We present a method to learn object class models from unlabeled and unsegmented
cluttered scenes for the purpose of visual object recognition. We focus on a particular type of …

We investigate a method for learning object categories in a weakly supervised manner.
Given a set of images known to contain the target category from a similar viewpoint, learning …

We consider learning models for object recognition from examples. Our method is motivated
by systems that use the Hausdorff distance as a shape comparison measure. Typically an …

In this paper we investigate a new method of learning part-based models for visual object
recognition, from training data that only provides information about class membership (and …

We present a method to learn and recognize object class models from unlabeled and
unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible …

In recent years there has been growing interest in recognition models using local image
features for applications ranging from long range motion matching to object class …

We propose a method to learn heterogeneous models of object classes for visual
recognition. The training images contain a preponderance of clutter and learning is …

We present a discriminative part-based approach for the recognition of object classes from
unsegmented cluttered scenes. Objects are modeled as flexible constellations of parts …

In this paper we present a computationally efficient framework for part-based modeling and
recognition of objects. Our work is motivated by the pictorial structure models introduced by …

We describe a flexible object recognition and modelling system (FORMS) which represents
and recognizes animate objects from their silhouettes. This consists of a model for …