A fundamental problem of vision is how to deal with the astronomical complexity of images, scenes, and visual tasks. For example, considering the enormous input space of images and output space of objects, how can a human observer obtain a coarse interpretation of an image within less than 150 msec? And how can the observer, given more time, be able to parse the image into its components (objects, object parts, and scene structures) and reason about their relationships and actions? The same complexity problem arguably arises in most aspects of intelligence and addressing it is critical to understanding the brain and to designing artificial intelligence systems. This talk describes a research program which addresses this problem by using hierarchical compositional models which represent objects, and scene structures, in terms of elementary components which can be grouped together to form more complex structures, shared between different objects, and which are represented more abstractly in summary form. This program is illustrated by examples including: (i) low-level representations of images, (ii) segmentation and bottom-up attentional mechanisms, (iii) detection and parsing objects, (iv) estimating the 3D shapes of objects and scene structures from single images. We briely discuss ongoing work that relates these models to experimental studies of the brain, including psychophysics, elextrophysiology, and fMRI.
Professor Yuille is the Director of the UCLA Center for Cognition, Vision, and Learning, as well as a Professor at the UCLA Department of Statistics, with courtesy appointments at the Departments of Psychology, Computer Science, and Psychiatry. He is affiliated with the UCLA Staglin Center for Cognitive Neuroscience, the NSF Center for Brains, Minds and Machines, and the NSF Expedition in Visual Cortex On Silicon. His undergraduate degree was in Mathematics and his Phd in Theoretical Physics, both at the University of Cambridge. He has held appointments at MIT, Harvard, the Smith-Kettlewell Eye Research Institute, and UCLA. His research interests include computer vision, cognitive science, neural network modeling and machine learning. He has over three hundred peer reviewed publications. He has won several awards including the Marr prize and the Helmholtz test of time award. He is a fellow of IEEE.