COS598/PSY594 Vision: From Neuronal Mechanisms to Computational Models

Spring 2008 by Fei-Fei Li

References

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Lecture #5:

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Visual Thinking with Graph Network
Jianbo Shi
Computer and Information Science
University of Pennsylvania

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.

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(supplementary) D. Lowe (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision.  60(2); 91-110.

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