Abstract
Image segmentation is a partitioning of an image into constituent parts according to its attributes such as pixel intensity, spectral values, and/or textural properties [1]. Image binarization segmentation which is defined as dividing an image into objects and background is the most fundamental and important processing step and common, basic and key technique in the research of object identification, image understanding and computer vision. The performance of image segmentation will impact directly on the subsequent object identification and image understanding. There are many methods of image segmentation and the simplest and most effective one is the method based on the gray-level threshold, but it is very difficult to select a appropriate threshold. In this chapter, two image segmentation approaches with PCNN: one based on entropy or cross-entropy and the other based on genetic algorithm (GA), are introduced.
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Reference
Tilton JC (1996) Hybrid image segmentation for earth remote sensing data analysis. In: International Geoscience and Remote Sensing Symposium, Lincoln, 27–31 May 1996
Long JY, Jin LW (2004) An image binarization method based on global mean and local standard deviation. Computer Engineering 30(2): 70–72
Chen D, Zhang F, He GM (2003) An improved binarization algorithm for document image. Computer Engineering 29(13): 85–86
Gao YY, Zhang L, Wu GW (1999) An algorithm for threshold based on arithmetic mean of gray value. Journal of Image and Graphics 4(6): 524–528
Lu JZ (2004) The research on the binary image algorithm and its realization. Sci/tech Information Development & Economy 14(12): 266–267
Su MJ, Chen R, Ma YD (2007) Research on the threshold segmentation algorithm based on entropy and histogram of image. China Science Paper Online. http://www.paper.edu.cn/advance search. Accessed 11 October 2007
Ostu N (1979) A threshold selection method from gray-level histograms. IEEE transactions on System, Man, and Cybernetics 9(1): 62–66
Zhang YJ (1996) A survey on evaluation methods for image segmentation. Pattern recognition 29(8): 1335–1346
Ranganath HS, Kuntimad G (1999) Object detection using pulse coupled neural networks. IEEE Transactions on Neural Networks 10(3): 615–620
Li CH, Lee CK (1993) Minimum cross-entropy thresholding. Pattern Recognition 26(4): 617–625
Rechenberg I (1965) Cybemetic solution path of an experimental problem. Royal Aircraft Establishment, Library translation No. 1222, Farnborough, Hants., U.K.
Schwefel HP (1981) Numerical optimization of computer model. Wiley, Chichester
Li MQ, Kou JS, Li D et al (2002) Basic theory and application of GA. Science Press, Beijing
Gu XD, Guo SD, Yu DH (2002) A new approach for image segmentation based on unit-linking PCNN. In: The 1st International Machine Learning and Cybernetics Conference, Beijing, 4–5 November 2002
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© 2010 Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg
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Ma, Y., Zhan, K., Wang, Z. (2010). Image Segmentation. In: Applications of Pulse-Coupled Neural Networks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13745-7_3
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DOI: https://doi.org/10.1007/978-3-642-13745-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13744-0
Online ISBN: 978-3-642-13745-7
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