Classification-based Probabilistic Modeling of Texture Transition for Fast Line Search Tracking and Delineation

We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak lear ners which operate on image intensity discriminative features which are defined on small patches and fast to compute. A database which is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak lear ners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions which can readily be used for line search texture boundar y detection in the direction normal to an initial boundar y estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator which requires a ribbon like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2-D delineation and fast 3-D tracking and compare its perfor mance with other existing methods for line search boundary detection.