Quadtree-based eigendecomposition for pose estimation in the presence of occlusion and background clutter

Includes bibliographical references (pages 29-30).Eigendecomposition-based techniques are popular for a number of computer vision problems, e.g., object and pose estimation, because they are purely appearance based and they require few on-line computations. Unfortunately, they also typically require an unobstructed view of the object whose pose is being detected. The presence of occlusion and background clutter precludes the use of the normalizations that are typically applied and significantly alters the appearance of the object under detection. This work presents an algorithm that is based on applying eigendecomposition to a quadtree representation of the image dataset used to describe the appearance of an object. This allows decisions concerning the pose of an object to be based on only those portions of the image in which the algorithm has determined that the object is not occluded. The accuracy and computational efficiency of the proposed approach is evaluated on 16 different objects with up to 50% of the object being occluded and on images of ships in a dockyard

Dynamic Appearance-Based Recognition

We describe a hierarchical appearance-based method for learning, recognizing, and predicting arbitrary spatiotemporal sequences of images. The method, which implements a robust hierarchical form of the Kalman filter derived from the Minimum Description Length (MDL) principle, includes as a special case several well-known object encoding techniques including eigenspace methods for static recognition. Successive levels of the hierarchical filter implement dynamic models operating over successively larger spatial and temporal scales. Each hierarchical level predicts the recognition state at a lower level and modifies its own recognition state using the residual error between the prediction and the actual lower-level state. Simultaneously, on a longer time scale, the filter learns an internal model of input dynamics by adapting its generative and state transition matrices at each level to minimize prediction errors. The resulting prediction /learning scheme thereby implements an on-line fo..