Paper in CVPR 2014 “Efficient Hierarchical Graph-Based Segmentation of RGBD Videos”

  • 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. [PDF] [WEBSITE] [BIBTEX]
    @InProceedings{    2014-Hickson-EHGSRV,
      author  = {Steven Hickson and Stan Birchfield and Irfan Essa
          and Henrik Christensen},
      booktitle  = {{Proceedings of IEEE Conference on Computer Vision
          and Pattern Recognition (CVPR)}},
      month    = {June},
      organization  = {IEEE Computer Society},
      pdf    = {},
      title    = {Efficient Hierarchical Graph-Based Segmentation of
          RGBD Videos},
      url    = {},
      year    = {2014}


We present an efficient and scalable algorithm for seg- menting 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 process- ing, 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.

Tags: , , , , , | Categories: Computer Vision, Henrik Christensen, Papers, Steven Hickson | Date: June 22nd, 2014 | By: Irfan Essa |

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