Statistical significance based graph cut segmentation for shrinking bias
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Efficient articulated trajectory reconstruction using dynamic programming and filters
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Analytical dynamic programming matching
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
A segmentation-free method for image classification based on pixel-wise matching
Journal of Computer and System Sciences
Real-Time exact graph matching with application in human action recognition
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
SCoBeP: Dense image registration using sparse coding and belief propagation
Journal of Visual Communication and Image Representation
Computer Vision and Image Understanding
FingerInk: turn your glass into a digital board
Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration
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Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting since, by carefully exploiting problem structure, they often provide nontrivial guarantees concerning solution quality. In this paper, we review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo, the mid-level problem of interactive object segmentation, and the high-level problem of model-based recognition.