The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Min-cut based segmentation of point clouds
Abstract
We present a min-cut based method of segmenting objects in point clouds. Given an object location, our method builds a k-nearest neighbors graph, assumes a background prior, adds hard foreground (and optionally background) constraints, and finds the min-cut to compute a foreground-background segmentation. Our method can be run fully automatically, or interactively with a user interface. We test our system on an outdoor urban scan, quantitatively evaluate our algorithm on a test set of about 1000 objects, and compare to several alternative approaches.
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Cites
Graph Cuts and Efficient N-D Image Segmentation
Boykov, Y., & Funka-Lea, G. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7934-5 · Cited by 1,910
Cited by
Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657
Metadata
- Authors
- Golovinskiy, A., & Funk, T.
- Venue
- 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 39-46
- OpenAlex
- W1996688648
- Cited by
- 293
- References
- 1
- FWCI
- 21.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Golovinskiy, A., & Funk, T. (2009). Min-cut based segmentation of point clouds. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. https://doi.org/10.1109/iccvw.2009.5457721
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