Analyzing of the Image Characteristic Base on Potential Field Distraction Function

Abstract:

A new definition of potential field distribution function of image was proposed, and the characteristics of the distribution function were analyzed. Distribution function of potential field represents whole fluctuant information of image. So it can be used to analyze the image characteristics. Potential field of image background is weak and correspond to Gaussian distribution. Low stage of potential field distribution function can be used to evaluate noise level of image. Inflection point of the amplitude distribution function of potential field represented the transition point between the background and the actual edge in potential field image. So value of this point can be used as the threshold value to identify edge. When object distance of microscope changes, the edge comes more and more clear, and the edge area judged by the method above will be bigger and bigger. This character can be used to instruct auto-focus process.

• Periodical: Applied Mechanics and Materials (Volume 339)
• Pages: 389-395
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.339.389
• Online since: July 2013
• Authors: Ze Biao Shan, Yao Wu Shi, Hong Cheng Li, Hong Wei Shi
• Keywords: Auto-Focus, Edge Labeling, Inflection Point, Potential Field Distribution Function

References

[1] Tony. F. Chan, Jian Hong Shen, Wenbin Chen, Jin Cheng: Image Processing and Analysis[M] , Beijing: Science Press,2011.

[2] Jiao Wang. Digital Image Processing[M]: Beijing: Science Press,2009.

[3] Lan Wang, Jin Wu: An Improved Canny Edge Detection Algorithm[J]. Microcomputer Information. 2010, 26(12),198-202.

[4] Jinli Yao, Xia Wang: Microscope Image Denoising Algorithm Study and Realization Based on Wavelet Transform[J]. Computer & Digital Engineering. 2008, 36(7), 21-26.

[5] Qingwu Li, Chunyuan He: Two-Dimensional Wavelet Analysis Theory and Its Application in Eliminating Noises of Microscopic Image[J]. Journal of Henan Normal University(Natural Science). 2005, 33(2),44-46.

[6] Xuange Peng, Chunjuan OuYang, Yingchun OuYang, Bing Zhu.Micro: Micro image denoising algorithm based on multidirectional 1D wavelet transform[J]. Computer Engineering and Applications. 2007, 43(12), 31-33.

DOI: 10.1109/iciii.2009.215

[7] L. Hamami, N. Lassouaoui: An Approach Fractal and Analysis of Variogram for Edge Detection of Biomedical Images[C]. IWANN 2001.

DOI: 10.1007/3-540-45723-2_40

[8] Guoping Yan, Junfeng He: Extended Laplacian of Gaussian operator for edge detection[J]. Journal of Huazhong University of Science and Technology(Natural Science Edition). 2006, 34(10), 21-23.

[9] Liming Guan, Lei Li, Jian Lin: Image edge detection method based on improved LOG operator[J]. Journal of Mechanical & Electrical Engineering.2010, 27(12), 13-115.

[10] Hongwen Luo, Siliang Ma, Zhongyu Xu: Threshold Analysis of Micro Image in Edge Detection Based on Statistical Approach[J]. Journal of Jilin University (Science Edition).2006, 44(3):397-400.

[11] Shuai Wan, Fuzheng Yang, Mingyi He: Gradient-threshold edge detection based on perceptually adaptive threshold selection[C], Industrial Electronics and Applications. 2008.

DOI: 10.1109/iciea.2008.4582665