自然科学版 英文版
自然科学版 英文版
自然科学版 英文版

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中南大学学报(英文版)

Journal of Central South University

Vol. 26    No. 6    June 2019

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An enhanced image binarization method incorporating with Monte-Carlo simulation
HAN Zheng(韩征)1, 2, SU Bin(粟滨)1, LI Yan-ge(李艳鸽)1, 2, MA Yang-fan(马杨帆)1, WANG Wei-dong(王卫东)1, 3, CHEN Guang-qi(陈光齐)4

1. School of Civil Engineering, Central South University, Changsha 410075, China;
2. State Key Laboratory of Geohazard Prevention and Geo-environment Protection, Chengdu 610000, China;
3. The Key Laboratory of Engineering Structures of Heavy Haul Railway, Ministry of Education,Changsha 410075, China;
4. Department of Civil and Structural Engineering, Kyushu University, Fukuoka 819-0395, Japan

Abstract:We proposed an enhanced image binarization method. The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background, spatially-changed illumination, and uncertainties of block size in traditional method. The proposed method first partitions the image into square blocks that reflect local characteristics of the image. After image partitioning, each block is binarized using Otsu’s thresholding method. To minimize the influence of the block size and the boundary effect, we incorporate Monte-Carlo simulation into the binarization algorithm. Iterative calculation with varying block sizes during Monte-Carlo simulation generates a probability map, which illustrates the probability of each pixel classified as foreground. By setting a probability threshold, and separating foreground and background of the source image, the final binary image can be obtained. The described method has been tested by benchmark tests. Results demonstrate that the proposed method performs well in dealing with the complex background and illumination condition.

 

Key words: binarization method; local thresholding; Monte-Carlo simulation; benchmark tests

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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