Title:
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos
Efficient Hierarchical Graph-Based Segmentation of RGBD Videos
| dc.contributor.author | Hickson, Steven | |
| dc.contributor.author | Birchfield, Stan | |
| dc.contributor.author | Essa, Irfan | |
| dc.contributor.author | Christensen, Henrik I. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Institute for Robotics and Intelligent Machines | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Interactive Computing | en_US |
| dc.contributor.corporatename | Microsoft Corporation | en_US |
| dc.date.accessioned | 2015-06-17T16:56:05Z | |
| dc.date.available | 2015-06-17T16:56:05Z | |
| dc.date.issued | 2014-06 | |
| dc.description | © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. | en_US |
| dc.description | DOI: 10.1109/CVPR.2014.51 | |
| dc.description.abstract | We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach. Our algorithm processes a moving window over several point clouds to group similar regions over a graph, resulting in an initial over-segmentation. These regions are then merged to yield a dendrogram using agglomerative clustering via a minimum spanning tree algorithm. Bipartite graph matching at a given level of the hierarchical tree yields the final segmentation of the point clouds by maintaining region identities over arbitrarily long periods of time. We show that a multistage segmentation with depth then color yields better results than a linear combination of depth and color. Due to its incremental processing, our algorithm can process videos of any length and in a streaming pipeline. The algorithm’s ability to produce robust, efficient segmentation is demonstrated with numerous experimental results on challenging sequences from our own as well as public RGBD data sets. | en_US |
| dc.embargo.terms | null | en_US |
| dc.identifier.citation | S. Hickson, S. Birchfield, I. Essa, and H. Christensen (2014), “Efficient Hierarchical Graph-Based Segmentation of RGBD Videos,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. | en_US |
| dc.identifier.doi | 10.1109/CVPR.2014.51 | |
| dc.identifier.uri | http://hdl.handle.net/1853/53657 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | |
| dc.subject | Clustering | en_US |
| dc.subject | Dendrogram | en_US |
| dc.subject | 4D segmentation | en_US |
| dc.subject | Grouping and shape representation | en_US |
| dc.subject | Point cloud segmentation | en_US |
| dc.subject | Segmentation | en_US |
| dc.title | Efficient Hierarchical Graph-Based Segmentation of RGBD Videos | en_US |
| dc.type | Text | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.author | Essa, Irfan | |
| local.contributor.author | Christensen, Henrik I. | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| local.contributor.corporatename | College of Computing | |
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