforms state of the art by using longer codes. We also evaluate our method on ImageNet and show that our method outperforms state-of-the-art binary code methods on this large scale dataset. Lastly, our codes can discover a discriminative set of attributes.

Anomaly Detection on Railway Components using Sparse Representations

Speaker: Xavier Gibert-Serra -- Date: October 4, 2012

High-speed rail (HSR) requires high levels of reliability of the track infrastructure. Automated visual inspection is useful for finding many anomalies such as cracks or chips on joint bars and concrete ties, but existing vision-based inspection systems often produce high number of false detections, and are very sensitive to external factors such as changes in environmental conditions. For example, state-of-the-art algorithms used by the railroad industry nominally perform at a detection rate of 85% with a false alarm rate of 3% and performance drops very quickly as image quality degrades. On the tie inspection problem, this false alarm rate would correspond to 2.6 detections per second at 125 MPH, which cannot be handled by an operator. These false detections have many causes, including variations in anomaly appearance, texture, partial occlusion, and noise, which existing algorithms cannot handle very well. To overcome these limitations, it is necessary to reformulate this joint detection and segmentation problem as a Blind Source Separation problem, and use a generative model that is robust to noise and is capable of handling missing data.

In signal and image processing, Sparse Representations (SR) is an efficient way of describing a signal as a linear combination of a small number of atoms (elementary signals) from a dictionary. In natural images, sparsity arises from the statistical dependencies of pixel values across the image. Therefore, statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Independent Component Analysis (ICA) have been used for dimensionality reduction in several computer vision problems. Recent advances in SR theory have enabled methods that learn optimal dictionaries directly from training data. For example, K-SVD is a very well known algorithm for automatically designing over-complete dictionaries for sparse representation.

In this detection problem, the anomalies have very well defined structure and therefore, they can be represented sparsely in some subspace. In addition, the image background has very structured texture, so it is sparse with respect to a different frame. Theoretical results in mathematical geometric separation show that it is possible to separate these two image components (regular texture from contours) by minimizing the L1 norm the coefficients in geometrically complementary frames. More recently, it has been shown that this problem can be solved efficiently using thresholding and total variation regularization. Our experiments show that the sparse coefficients extracted from the contour component can be converted into feature vectors that can be used to cluster and detect these anomalies.

Past Semesters

• Schedule Spring 2012
• Schedule Fall 2011
• Schedule Summer 2011

Current Seminar Series Coordinators

Emails are at umiacs.umd.edu.

Angjoo Kanazawa, kanazawa@	(student of Professor David Jacobs)
Sameh Khamis, sameh@	(student of Professor Larry Davis)
Jie Ni, jni@	(student of Professor Rama Chellappa)
Ching Lik Teo, cteo@	(student of Professor Yiannis Aloimonos)

Gone but not forgotten.

Anne Jorstad, jorstad@	(student of Professor David Jacobs)
Sima Taheri, taheri@	(student of Professor Rama Chellappa)

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