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Estimation of Optimal Global Threshold Based on Statistical Change-Point Detection and a New Thresholding Performance Criteria

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Transactions on Engineering Technologies (IMECS 2017)

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Abstract

Global thresholding of gray-level image is a method of selecting an optimal gray-value that partition it into two mutually exclusive regions called background and foreground (object). The aim of this paper is to interpret the problem of optimal global threshold estimation in the terminology of statistical change-point detection (CPD). An important advantage of this approach is that it does not assume any prior statistical distribution of background or object classes. Further, this method is less influenced by the presence of outliers due to our judicious derivation of a robust criterion function depending on Kullback-Leibler (KL) divergence measure. Experimental results manifest the efficacy of proposed algorithm compared to other popular methods available for global image thresholding. In this paper, we also propose a new criterion for performance evaluation of thresholding algorithms. This performance criterion does not depend on any ground truth image. This performance criterion is used to compare the results of proposed thresholding algorithm with most cited global thresholding method found in the literature.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Rohit Kamal Chatterjee

  2. Department of Computer Science & Engineering, Jadavpur University, Kolkata, India

    Avijit Kar

Authors
  1. Rohit Kamal Chatterjee
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  2. Avijit Kar
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Corresponding author

Correspondence to Rohit Kamal Chatterjee .

Editor information

Editors and Affiliations

  1. International Association of Engineers, Hong Kong, Hong Kong

    Sio-Iong Ao

  2. Department of Computer and Communication Engineering, Daegu Catholic University, Daegu, Korea (Republic of)

    Haeng Kon Kim

  3. Instituto Tecnologico de Tijuana, Tijuana, Mexico

    Oscar Castillo

  4. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, Hong Kong

    Alan Hoi-Shou Chan

  5. Department of Industrial Engineering and Management, Kanagawa University, Yokohama, Japan

    Hideki Katagiri

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Chatterjee, R.K., Kar, A. (2018). Estimation of Optimal Global Threshold Based on Statistical Change-Point Detection and a New Thresholding Performance Criteria. In: Ao, SI., Kim, H., Castillo, O., Chan, AS., Katagiri, H. (eds) Transactions on Engineering Technologies. IMECS 2017. Springer, Singapore. https://doi.org/10.1007/978-981-10-7488-2_21

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  • DOI: https://doi.org/10.1007/978-981-10-7488-2_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7487-5

  • Online ISBN: 978-981-10-7488-2

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