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An Empirical Method for Threshold Selection

초록

영어

The performance of a number of image processing methods depends on the output quality of a thresholding process. Typical thresholding methods are based on partitioning pixels in an image into two clusters. In this paper, a new thresholding method is presented. The main contribution of the proposed approach is the application of the empirical mode decomposition (EMD) on detecting an optimal threshold for an input image. 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.

목차

Abstract
 1 Introduction
 2 Empirical Mode Decomposition (EMD)
 3 Threshold Selection Based-On EEMD
 4 Experimental Results
 5 Conclusion
 References

저자정보

  • Stelios Krinidis Information Management Department, Technological Institute of Kavala
  • Michail Krinidis Information Management Department, Technological Institute of Kavala
  • Vassilios Chatzis Information Management Department, Technological Institute of Kavala

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