more abstract on youtube
google engEdu Tech Talks Channel
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
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