Greed is good: approximating independent sets in sparse and bounded-degree graphs
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Self-Calibration of Stationary Cameras
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Vision and Applications
Self-Calibration of Rotating and Zooming Cameras
International Journal of Computer Vision
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Robust Video Mosaicing through Topology Inference and Local to Global Alignment
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A Graph-Based Global Registration for 2D Mosaics
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
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Applying graph-theoretic concepts to solve computer vision problems makes it not only trivial to analyze the complexity of the problem at hand, but also existing algorithms from the graph-theory literature can be used to find a solution. We consider the challenging tasks of frame selection for use in mosaicing, and feature selection from Computer Vision, andMachine Learning, respectively, and demonstrate that we can map these problems into the existing graph theory problem of finding the maximum independent set. For frame selection, we represent the temporal and spatial connectivity of the images in a video sequence by a graph, and demonstrate that the optimal subset of images to be used in mosaicing can be determined by finding the maximum independent set of the graph. This process of determining the maximum independent set, not only reduces the overhead of using all the images, which may not be significantly contributing in building the mosaic, but also implicitly solves the "camera loop-back" problem. For feature selection, we conclude that we can apply a similar mapping to the maximum independent set problem to obtain a solution. Finally, to demonstrate the efficacy of our frame selection method, we build a system for mosaicing, which uses our method of frame selection.