Optimal Global Threshold Estimation Using Statistical Change Point Detection

R. Chatterjee, A. Kar
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引用次数: 2

Abstract

Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does not assume any prior statistical distribution of background and object grey levels. Further, this method is less influenced by an outlier due to our judicious derivation of a robust criterion function depending on Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method compared to other popular methods available for global image thresholding. In this paper we also propose a performance criterion for comparison of thresholding algorithms. This performance criteria does not depend on any ground truth image. We have used this performance criterion to compare the results of proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
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基于统计变化点检测的最优全局阈值估计
本文的目的是将全局图像阈值问题重新表述为一种有充分根据的统计方法,即变化点检测(CPD)问题。我们提出的CPD阈值算法不假设背景和目标灰度的任何先验统计分布。此外,由于我们根据Kullback-Leibler (KL)散度度量明智地推导了鲁棒准则函数,因此该方法受异常值的影响较小。实验结果表明,该方法与其他常用的图像全局阈值分割方法相比,具有较好的有效性。本文还提出了一种比较阈值算法的性能标准。该性能标准不依赖于任何地面真值图像。我们使用这一性能标准来比较所提出的阈值算法与文献中引用的大多数全局阈值算法的结果。
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