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Conference Papers Year : 2012

Image Threshold Selection Exploiting Empirical Mode Decomposition

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Abstract

Thresholding process is a fundamental image processing method. Typical thresholding methods are based on partitioning pixels in an image into two clusters. A new thresholding method is presented, in this paper. The main contribution of the proposed approach is the detection of an optimal image threshold exploiting the empirical mode decomposition (EMD) algorithm. The EMD algorithm can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). When the image is decomposed by empirical mode decomposition (EMD), the intermediate IMFs of the image histogram have very good characteristics on image thresholding. The experimental results are provided to show the effectiveness of the proposed threshold selection method.
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Dates and versions

hal-01521420 , version 1 (11-05-2017)

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Attribution - CC BY 4.0

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Stelios Krinidis, Michail Krinidis. Image Threshold Selection Exploiting Empirical Mode Decomposition. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.395-403, ⟨10.1007/978-3-642-33409-2_41⟩. ⟨hal-01521420⟩
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