Visual Thinking with Graph Network Jianbo Shi Assistant Professor Computer and Information Science University of Pennsylvania Time/Location: 1:30pm Mon Mar 3, CS 301 Many visual perception tasks are fundamentally NP-hard computational problems. Solving these problems robustly requires thinking through combinatorially many hypothesis. Despite this, our human visual system performs these tasks effortlessly. How is this done? I would like to make two points on this topic. First, formulating visual thinking as NP-hard computation tasks has an important advantage: visual routines can be analyzed precisely to identify their behaviors independently of their implementations. Second, I will show there is a class of graph optimization problems which can be implemented using a distributed network system with physical (and plausible biological) interpretation. I will demonstrate this graph based approach for: 1) image segmentation using Normalized Cuts with explanations for illusory contours, visual pop out and attention; 2) salient contour grouping using Untangling Cycle; and 3) contour context selection for shape detection. -- --------------------------------------------- Li, Fei-Fei Ph.D. (publish under L. Fei-Fei) Assistant Professor Computer Science Dept. Princeton University 35 Olden St. Princeton, NJ 08540 Tel: (609)258-8130 Office: Room 211, CS Building Website: http://vision.cs.princeton.edu ---------------------------------------------