Generative Models for Image Analysis
- 2011-01-24 (Mon.), 10:30 AM
- 中研院-統計所 2F 交誼廳
- 茶 會:上午10:10統計所二樓交誼廳
- Dr. Lo-Bin Chang (張洛賓博士)
- Brown University
Abstract
??A probabilistic grammar for the grouping and labeling of parts and objects, when taken together with pose and part-dependent appearance models, constitutes a generative scene model and a Bayesian framework for image analysis. To the extent that the generative model generates features, as opposed to pixel intensities, the posterior distribution (i.e. the conditional distribution on part and object labels given the image) is based on incomplete information; feature vectors are generally insufficient to recover the original intensities. I will propose a way to learn pixel-level models for the appearances of parts.? I will demonstrate the utility of the models with some experiments in Bayesian image classification.