Joint Priors for Variational Shape and Appearance Modeling

We are interested in modeling the variability of different images of the same scene, or class of objects, obtained by changing the imaging conditions, for instance the viewpoint or the illumination. Understanding of such a variability is key to reconstruction of objects despite changes in their appearance (e.g. due to non-Lambertian reflection), or to recognizing classes of objects (e.g. cars), or individual objects seen from different vantage points. We propose a model that can account for changes in shape or viewpoint, appearance, and also occlusions of line of sight. We learn a prior model of each factor (shape, motion and appearance) from a collection of samples using principal component analysis, akin a generalization of "active appearance models" to dense domains affected by occlusions. The ultimate goal of this work is stereo reconstruction in 3D, but first we have developed the first stage in this approach by addressing the simpler case of 2D shape/radiance detection...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Jeremy D. Jackson, Anthony J. Yezzi, Stefano Soatto
• Keywords: Active Appearance Models, Computer Vision, CVPR 2007, Individual Objects, Objects Despite Changes, Principal Component Analysis, Stereo Reconstruction