Shape Priors and Online Appearance Learning for Variational Segmentation and Object Recognition in Static Scenes

We present an integrated two-level approach to computationally analyzing image sequences of static scenes by variational segmentation. At the top level, estimated models of object appearance and background are probabilistically fused to obtain an a-posteriori probability for the occupancy of each pixel. The data-association strategy handles object occlusions explicitly.

At the lower level, object models are inferred by variational segmentation based on image data and statistical shape priors. The use of shape priors allows to distinguish between recognition of known objects and segmentation of unknown objects. The object models are sufficiently flexible to enable the integration of general cues like advanced shape distances. At the same time, they are highly constrained from the optimization viewpoint: the globally optimal parameters can be computed at each time instant by dynamic programming.

The novelty of our approach is the integration of state-of-the-art variational segmentation into a probabilistic framework for static scene analysis that combines both on-line learning and prior knowledge of various aspects of object appearance.