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

Conference paper


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.


Change-point detection Independence of ground truth image Global image thresholding Kullback-Leibler divergence Robust statistical measure Thresholding performance criteria 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringBirla Institute of TechnologyMesra, RanchiIndia
  2. 2.Department of Computer Science & EngineeringJadavpur UniversityKolkataIndia

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