1. today

2. you will entertain me

3. Enhancing the Throughput of Video Streaming Using Automatic Colorization Sender Automatic Colorization Internet Receiver

6. Spatial Pyramid GIST Scene Recognition Improvement Histogram on Geometry Segmentation Regions J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba SUN Database: Large-scale Scene Recognition from Abbey to Zoo CVPR2010

7. Content-Awareness Text Recognition 1. A piece of lecture video2. Identify the text region Linear Algebra Lecture 21 Eigenvalues – Eigenvectors Det [A - λI] = 0 TRACE = λ 1 + λ 2 + … + λ n

8. for privacy-preserving “opt-in” video services and dynamic layers My research group recently deployed an invasive sensor network to investigate issues such as privacy and narrative in the era of ubiquitous surveillance. Fine-grained RF localization and video matting Our first attempt at a privacy system was a blunt instrument– press the big red button on your personal tag to kill the sensors. There are two big problems with this approach: it is strictly “opt-out,” and one person’s preferences affect everyone around them. The next step is to create an opt-in system, in which users who wish to be seen carry a special RF tag and everyone else is invisible (replaced by background). This is made possible by recent advances in RF localization and computer vision.

9. for privacy-preserving “opt-in” video services and dynamic layers Fine-grained RF localization and video matting The first step is to implement the computer vision services necessary to make such a system work: Background estimation & video matting Motion estimation for image-tag correspondence 3-d from a single camera to deal with occlusions Correspondence across cameras with overlapping views

10. Generative Models for Texture Synthesis Lawson Wong Goals of generative-model based vision: (GMBV Workshop at CVPR 2004) –Formulate a model of image generation –Estimate posterior probability of model parameters given image Applications: Object detection, recognition, tracking … –All of vision! We can solve everything! But we haven’t solved everything yet –Perhaps we should look at a simpler case Proposal: Textures –Much more structure than images –Possibly forms a basic unit for understanding images –Project goal: Model and synthesize textures vs.

19. A Study of Motion Layer Segmentation Algorithms In this project I intend to experiment with motion layer segmentation algorithms. The purpose of motion segmentation is, given an image sequence, to decompose the sequence into layers of pixels, moving coherently through time as a result of an underlying process. Such algorithms typically operate on top of dense motion field estimation, and so it should be interesting to try and apply them using different optical flow algorithms. Another challenge lies on the actual methodology used to evaluate their results. To my knowledge, a comprehensive study that compares the results of motion segmentation algorithms to ground truth data has yet to be conducted (the only reference I found is [7]). The project will probably involve implementing 1-2 such algorithms and investigating their performance on synthetic and natural video data. I would like to start with the fundamentals, and have already implemented the simple approach described in [1]. I would also like to experiment with [2]/[3]/[4] and if time will allow with newer approaches such as [5]/[6]. Some references: [1] Y. Weiss, Motion Segmentation using EM - a short tutorial [2] J. Wang, E. Adelson, Representing Moving Images with Layers, ToIP 94 [3] Y. Weiss, E. Adelson, Perceptually organized EM: A framework for motion segmentation that combines information about form and motion, ICCV 95 [4] J. Shi, J. Malik, Motion Segmentation and Tracking Using Normalized Cuts, ICCV 98 [5] Q. Ke, T. Kanade, A Robust Subspace Approach to Layer Extraction, IEEE motion 02, [6] M. Kumar, P. Torr, A. Zisserman, Learning Layered Motion Segmentations of Video, IJCV 07 [7] L. Zappella et al., Motion Segmentation: a Review, Proceeding of the 2008 conference on Artificial Intelligence Research and Development 08

21. Computer Vision Final Project: Does multispectral imaging improve object recognition and segmentation? Roarke Horstmeyer Concept: There are many current approaches using RGB color values (sometimes in another space like YUV) to perform object segmentation and help with texture segmentation,. Also, RGB color content is combined with position info in algorithms like Mean Shift Segmentation. Hypothesis: Can using more than 3 overlapping color measurements improve segmentation algorithms? What is performance vs. # spectral bands? Approach: a. Modify k-means based algorithm, mean shift segmentation, and other algorithms to use additional spectral content. b. Test algorithms with 30 spectral channel shared database: http://www.cs.columbia.edu/CAVE/databases/multispectral/ http://www.cs.columbia.edu/CAVE/databases/multispectral/ c. Take own data with multiple spectral filters using a 5x5 camera array More colors Improved segmentation?

24. SIFT On the GPU

25. Automated Prediction of Consumer Response From Face and Gestures Can we predict the outcome of consumer tests from facial actions and gestures? Track Features Predict Response Which actions and gestures are salient, which are not? Can we determine from fewer tests whether the product will be a hit? METHODOLOGY: Track features Identify actions/gestures Map to response – like/dislike Validate against test set Evaluate performance Identify Actions/ Gestures