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Computer Science @ Rochester
Friday, April 25, 2008
3:30 PM
Computer Studies Bldg. Room 632
Ph.D. Thesis Proposal
Paul Ardis
University of Rochester
Video Inpainting Using Human Attention Modeling
Inpainting techniques, for all that they have accomplished, continue to fail to satisfy. By refusing to quantify result quality and approximating human visual expectation in an ad-hoc fashion, current methods remain innovationally stagnant. Furthermore, modern methods that attempt to diverge from their traditional roots in data interpolation are faced with the risk of computational intractibility. I propose to improve this situation over the next two or three years by making use of human attentional modeling and statistical learning techniques, ultimately providing a clear indication of advancement in the field. Specifically, I intend to: 1.) learn a decision boundary between patch-based methods and interpolation methods using a modified Conditional Random Field, 2.) compile a high-resolution video dataset for training and benchmark comparison of methods in the field, 3.) analyze the quality and efficiency of results produced using the learned decision boundary and simple existing methods as options for selection, 4.) adjust existing salience models and produce a novel method of computation learned from natural statistics in existing eyetrack datasets, 5.) produce a novel interpolation method based upon a salience-guided analysis of global and local image statistics, and 6.) compare my work against a number of leading algorithms in a series of double-blind human subject experiments as well as studying computational efficiency.