A Novel Threshold Selection Method Based on Iterative Clustering Strategy

Jin Guo He

doi:10.4028/www.scientific.net/AMM.433-435.288

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 433-435A Novel Threshold Selection Method Based on...

A Novel Threshold Selection Method Based on Iterative Clustering Strategy

Abstract:

This paper extends our previous algorithm for clustering. This previous algorithm works fine on simulated data. It can acquire satisfactory clustering results even with annular or zonal simulated data by causing the data to shrink within a cluster. To make use of the advantages of the previous algorithm, a one-dimensional (1D) histogram is mapped to a two-dimensional (2D) image and can be clustered by the previous algorithm, thus leading to stable results of histogram thresholds. The shrinking procedures of the 2D image or the 1D histogram are given, and a new parameter strategy is discussed.

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Periodical:

Applied Mechanics and Materials (Volumes 433-435)

Pages:

288-296

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.433-435.288

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Online since:

October 2013

Authors:

Jin Guo He

Keywords:

Clustering, Histogram, Iterative, Threshold

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References

[1] Jinguo He. The correspondence and branching problem in medical contour reconstruction. Proceeding of IEEE International Conference on Systems, Man and Cybernetics, 1591 - 1595, (2008).

DOI: 10.1109/icsmc.2008.4811514

Google Scholar

[2] Qiyao Yu, Clausi, D.A. IRGS: Image segmentation using edge penalties and region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12): 2126-2139, (2008).

DOI: 10.1109/tpami.2008.15

Google Scholar

[3] Ray N., Saha B.N. Edge Sensitive variational image thresholding. IEEE International Conference on Image Processing, 6(VI-37 -VI-40), (2007).

DOI: 10.1109/icip.2007.4379515

Google Scholar

[4] Rosenfeld A, Torre P De La. Histogram concavity analysis as an aid in threshold selection. IEEE Trans SMC-13(2): 231-235, (1983).

DOI: 10.1109/tsmc.1983.6313118

Google Scholar

[5] Pun T. A new method for gray-level picture thresholding using the entropy of histogram. Signal Processing, 2: 223-237, (1980).

DOI: 10.1016/0165-1684(80)90020-1

Google Scholar

[6] W. Tsai. Moment-preserving thresholding: A new approach. Computer Vision, Graphics and Image Processing, 29: 377-393, (1985).

DOI: 10.1016/0734-189x(85)90133-1

Google Scholar

[7] Xinwen Yu, Zuorui Shen, Application of several segmentation algorithms to the digital image of helicoverpa armigera, Journal of China Agricultural University, 6(5): 69-75, (2001).

Google Scholar

[8] Kaiping Wei, Tao Zhang, Xianjun Shen, Jingnan Liu. An improved threshold selection algorithm based on particle swarm optimization for image segmentation, Third International Conference on Natural Computation, 5: 24-27, (2007).

DOI: 10.1109/icnc.2007.226

Google Scholar