Abstract. We consider the problem of detecting a large number of different classes of objects in cluttered scenes. We present a learning procedure, based on boosted decision stumps...
Antonio B. Torralba, Kevin P. Murphy, William T. F...
Abstract. This paper presents a novel and efficient method for locating deformable shapes in cluttered scenes. The shapes to be detected may undergo arbitrary translational and rot...
In this paper we extend a method that uses image patch histograms and discriminative training to recognize objects in cluttered scenes. The method generalizes and performs well for...
Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research i...
Andrea Frome, Daniel Huber, Ravi Kolluri, Thomas B...
Given an unstructured collection of captioned images of cluttered scenes featuring a variety of objects, our goal is to learn both the names and appearances of the objects. Only a...
Michael Jamieson, Afsaneh Fazly, Sven J. Dickinson...
Both detection and tracking people are challenging problems, especially in complex real world scenes that commonly involve multiple people, complicated occlusions, and cluttered o...