Visual object recognition (“Is there a chair in this photograph?”) remains the grand challenge of computer vision. Any solution to this problem must account for the fact that the same object (or different instances of the same object class) may look very different from one image to the next, due to “external” causes (e.g., viewpoint or lighting changes), or “internal” variability (e.g., a Chevy Chevette and a Ferrari are both cars). In this context, I will first discuss a representation of individual three-dimensional (3D) objects in terms of small (planar) patches and their invariants, which, once combined with global geometric constraints, allows the automated acquisition of 3D object models from a small set of unregistered pictures, and their recognition in cluttered photographs taken from unconstrained viewpoints. I will then propose a probabilistic part-based approach to category-level object recognition, where each training image is represented by a set of features encoding the pattern of occurrences of semi-local parts (spatially coherent, distinctive groups of keypoints), and the posterior distribution of the class labels given this pattern is learned using a discriminative maximum entropy framework. Both approaches will be illustrated with extensive experiments.
Jean Ponce’s research focuses on computer vision (3D photography and object recognition) and robotics (grasp and manipulation planning). He received the Doctorat de Troisieme Cycle and Doctorat d’Etat degrees in Computer Science from the University of Paris Orsay in 1983 and 1988. He held Research Scientist positions at the Institut National de la Recherche en Informatique et Automatique (1981–1984), the MIT Artificial Intelligence Laboratory (1984–1985), and the Stanford University Robotics Laboratory (1985–1989). Since 1990, he has been with the Department of Computer Science and the Beckman Institute at the University of Illinois at Urbana-Champaign, where he is a Full Professor. Dr. Ponce is the author of over a hundred technical publications, including the textbook ``Computer Vision: A Modern Approach’’, in collaboration with David Forsyth. Dr. Ponce is Editor-in-Chief of the International Journal of Computer Vision, and was an Area Editor of Computer Vision and Image Understanding (1994–2000) and an Associate Editor of the IEEE Transactions on Robotics and Automation (1996–2001). He was Program Chair of the 1997 IEEE Conference on Computer Vision and Pattern Recognition and served as General Chair of the year 2000 edition of this conference. In 2003, he was named an IEEE Fellow for his contributions to Computer Vision, and he received a US patent for the development of a robotic parts feeder.