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IFOC: Intensity Fitting on Overlapping Cover for Image Segmentation

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

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Abstract

Region-based image segmentation methods often require a global statistical description of the image intensities in the entire foreground and background, which however is in general not available for real world images due to the complicated intensity variation within the regions of interest. In this paper, we propose a foreground-background segmentation algorithm for images with intensity inhomogeneity in a level set framework, which exploits simple distribution of local image intensities. We assume that the intensities of the foreground and background within a small enough neighborhood are separable and can be well approximated by two constants. We call such a neighborhood an intensity separable neighborhood (ISN). Given a set of overlapping ISNs that form a cover of the entire image domain or a region of interest, we formulate image segmentation as a problem of seeking for an optimal level set function that represents the foreground and background with its positive and negative sign, and a pair of constants that approximate the local foreground and background intensities within the ISNs. This formulation is a significant extension and improvement of our previous work. The main contributions in this paper include: (1) We eliminate an intrinsic drawback in our previous work that the fitting functions are not well defined for points far away from the zero level set, which causes unstable performance of segmentation; (2) The new algorithm is more efficient than our previous algorithm due to the sparse placement of the ISNs.

C. Li—This work is supported by NSFC (Grant numbers G0561671135, G0591630311, M0501020111531005). The authors thank the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. University of Electronic Science and Technology of China (UESTC), Chengdu, China

    Xue Shi & Chunming Li

Authors
  1. Xue Shi
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  2. Chunming Li
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Corresponding author

Correspondence to Xue Shi .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Shi, X., Li, C. (2019). IFOC: Intensity Fitting on Overlapping Cover for Image Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_47

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  • DOI: https://doi.org/10.1007/978-3-030-33723-0_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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