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图像边缘检测是图像处理的一种重要技术, 其中不确定性表示与提取是关键问题之一. 在现有模拟物理学思想的相关方法基础之上, 提出了基于认知物理学的不确定性边缘表示与提取方法. 该方法利用数据场发现图像全局灰度认知, 构建图像灰度值空间到数据场势值空间的映射关系, 从场论的角度建立了可扩展的理论框架、统一了现有相关方法; 另一方面, 构造半升云模型建立云模型确定度的变化幅度与边缘像素表示与提取的内在关联关系, 最终在认知物理学核心理论的支持下实现图像不确定性边缘表示与提取. 所提出的方法时间耗费近似与图像尺寸成线性关系. 定性和定量的实验结果及分析表明, 该方法的分割效果较好, 性能稳定, 具有合理性和有效性.Image edge detection is an important tool of image processing, in which edge representation and extraction with uncertainty is one of key issues. Based on the physics-like methods for image edge representation and extraction, a novel cognitive physics-based method with uncertainty is proposed. The method uses data field to discover the global information from the image and then to map it from grayscale space to the appropriate potential space. From the point of view of the field theory, the method establishes an extensible theoretical framework and unifies the existing physics-like methods. On the other hand, the method defines the ascending half-cloud to construct the internal relationship between the range of cloud uncertainty degree and the edge representation and extraction. Finally, the method achieves image edge representation and extraction with uncertainty using the cognitive physics. The time complexity of the proposed algorithm is approximately linear in the size of the original image. It is indicated by the quantitative and qualitative experiments that the proposed method yields accurate and robust result, and is reasonable and effective.
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Keywords:
- edge detection /
- image segmentation /
- cloud model /
- data field
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[1] Wu Y Q, Zhang J K 2010 Acta Phys. Sin. 59 5487 (in Chinese) [吴一全, 张金矿 2010 59 5487]
[2] Ma J F, Hou K, Bao S L, Chen C 2011 Chin. Phys. B 20 028701
[3] Tang Y G, Di Q Y, Zhao L X, Guan X P, Liu F C 2009 Acta Phys. Sin. 58 9 (in Chinese) [唐英干, 邸秋艳, 赵立兴, 关新平, 刘福才 2009 58 9]
[4] Song F J, Jutamulia S, Song J L, Yao S Y, Wang D 2003 Acta Phys. Sin. 52 3055 (in Chinese) [宋菲君, 赵文杰, Jutamulia S, 宋建力, 姚思一, 王栋 2003 52 3055]
[5] He S H, Yang S Q, Shi A G, Li T W 2009 Acta Phys. Sin. 58 794 (in Chinese) [何四华, 杨绍清, 石爱国, 李天伟 2009 58 794]
[6] Dong J T, Xu Y, Zong X P 2006 Acta Phys. Sin. 55 3223 (in Chinese) [董江涛, 徐艳, 宗晓萍 2006 55 3223]
[7] Chen D P, Xing C F, Zhang Z, Zhang C L 2012 Acta Phys. Sin. 61 024202 (in Chinese) [陈大鹏, 刑春飞, 张峥, 张存林 2012 61 024202]
[8] Direkoglu C, Dahyot R, Manzke M 2012 Int. J. Comput. Vision 100 170
[9] Sun G Y, Liu Q H, Liu Q, Ji C Y, Li X W 2007 Pattern Recogn. 40 2766
[10] Lopez-Molina C, Bustince H, Fernandez J, Couto P, De Baets B 2010 Pattern Recogn. 43 3730
[11] Lopez-Molina C, Bustince H, Galar M, Fernandez J, De Baets B 2009 Ninth International Conference on Intelligent Systems Design and Applications Pisa, Italy November 30-December 2, 2009 p1347
[12] Wang Z R, Quan Y M 2007 International Symposium on Intelligent Signal Processing and Communication Systems Xiamen, China November 28 - December 1, 2007 p260
[13] Bouda B, Masmoudib L, Aboutajdine D 2008 Signal Process. 88 905
[14] Wu T, Gao Y 2011 ICIC Express Lett. 5 733
[15] Nixon M, Liu X U, Direkoglu C, Hurley D 2011 Comput. J. 54 11
[16] Direkoglu C, Nixon M, Liu X U, Hurley D 2011 Pattern Recogn. Lett. 32 270
[17] Boskovitz V, Guterman H 2002 IEEE T. Fuzzy Syst. 2 247
[18] Pal S K, King R A 1983 IEEE T. Pattern Anal. 1 69
[19] Bezdek J, Chandrasekhar R, Attikouzel Y 1998 IEEE T. Fuzzy Syst. 1 52
[20] Lopez-Molina C, De Baets B, Bustince H 2011 Comput. Vis. Image Und. 11 1571
[21] Li D Y, Liu C Y, Gan W Y 2009 Int. J. Intell. Syst. 24 357
[22] Gan W Y, Li D Y, Wang J M 2006 Acta Electron. Sin. 34 258 (in Chinese) [淦文燕, 李德毅, 王建民 2006 电子学报 34 258]
[23] Li D Y, Du Y 2005 Artificial Intelligence with Uncertainty (Beijing: National Defence Industry Press) p187 (in Chinese) [李德毅, 杜鹢 2005 不确定性人工智能 (北京: 国防工业出版社) 第187页]
[24] Qin K, Xu K, Liu F L, Li D Y 2011 Comput. Math. Appl. 62 2824
[25] Wu T, Qin K 2012 Neurocomputing 97 278
[26] Liu Y, Li D Y, Zhang G W 2009 Acta Electron. Sin. 37 1651 (in Chinese) [刘禹, 李德毅, 张光卫 2009 电子学报 37 1651]
[27] Rosin P L 2001 Pattern Recogn. 34 2083
[28] Wu T, Qin K 2012 Opt. Lasers Eng. 50 131
[29] Baddeley A J 1992 Robust Computer Vision: Quality of Vision Algorithms (Karlsruhe: Wichmann Verlag) p152
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