为实现数字迷彩图案设计的自动化和快速化，基于马尔科夫随机场和金字塔模型构建了数字迷彩图案的设计体系. 采用均值聚类法提取背景图像的主色和面积比例等特征，利用马尔科夫随机场模型模拟自然地物的纹分布，并用金字塔模型实现迷彩图案的分解有效对付不同距离的侦察威胁，构建了全新的数字迷彩图案设计体
系. 结合南方林地某区域的背景特性，完成了数字迷彩图案的设计. 结果表明，该模型初步实现了数字迷彩图案设计的自动化和精确化，有效提高了迷彩图案设计的效率和质量.
For the fast and automatic design of digital pattern painting, a design platform based on Markov random field and a pyramid structure is constructed. Major colors and their area percentages are derived using a clustering method. The Markov random field model is used to simulate natural texture distribution, and
the pyramid structure is used to decompose the digital pattern paintings to combat reconnaissance threats at different distances. The design platform is thus constructed. A test pattern painting is designed based on the background characteristics of a forest region. The results show that the proposed model can be used to design digital pattern painting automatically and quickly, resulting in effective improvements in efficiency and quality.
[1] 喻钧，杨武侠，胡志毅，陈宏书. 数码迷彩的生成算法研究 [J]. 光电工程，2010, 37(11): 110-114.
YU Jun, YANG Wuxia, HU Zhiyi, CHEN Hongshu. Research of digital camouflage generation algorithm [J]. Opto-electronic Engineering, 2010, 37(11): 110-114. (in Chinese)
[2] 喻钧，初苗，田少辉，胡志毅. 迷彩设计中背景图像聚类方法的比较分析 [J]. 应用科学学报，2009, 27(5): 475-479.
YU Jun, CHU Miao, TIAN Shaohui, HU Zhiyi. Comparison and analysis for image clustering methods of camouflage design [J]. Journal of Applied Sciences, 2009, 27(5): 475-479. (in Chinese)
[3] CHEN W T, LIU W C, CHEN M S. Adaptive color feature extraction based on image color distributions [J]. IEEE Transactions on Image Processing, 2010, 19(8): 2005-2016.
[4] SAAD M A, BOVIK A C. Extracting regions of interest from still images: color saliency and wavelet-based approaches [C]// 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop. New Jersey, USA: Institute of Electrical and Electronics Computer Society, January 2009: 540-543.
[5] RAUT S, RAGHUVANSHI M, DHARASKAR R, RAUT A. Image segmentation-a state-of-art survey for prediction [C]// Proceedings-International Conference on Advanced Computer Control, New Jersey, USA: Institute of Electrical and Electronics Engineer Computer Society, January 2009: 420-424.
[6] FU K S, YOUNG T Y. Handbook of pattern recognition and image processing [M]. New York: Academic Press, 1986.
[7] MOUSSA A, SBIHI A, POSTAIRE J G. A Markov random ?eld model for mode detection in cluster analysis [J]. Pattern Recognition Letters, 2008, 29: 1197-1207.
[8] LI M, NGUYEN T Q. Markov random field model-based edge-directed image interpolation [J]. IEEE Transactions on Image Processing, 2008, 17(7): 1121-1128.
[9] DEY R, NANDA P K, PANDA S. Constrained Markov random field model for color and texture image segmentation [C]// IEEE International Conference on Signal processing, Communications and Networking Madras Institute of Technology, Anna University Chennai India, January 2008: 317-322.
[10] BABACAN S D, MOLINA R, KATSAGGELOS A K. Generalized Gaussian Markov random field image restoration using variational distribution approximation [C]// IEEE International Conference on Acoustics, Speech and Signal Processing, 2008: 1265-1268.
[11] LIU L F, JIAO L C, HUO H W. A greedy two-stage Gibbs sampling method for motif discovery in biological sequences [C]// 2008 International Conference on BioMedical Engineering and Informatics, 2008: 13-17.
[12] 邵晓鹏，张建奇. 基于GLC模型的红外纹理合成方法研究 [J]. 红外与毫米波学报，2003, 22(5): 341-345.
SHAO Xiaopeng, ZHANG Jianqi. The study on infrared texture simulation based on GLC model [J]. Journal of Infrared and Millimeter Waves, 2003, 22(5): 341-345. (in Chinese)
[13] QUARANTA C, DANIELE G, BALZAROTTI G. Numerical method for IR background and clutter simulation [C]// International Society for Optical Engineering (SPIE), 1997, 3062: 10-21.
