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An Experimental Evaluation of Different Features and Nodal Costs for Horizon Line Detection

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

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Abstract

Horizon line detection is a segmentation problem where a boundary between a sky and non-sky region is searched. Conventionally edge detection is performed as the first step followed by dynamic programming to find the shortest path which conforms to the detected horizon line. Recent work has proposed the use of machine learning to reduce the number of non-horizon edges to accurately detect the horizon line. In this paper, we investigate the suitablity of various local texture features and their combinations to reduce the number of false classifications for a recently proposed horizon detection approach. Specifically, we explore SIFT, LBP, HOG and their combinations SIFT-LBP, SIFT-HOG, LBP-HOG and SIFT-LBP-HOG as features to train the SVM classifier. We further show that using only edge information as the nodal costs is not enough and propose various nodal costs which can result in enhanced accuracy of the detected horizon line as evidenced by the conducted experiments and results. We compare our proposed formulations with an earlier approach relying only on edges and suffers due to faulty assumptions. We report our comparative results for an image set comprising of mountainous images captured during an outdoor robot exploration of Basalt Hills.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Engineering, University of Nevada, Reno, USA

    Touqeer Ahmad & George Bebis

  2. Dept. of Electrical and Computer Engineering, University of Nevada, Las Vegas, USA

    Emma Regentova

  3. NASA Ames Research Center, USA

    Ara Nefian & Terry Fong

Authors
  1. Touqeer Ahmad
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  2. George Bebis
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  3. Emma Regentova
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  4. Ara Nefian
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  5. Terry Fong
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

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

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Santa Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Ahmad, T., Bebis, G., Regentova, E., Nefian, A., Fong, T. (2014). An Experimental Evaluation of Different Features and Nodal Costs for Horizon Line Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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