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Supervoxel-Based Segmentation of 3D Volumetric Images

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10111))

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Abstract

While computer vision has made noticeable advances in the state of the art for 2D image segmentation, the same cannot be said for 3D volumetric datasets. In this work, we present a scalable approach to volumetric segmentation. The methodology, driven by supervoxel extraction, combines local and global gradient-based features together to first produce a low level supervoxel graph. Subsequently, an agglomerative approach is used to group supervoxel structures into a segmentation hierarchy with explicitly imposed containment of lower level supervoxels in higher level supervoxels. Comparisons are conducted against state of the art 3D segmentation algorithms. The considered applications are 3D spatial and 2D spatiotemporal segmentation scenarios.

This work is partially supported by AFOSR under FA9550-15-1-0047.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, 32611, USA

    Chengliang Yang, Manu Sethi, Anand Rangarajan & Sanjay Ranka

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  1. Chengliang Yang
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  2. Manu Sethi
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  3. Anand Rangarajan
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  4. Sanjay Ranka
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Corresponding author

Correspondence to Chengliang Yang .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Yang, C., Sethi, M., Rangarajan, A., Ranka, S. (2017). Supervoxel-Based Segmentation of 3D Volumetric Images. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10111. Springer, Cham. https://doi.org/10.1007/978-3-319-54181-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-54181-5_3

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