电气设备红外与可见光图像的配准方法研究
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摘要
本文把图像配准引入到电气设备的双通道图像在线监测中,红外图像与可见光图像配准后可得到设备更丰富、更全面、更综合的互补信息,能提高电气设备实时在线监测的准确性与有效性,有利于保障电网的运行安全。本文首先构建了变电站电气设备双通道图像在线监测系统,研究了配准之前的预处理方法和红外与可见光图像的配准方法,主要工作如下:
1.根据现场的实际情况,提出了一种变电站电气设备双通道图像在线实时监测的方案,构建了以红外传感器和可见光CCD(Charge Coupled Device,电荷耦合器件)传感器为核心器件的双通道监测的实际系统。系统实现了对设备的可见光图像和热状态信息的远程、实时、在线监测,提高了在线监测的可靠性和准确性。现场运行表明,该系统稳定可靠、效果良好。
2.复杂的现场采集条件使获得的红外图像和可见光图像存在噪声干扰,配准之前必须对图像进行预处理。研究了两种消噪方法:①从估计图像中噪声的统计特性出发,提出了一种基于噪声标准差估计的小波图像去噪方法;②图像信号和噪声信号是由不同的物理源产生的,是相互统计独立的,将ICA(Independent Component Analysis)方法应用于图像去噪,提出了一种基于FastICA算法的去噪方法。实验结果表明两种方法都能提高图像质量,突出图像特征,为下一步红外图像与可见光图像的配准研究做了充分准备。
3.通过理论分析与推导得出了基于经验模式分解的配准原理,并以互信息为配准测度,提出了一种基于剩余图像和互信息的红外与可见光图像的配准方法。大量红外与可见光图像配准结果验证了配准原理的正确性和方法的有效性及先进性,该方法取得了比传统互信息法、基于小波分解和互信息的方法更高的配准精度。对医学图像、遥感图像等多模态图像的配准也获得了较高精度的配准效果。
4.把新颖且性能优秀的SURF(Speeded Up Robust Features)算法应用到红外与可见光图像的配准中,针对红外和可见光图像细节差别大甚至存在对比度反转和两者轮廓大体一致的特点,提出了一种基于边缘提取的SURF红外与可见光图像配准方法,解决了用红外负像配准的片面性问题和SURF算法对模态敏感的问题,从电气设备红外与可见光图像的配准结果可以看出该方法取得了非常快的配准速度和较好的配准效果,可满足在线实时的应用需求。
Image registration is introduced into the dual-channel image on-line monitoring for power equipment, thus more abundant and comprehensive complementary information could be obtained through matching infrared and visible image, which could improve the accuracy and validity of real-time on-line monitoring for power equipment, and be benefit to guarantee the safe operation of power system. The dual-channel image on-line monitoring system for power equipment is established, and then the preprocessing method and the registration method of infrared/visible image of power equipment are studied in this paper. The main work is done as follows.
1. The dual-channel image on-line monitoring method for power equipment in the substation is proposed according to the actual demand. A practical system with the infrared sensor and visible light CCD (Charge Coupled Device) sensor as the key devices is established. The system has realized remote, real-time and on-line monitoring of the power equipment’s visible image and thermal information, which could improve the reliability and accuracy. It is stable shown by its on-the-spot operation.
2. The preprocessing of image before registration is inevitable because of the noise jamming in image acquisition under complicated field conditions. There are two de-noising methods studied in the paper.①Wavelet de-noising method is proposed based on the estimate of standard deviation according to the statistical characteristics of the noises.②The image and noise have mutually statistical independency, and ICA (Independent Component Analysis) is applied to image de-noising. So, the de-noising method based on the FastICA algorithm is proposed. Both methods could improve the quality of image and highlight image features, which make full preparation for the registration.
3. The registration principle based on the Empirical Mode Decomposition (EMD) is obtained by theoretical analysis and deduction. On the basis of this principle, the infrared and visible image registration method is proposed with mutual information as registration measure. A lot of experimental results demonstrate that the registration principle is correct and the proposed method is advanced and effective. It has more precision compared with the traditional mutual information and wavelet composition method. The registration of typical multi-modal images such as medical image and remote sensing image has also high-precision results.
4. A novel detector and descriptor with high performance, coined SURF (Speeded Up Robust Features), has been introduced to the infrared and visible image registration. And the registration method is proposed based on edge extraction and SURF in view of the features that there is large difference, even contrast inversion and consistent contour between infrared and visible image in image details. The method can solve the problems of unilateralism of the infrared negative image being directly used to register and the modality-sensitivity of SURF. It has achieved good results and high speed according to the experimental results, and can fully satisfy the practical demand for real-time applications.
1.根据现场的实际情况,提出了一种变电站电气设备双通道图像在线实时监测的方案,构建了以红外传感器和可见光CCD(Charge Coupled Device,电荷耦合器件)传感器为核心器件的双通道监测的实际系统。系统实现了对设备的可见光图像和热状态信息的远程、实时、在线监测,提高了在线监测的可靠性和准确性。现场运行表明,该系统稳定可靠、效果良好。
2.复杂的现场采集条件使获得的红外图像和可见光图像存在噪声干扰,配准之前必须对图像进行预处理。研究了两种消噪方法:①从估计图像中噪声的统计特性出发,提出了一种基于噪声标准差估计的小波图像去噪方法;②图像信号和噪声信号是由不同的物理源产生的,是相互统计独立的,将ICA(Independent Component Analysis)方法应用于图像去噪,提出了一种基于FastICA算法的去噪方法。实验结果表明两种方法都能提高图像质量,突出图像特征,为下一步红外图像与可见光图像的配准研究做了充分准备。
3.通过理论分析与推导得出了基于经验模式分解的配准原理,并以互信息为配准测度,提出了一种基于剩余图像和互信息的红外与可见光图像的配准方法。大量红外与可见光图像配准结果验证了配准原理的正确性和方法的有效性及先进性,该方法取得了比传统互信息法、基于小波分解和互信息的方法更高的配准精度。对医学图像、遥感图像等多模态图像的配准也获得了较高精度的配准效果。
4.把新颖且性能优秀的SURF(Speeded Up Robust Features)算法应用到红外与可见光图像的配准中,针对红外和可见光图像细节差别大甚至存在对比度反转和两者轮廓大体一致的特点,提出了一种基于边缘提取的SURF红外与可见光图像配准方法,解决了用红外负像配准的片面性问题和SURF算法对模态敏感的问题,从电气设备红外与可见光图像的配准结果可以看出该方法取得了非常快的配准速度和较好的配准效果,可满足在线实时的应用需求。
Image registration is introduced into the dual-channel image on-line monitoring for power equipment, thus more abundant and comprehensive complementary information could be obtained through matching infrared and visible image, which could improve the accuracy and validity of real-time on-line monitoring for power equipment, and be benefit to guarantee the safe operation of power system. The dual-channel image on-line monitoring system for power equipment is established, and then the preprocessing method and the registration method of infrared/visible image of power equipment are studied in this paper. The main work is done as follows.
1. The dual-channel image on-line monitoring method for power equipment in the substation is proposed according to the actual demand. A practical system with the infrared sensor and visible light CCD (Charge Coupled Device) sensor as the key devices is established. The system has realized remote, real-time and on-line monitoring of the power equipment’s visible image and thermal information, which could improve the reliability and accuracy. It is stable shown by its on-the-spot operation.
2. The preprocessing of image before registration is inevitable because of the noise jamming in image acquisition under complicated field conditions. There are two de-noising methods studied in the paper.①Wavelet de-noising method is proposed based on the estimate of standard deviation according to the statistical characteristics of the noises.②The image and noise have mutually statistical independency, and ICA (Independent Component Analysis) is applied to image de-noising. So, the de-noising method based on the FastICA algorithm is proposed. Both methods could improve the quality of image and highlight image features, which make full preparation for the registration.
3. The registration principle based on the Empirical Mode Decomposition (EMD) is obtained by theoretical analysis and deduction. On the basis of this principle, the infrared and visible image registration method is proposed with mutual information as registration measure. A lot of experimental results demonstrate that the registration principle is correct and the proposed method is advanced and effective. It has more precision compared with the traditional mutual information and wavelet composition method. The registration of typical multi-modal images such as medical image and remote sensing image has also high-precision results.
4. A novel detector and descriptor with high performance, coined SURF (Speeded Up Robust Features), has been introduced to the infrared and visible image registration. And the registration method is proposed based on edge extraction and SURF in view of the features that there is large difference, even contrast inversion and consistent contour between infrared and visible image in image details. The method can solve the problems of unilateralism of the infrared negative image being directly used to register and the modality-sensitivity of SURF. It has achieved good results and high speed according to the experimental results, and can fully satisfy the practical demand for real-time applications.
引文
[1] ZitováB, Flusser J. Image registration methods: a survey. Image and Vision Computing, 2003,21(11):977~1000
[2]倪国强,刘琼.多源图像配准技术分析与展望.光电工程,2004,31(9):1~6
[3]王忆锋,张海联,李灿文,等.多传感器数据融合中的配准技术.红外与激光工程,1998,27(1):38~41
[4]钮永胜,倪国强.多传感器图像自动配准技术研究.光学技术,1999,(1): 16~18,20
[5]刘松涛,杨绍清.图像配准技术性能评估及实现概况.电光与控制,2007,14(3): 73~78, 83
[6]宋振环,唐伶俐,李传荣.遥感图像配准技术研究最新进展.科技导报,2007,25(12):53~58
[7] Li Hui, Manjunath B, Mitra S. A contour-based approach to multisensor image registration. IEEE Transactions on Image Processing, 1995,4(3):320~334
[8] Dana K, Anandan P. Registration of visible and infrared images. SPIE, 1993, (1957):2~13
[9] Li Hui, Zhou Yi-Tong. Automatic visual/infrared image registration. Optical Engineering, 1996,35(2):391~400
[10]孙才新.输变电设备状态在线监测与诊断技术现状和前景.中国电力,2005,38(2):1~7
[11]徐大可,张元林,王文胜.变电站电气设备在线监测综述.电力自动化设备,2001,21(9):54~57
[12] Brown L G. A survey of image registration techniques. ACM Computing Surveys, 1992,24(4):325~376
[13] Gao Zhi-Yong, Zhen Kang-Shen, Yi Ping-Jing. Two-stage correlation method in low SNR for fast image registration. In: Proceedings of IEEE, 1991:337~341
[14]薛模根,韩裕生,李从利,等.基于区域同名度矩阵的连续帧图像配准.中国图象图形学报,2006, 11(1):53~59
[15]赵辉,陈辉,于泓.一种改进的全景图自动拼接算法.中国图象图形学报,2007, 12(2):336~342
[16] Kaneko S, Satoh Y, Igarashi S. Using selective correlation coefficient for robust image registration. Pattern Recognition, 2003,36(5):1165~1173
[17]李晓明,赵训坡,郑链,等.基于Fourier-Mellin变换的图像配准方法及应用扩展.计算机学报,2006,29(3):466~472
[18]刘卫光,崔江涛,周利华.插值和相位相关的图像亚像素配准方法.计算机辅助设计与图形学学报,2005,17(6):1273~1277
[19] Chen Q S, Defrise M, Deconinck F. Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(12): 1156~1168
[20] Anuta P E. Spatial registrations of multispectral and multitemporal digital imagery using fast Fourier transform. IEEE Transactions on Geoscience Electronics, 1970, (8):353~368
[21]时永刚,邹谋炎.图像配准中统计型相似性测度的比较与分析方法.计算机学报,2004,27(9):1278~1283
[22] Viola P, Wells W. Alignment by maximization of mutual information. In: Proceedings of the 5th Int. Conf. Computer Vision, Boston, MA, June 1995,16~23
[23] Collignon A, Maes F, Vandermeulen D, et al. Automated multimodality image registration using information theory. In: Proceedings of the Information Processing in Medical Imaging Conference, Dordrecht, June 1995,263~274
[24]张见威,韩国强,沃焱.基于边界距离场互信息的图像配准方法.通信学报,2006,27(7):87~93
[25]钮永胜,倪国强.基于小波分析的可见光图像自动配准方法研究.高技术通讯,1999,(9):24~27
[26] Tuytelaars T, Mikolajczyk K. Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, 2008,3(3):177~280
[27] Moravec H. Rover visual obstacle avoidance. In: International Joint Conference on Artificial Intelligence, Vancouver, Canada, 1981,785~790
[28] Harris C, Stephens M. A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, Manchester, UK, 1988,147~151
[29] Lindeberg T. Feature detection with automatic scale selection. International Journal of Computer Vision, 1998,30(2):79~116
[30] Mikolajczyk K, Schmid C. Indexing based on scale invariant interest points. In: Proceedings of the 8th International Conference on Computer Vision, Vancouver, Canada, 2001,525~531
[31] Lowe D. Object recognition from local scale-invariant features. In: International Conference on Computer Vision, Corfu, Greece, 1999,1150~1157
[32] Kadir T, Brady M. Scale, saliency and image description. International Journal of Computer Vision, 2001,45(2):83~105
[33] Mikolajczyk K, Schmid C. Scale and affine invariant interest point detectors. International Journal of Computer Vision, 2004,60(1):63~86
[34] Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(10): 1615~1630
[35] Gool L, Moons T, Ungureanu D. Affine/photometric invariants for planar intensity patterns. In: Proceedings of the 4th European Conference Computer Vision, 1996, 642~651
[36] Schaffalitzky F, Zisserman A. Multi-view matching for unordered image sets. In: Proceedings of the 7th European Conference Computer Vision, 2002,414~431
[37] Freeman W, Adelson E. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991,13(9):891~906
[38] Lowe D. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004,60(2):91~110
[39] Bay H, Tuytelaars T, Gool L. SURF: Speeded Up Robust Features. In: Proceedings of the 9th European Conference Computer Vision, Graz, Austria, 2006,404~417
[40] Ke Y, Sukthankar R. PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, USA, 2004, 506~513
[41] Abdel-Hakim A, Farag A. CSIFT: A SIFT descriptor with color invariant characteristics. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006,(2):1978~1983
[42] Ros J, Laurent C. Description of local singularities for image registration. In: The 18th International Conference on Pattern Recognition, 2006,(4):61~64
[43] Cheung W, Hamarneh G. N-SIFT: N-dimensional scale invariant feature transform for matching medical images. In: The 4th IEEE International Symposium on Biomedical Imaging, 2007,720~723
[44] Kelman A, Sofka M, Stewart C. Keypoint descriptors for matching across multiple image modalities and non-linear intensity variations. In: The IEEE Conference on Computer Vision and Pattern Recognition, 2007,1~7
[45]李晓明,郑链,胡占义.基于SIFT特征的遥感影像自动配准.遥感学报,2006,10(6):885~892
[46]厉茂海,洪炳镕,罗荣华,等.基于单目视觉的移动机器人全局定位.机器人,2007,29(2):140~144,178
[47] Chen Jian, Tian Jie. Rapid multi-modality preregistration based on SIFT descriptor. In: Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, 2006,1437~1440
[48] Bay H, Fasel B, Gool L. Interactive museum guide: fast and robust recognition of museum objects. In: Proceedings of the 1st International Workshop on Mobile Vision, Graz, Austria, May 2006
[49] Murillo A, Guerrero J, Sagüés C. SURF features for efficient robot localization with omnidirectional images. In: The IEEE International Conference on Robotics and Automation, Roma, Italy, April 2007,3901~3907
[50] Coiras E, Santamaria J, Miravet C. A segment-based registration technique for visual-ir images. Optical Engineering, 2000,39(1):282~289
[51] Yang Xiang-Jie, Sheng Yun-Long, et al. Robust multisensor image registration with partial distance merits. In: Proceedings of the 3rd International Conference on Information Fusion, Roma, 2000,(1):23~29
[52] Hines G, Rahman Z, Jobson D, et al. Multi-image registration for an enhanced vision system. In: Proceedings of the SPIE, 2003,(5108):231~241
[53] Wang Jian-Gang, Sung E, Venkateswarlu R. Registration of infrared and visible spectrum imagery for face recognition. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, 2004, 638~644
[54] ?egviéS. A multimodal image registration technique for structured polygonal scenes. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia, 2005,500~505
[55] Fan Xiao-Feng, Rho H, Saber E. Automatic registration of multi-sensor airborne imagery. In: The 34th Applied Imagery and Pattern Recognition Workshop, 2005, 81~86
[56] Hild M, Umeda G. Image registration in stereo-based multi-modal imaging systems. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005,70~75
[57] Kim K, Lee J, Ra J. Robust multi-sensor image registration by enhancing statistical correlation. In: The 8th International Conference on Information Fusion, 2005, 380~386
[58] Kern J, Pattichis M. Robust multispectral image registration using mutual information models. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(5):1494~1505
[59] Istenic R, Heric D, Ribaric S, et al. Thermal and visual image registration in hough parameter space. In: The 14th International Conference on System, Signal and Image Processing, and 6th EURASIP Conference Focused on Speech and Image Processing, Multimedia Communications and Services, 2007, 106~109
[60] Li Tie-Jun, Chen Zhe, Wang Ren-Xiang. Fuzzy feature matching between infrared image and optical image. In: Proceedings of the SPIE, 2001,(4602):260~265
[61] Huang Xi-Shan, Chen Zhe. A wavelet-based multisensor image registration algorithm. In: The 6th International Conference on Signal Processing, 2002, (1): 773~776
[62]曾文锋.红外与可见光图像融合中的快速配准方法.红外与激光工程,2002, 31(2):158~160
[63]舒丽霞,彭晓明,周成平,等.一种新颖的红外与可见光图像自动配准算法.华中科技大学学报,2003,31(6):92~94
[64]陶冰洁,王敬儒,张启衡.采用仿射变换的红外与可见光图像配准方法.光电工程,2004,31(11):39~41
[65]陈煜,田裕鹏.图像融合中的图像配准方法研究.激光与红外,2004, 34(1): 34~36
[66]刘卫光,郭师红,周利华.红外与可见光图像实时配准融合系统.红外技术,2004,26(5):66~70
[67]刘松涛,王学伟,周晓东,等.基于传感器参数和目标轮廓中心的自动配准算法研究.光学精密工程,2005,13(3): 354~363
[68]彭晓明,丁明跃,周成平,等.一种序列图像配准的计算框架.中国图象图形学报,2005,10(4):441~449
[69]马浚,曹治国.基于边缘信息的红外与可见光图像匹配技术.计算机与数字工程, 2006,34(12):30~32
[70]高峰,文贡坚,吕金建.基于干线对的红外与可见光最优图像配准算法.计算机学报,2007,30(6):1015~1021
[71]臧丽,王敬东.基于互信息的红外与可见光图像快速配准.红外与激光工程,2008,37(1):164~168
[72]赵振兵,高强,李然,等.基于黑白CCD传感器的近红外热像在线监测方法.电力系统自动化,2005,29(8):83~86
[73]付冬梅,李晓刚,唐征花,等.电器设备故障红外诊断技术及其软件系统开发.电力系统自动化,2001,25(9):55~57
[74]何洪英,姚建刚,蒋正龙,等.基于支持向量机的高压绝缘子污秽等级红外热像检测.电力系统自动化,2005,29(24):70~74,82
[75]鲁守银,钱庆林,张斌,等.变电站设备巡检机器人的研制.电力系统自动化,2006,30(13):94~98
[76]张定庆,廖俊必,袁仲凡,等.图像匹配中噪声分析和预处理.四川大学学报(工程科学版),2002,34(4):80~84
[77]张文君,缪栋,付光远,等.基于Wigner滤波的配准图去噪应用研究.计算机工程与应用,2003,39(1):81~82,137
[78]曹治国,马怡伟,桑农,等.夜间图像匹配中的斑状噪声预处理.红外与激光工程,2004,33(3):278~281
[79]刘庆华,唐伯良,董戎萍.双目立体匹配图像对的预处理研究.计算机工程与设计,2005,26(3):793~795
[80]王海南,郝重阳.基于数学形态学的PET图像背景噪声处理算法.计算机应用研究,2007,24(9):312~314
[81]薛弘晔,李言俊,张科.侧抑制在红外噪声图像配准中应用.红外技术,2008,30(4):217~220
[82]孙造宇,梁甸农,张永胜.星载InSAR复图像多级配准及其误差分析.信号处理,2008,24(3):508~511
[83] Pluim J P W, Maintz J B A, Viergever M A. Mutual-information-based registration of medical images: a survey. IEEE Transactions on Medical Imaging, 2003, 22(8): 986~1004
[84] Vidakovic B, Lozoya C. On time-dependent wavelet denoising. IEEE Transactions Signal Processing, 1998,46(9):2549~2551
[85]谢杰成,张大力,徐立文.小波图象去噪综述.中国图象图形学报(A版),2002, 7(3):209~217
[86] Donoho D L, Johnstone I M. Wavelet shrinkage: asymptopia? Journal of the Royal Statistical Society, Series B, 1995,57(2): 301~369
[87] Donoho D L, Johnstone I M. Ideal spatial adaptation via wavelet shrinkage. Biometrika, 1994, 81: 425~455
[88]林哲民,康学雷,张立明.在小波域中进行图像噪声方差估计的EM方法.红外与毫米波学报,2001,20(3):199~202
[89] Immerker J. Fast noise variance estimation. Computer Vision and Image Understanding, 1996, 64(2):300~302
[90] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989,11(7):674~693
[91] Donoho D L. De-noising by soft-thresholding. IEEE Transactions on Information Theory, 1995, 41(3):613~627
[92] Comon P. Independent component analysis, a new concept? Signal Processing, 1994, 36(3):287~314
[93] Bell A J, Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6):1129~1159
[94] Lee T, Girolami M, Sejnowski T J. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation, 1999,11(2):417~441
[95] Hyv?rinen A, Oja E. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997,9(7):1483~1492
[96]焦卫东,杨世锡,吴昭同.基于独立分量分析的噪声消除技术研究.浙江大学学报(工学版),2004,38(7):872~876,930
[97]杨红卫,彭煊,王炳锡.基于ICA的周期性噪声消除算法.计算机研究与发展,2004,41(11):2012~2018
[98]范羚,吴小培,龙飞,等.基于独立分量分析的图像特征提取及去噪.计算机工程与应用,2003,39(9):107~109,126
[99] Hyv?rinen A. Independent component analysis: algorithms and applications. Neural Networks, 2000,(13):411~430
[100]杨俊安,庄镇泉,钟子发,等.基于独立分量分析和遗传算法的图象分离方法研究与实现.中国图象图形学报,2003, 8(4):441~446
[101] Hyv?rinen A. Fast and robust fixed-point algorithm for independent component analysis. IEEE Transactions on Neural Networks, 1999,10(3):626~634
[102]孔凡让,刘志刚,王建平,等.图象处理在直流电机换向火花图象监测中的应用.中国电机工程学报,2001,21(6):53~57
[103] Cole-Rhodes A A, Johnson K L, LeMoigne J, et al. Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient. IEEE Transactions on Image Processing, 2003,12(12):1495~1511
[104]刘斌,彭嘉雄.图像配准的小波分解方法.计算机辅助设计与图形学学报,2003,15(9):1070~1073
[105]张见威,韩国强,张艳青,等.基于互信息和小波分解的混合优化医学图像配准.海南大学学报(自然科学版),2006,24(4):406~411
[106]汤敏.结合形态学梯度互信息和多分辨率寻优的图像配准新方法.自动化学报,2008,34(3):246~205
[107] Lagarias J C, Reeds J A, Wright M H, et al. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal of Optimization,1998,9(1):112~147
[108] Lucchese L, Leorin S, Cortelazzo G M. Estimation of two-dimensional affine transformations through polar curve matching and its application to image mosaicking and remote-sensing data registration. IEEE Transactions on Image Processing, 2006,15(10):3008~3019
[109] Huang N E, Shen Zheng, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear non-stationary time series analysis. Proceeding of the Royal Society of London A, 1998,454(1971):903~995
[110] Huang N E, Wu M L, Long S R, et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proceeding of the Royal Society of London A, 2003,459(2037):2317~2345
[111]李天云,高磊,聂永辉,等.基于经验模式分解处理局部放电数据的自适应直接阈值算法.中国电机工程学报,2006,26(15):29~34
[112]杨世锡,胡劲松,吴昭同,等.旋转机械振动信号基于EMD的希尔伯特变换和小波变换时频分析比较.中国电机工程学报,2003,23(6):102~107
[113]沈国际,陶利民,陈仲生.多频信号经验模式分解的理论研究及应用.振动工程学报,2005,18(1):91~94
[114]彭志科,何永勇,褚福磊.小波尺度谱在振动信号分析中的应用研究.机械工程学报,2002, 38(3):122~126
[115]李夕兵,张义平,刘志祥,等.爆破震动信号的小波分析与HHT变换.爆炸与冲击,2005, 25(6):528~535
[116] Nunes J C, Bouaoune Y, Delechelle E, et al. Image analysis by bidimensional empirical mode decomposition. Image and Vision Computing, 2003,21(12): 1019~1026
[117] Christophe D, Sylvain M, Valérie P. A fast algorithm for bidimensional EMD. IEEE Signal Processing Letters, 2005, 12(10):701~704
[118]秦勃,时鹏.经验模式多尺度图像分解.工程图学学报,2002,23(4):73~78
[119]盖强,殷福亮.二维Hilbert-Huang变换的分解方法研究.电子与信息学报,2006, 28(4):610~613
[120]陈东方,王诚.一种改进的二维经验模式分解方法.武汉科技大学学报,2007, 30(2):79~82
[121] Koenderink J J. The structure of images. Biological Cybernetics, 1984, (50): 363~396
[122] Lindeberg T. Scale-space theory: A basic tool for analysing structures at different scales. Journal of Applied Statistics, 1994, 21(2):224~270
[123] Matas J, Chum O, Urban M, et al. Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing, 2004, 22(10):761~767
[124] Tuytelaars T, Van Gool L. Matching widely separated views based on affine invariant regions. International Journal on Computer Vision, 2004, 59(1):61~85
[125] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 2(4): 509~522
[126] Mikolajczyk K, Tuytelaars T, Schmid C, et al. A comparison of affine region detectors. International Journal of Computer Vision, 2005,65(1/2):43~72
[127] Bay H, Ess A, Tuytelaars T, et al. SURF: Speeded-Up Robust Features(SURF). Computer Vision and Image Understanding, 2008,110(3):346~359
[128]田裕鹏.红外检测与诊断技术.北京:化学工业出版社,2006. 140~145
[129]章毓晋.图像工程(中册):图像分析(第二版).北京:清华大学出版社,2006. 75~85
[130] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image automated cartography. Communications of the ACM, 1981, 24(6):381~395
[2]倪国强,刘琼.多源图像配准技术分析与展望.光电工程,2004,31(9):1~6
[3]王忆锋,张海联,李灿文,等.多传感器数据融合中的配准技术.红外与激光工程,1998,27(1):38~41
[4]钮永胜,倪国强.多传感器图像自动配准技术研究.光学技术,1999,(1): 16~18,20
[5]刘松涛,杨绍清.图像配准技术性能评估及实现概况.电光与控制,2007,14(3): 73~78, 83
[6]宋振环,唐伶俐,李传荣.遥感图像配准技术研究最新进展.科技导报,2007,25(12):53~58
[7] Li Hui, Manjunath B, Mitra S. A contour-based approach to multisensor image registration. IEEE Transactions on Image Processing, 1995,4(3):320~334
[8] Dana K, Anandan P. Registration of visible and infrared images. SPIE, 1993, (1957):2~13
[9] Li Hui, Zhou Yi-Tong. Automatic visual/infrared image registration. Optical Engineering, 1996,35(2):391~400
[10]孙才新.输变电设备状态在线监测与诊断技术现状和前景.中国电力,2005,38(2):1~7
[11]徐大可,张元林,王文胜.变电站电气设备在线监测综述.电力自动化设备,2001,21(9):54~57
[12] Brown L G. A survey of image registration techniques. ACM Computing Surveys, 1992,24(4):325~376
[13] Gao Zhi-Yong, Zhen Kang-Shen, Yi Ping-Jing. Two-stage correlation method in low SNR for fast image registration. In: Proceedings of IEEE, 1991:337~341
[14]薛模根,韩裕生,李从利,等.基于区域同名度矩阵的连续帧图像配准.中国图象图形学报,2006, 11(1):53~59
[15]赵辉,陈辉,于泓.一种改进的全景图自动拼接算法.中国图象图形学报,2007, 12(2):336~342
[16] Kaneko S, Satoh Y, Igarashi S. Using selective correlation coefficient for robust image registration. Pattern Recognition, 2003,36(5):1165~1173
[17]李晓明,赵训坡,郑链,等.基于Fourier-Mellin变换的图像配准方法及应用扩展.计算机学报,2006,29(3):466~472
[18]刘卫光,崔江涛,周利华.插值和相位相关的图像亚像素配准方法.计算机辅助设计与图形学学报,2005,17(6):1273~1277
[19] Chen Q S, Defrise M, Deconinck F. Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(12): 1156~1168
[20] Anuta P E. Spatial registrations of multispectral and multitemporal digital imagery using fast Fourier transform. IEEE Transactions on Geoscience Electronics, 1970, (8):353~368
[21]时永刚,邹谋炎.图像配准中统计型相似性测度的比较与分析方法.计算机学报,2004,27(9):1278~1283
[22] Viola P, Wells W. Alignment by maximization of mutual information. In: Proceedings of the 5th Int. Conf. Computer Vision, Boston, MA, June 1995,16~23
[23] Collignon A, Maes F, Vandermeulen D, et al. Automated multimodality image registration using information theory. In: Proceedings of the Information Processing in Medical Imaging Conference, Dordrecht, June 1995,263~274
[24]张见威,韩国强,沃焱.基于边界距离场互信息的图像配准方法.通信学报,2006,27(7):87~93
[25]钮永胜,倪国强.基于小波分析的可见光图像自动配准方法研究.高技术通讯,1999,(9):24~27
[26] Tuytelaars T, Mikolajczyk K. Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, 2008,3(3):177~280
[27] Moravec H. Rover visual obstacle avoidance. In: International Joint Conference on Artificial Intelligence, Vancouver, Canada, 1981,785~790
[28] Harris C, Stephens M. A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, Manchester, UK, 1988,147~151
[29] Lindeberg T. Feature detection with automatic scale selection. International Journal of Computer Vision, 1998,30(2):79~116
[30] Mikolajczyk K, Schmid C. Indexing based on scale invariant interest points. In: Proceedings of the 8th International Conference on Computer Vision, Vancouver, Canada, 2001,525~531
[31] Lowe D. Object recognition from local scale-invariant features. In: International Conference on Computer Vision, Corfu, Greece, 1999,1150~1157
[32] Kadir T, Brady M. Scale, saliency and image description. International Journal of Computer Vision, 2001,45(2):83~105
[33] Mikolajczyk K, Schmid C. Scale and affine invariant interest point detectors. International Journal of Computer Vision, 2004,60(1):63~86
[34] Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(10): 1615~1630
[35] Gool L, Moons T, Ungureanu D. Affine/photometric invariants for planar intensity patterns. In: Proceedings of the 4th European Conference Computer Vision, 1996, 642~651
[36] Schaffalitzky F, Zisserman A. Multi-view matching for unordered image sets. In: Proceedings of the 7th European Conference Computer Vision, 2002,414~431
[37] Freeman W, Adelson E. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991,13(9):891~906
[38] Lowe D. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004,60(2):91~110
[39] Bay H, Tuytelaars T, Gool L. SURF: Speeded Up Robust Features. In: Proceedings of the 9th European Conference Computer Vision, Graz, Austria, 2006,404~417
[40] Ke Y, Sukthankar R. PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington DC, USA, 2004, 506~513
[41] Abdel-Hakim A, Farag A. CSIFT: A SIFT descriptor with color invariant characteristics. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006,(2):1978~1983
[42] Ros J, Laurent C. Description of local singularities for image registration. In: The 18th International Conference on Pattern Recognition, 2006,(4):61~64
[43] Cheung W, Hamarneh G. N-SIFT: N-dimensional scale invariant feature transform for matching medical images. In: The 4th IEEE International Symposium on Biomedical Imaging, 2007,720~723
[44] Kelman A, Sofka M, Stewart C. Keypoint descriptors for matching across multiple image modalities and non-linear intensity variations. In: The IEEE Conference on Computer Vision and Pattern Recognition, 2007,1~7
[45]李晓明,郑链,胡占义.基于SIFT特征的遥感影像自动配准.遥感学报,2006,10(6):885~892
[46]厉茂海,洪炳镕,罗荣华,等.基于单目视觉的移动机器人全局定位.机器人,2007,29(2):140~144,178
[47] Chen Jian, Tian Jie. Rapid multi-modality preregistration based on SIFT descriptor. In: Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, 2006,1437~1440
[48] Bay H, Fasel B, Gool L. Interactive museum guide: fast and robust recognition of museum objects. In: Proceedings of the 1st International Workshop on Mobile Vision, Graz, Austria, May 2006
[49] Murillo A, Guerrero J, Sagüés C. SURF features for efficient robot localization with omnidirectional images. In: The IEEE International Conference on Robotics and Automation, Roma, Italy, April 2007,3901~3907
[50] Coiras E, Santamaria J, Miravet C. A segment-based registration technique for visual-ir images. Optical Engineering, 2000,39(1):282~289
[51] Yang Xiang-Jie, Sheng Yun-Long, et al. Robust multisensor image registration with partial distance merits. In: Proceedings of the 3rd International Conference on Information Fusion, Roma, 2000,(1):23~29
[52] Hines G, Rahman Z, Jobson D, et al. Multi-image registration for an enhanced vision system. In: Proceedings of the SPIE, 2003,(5108):231~241
[53] Wang Jian-Gang, Sung E, Venkateswarlu R. Registration of infrared and visible spectrum imagery for face recognition. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, 2004, 638~644
[54] ?egviéS. A multimodal image registration technique for structured polygonal scenes. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia, 2005,500~505
[55] Fan Xiao-Feng, Rho H, Saber E. Automatic registration of multi-sensor airborne imagery. In: The 34th Applied Imagery and Pattern Recognition Workshop, 2005, 81~86
[56] Hild M, Umeda G. Image registration in stereo-based multi-modal imaging systems. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005,70~75
[57] Kim K, Lee J, Ra J. Robust multi-sensor image registration by enhancing statistical correlation. In: The 8th International Conference on Information Fusion, 2005, 380~386
[58] Kern J, Pattichis M. Robust multispectral image registration using mutual information models. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(5):1494~1505
[59] Istenic R, Heric D, Ribaric S, et al. Thermal and visual image registration in hough parameter space. In: The 14th International Conference on System, Signal and Image Processing, and 6th EURASIP Conference Focused on Speech and Image Processing, Multimedia Communications and Services, 2007, 106~109
[60] Li Tie-Jun, Chen Zhe, Wang Ren-Xiang. Fuzzy feature matching between infrared image and optical image. In: Proceedings of the SPIE, 2001,(4602):260~265
[61] Huang Xi-Shan, Chen Zhe. A wavelet-based multisensor image registration algorithm. In: The 6th International Conference on Signal Processing, 2002, (1): 773~776
[62]曾文锋.红外与可见光图像融合中的快速配准方法.红外与激光工程,2002, 31(2):158~160
[63]舒丽霞,彭晓明,周成平,等.一种新颖的红外与可见光图像自动配准算法.华中科技大学学报,2003,31(6):92~94
[64]陶冰洁,王敬儒,张启衡.采用仿射变换的红外与可见光图像配准方法.光电工程,2004,31(11):39~41
[65]陈煜,田裕鹏.图像融合中的图像配准方法研究.激光与红外,2004, 34(1): 34~36
[66]刘卫光,郭师红,周利华.红外与可见光图像实时配准融合系统.红外技术,2004,26(5):66~70
[67]刘松涛,王学伟,周晓东,等.基于传感器参数和目标轮廓中心的自动配准算法研究.光学精密工程,2005,13(3): 354~363
[68]彭晓明,丁明跃,周成平,等.一种序列图像配准的计算框架.中国图象图形学报,2005,10(4):441~449
[69]马浚,曹治国.基于边缘信息的红外与可见光图像匹配技术.计算机与数字工程, 2006,34(12):30~32
[70]高峰,文贡坚,吕金建.基于干线对的红外与可见光最优图像配准算法.计算机学报,2007,30(6):1015~1021
[71]臧丽,王敬东.基于互信息的红外与可见光图像快速配准.红外与激光工程,2008,37(1):164~168
[72]赵振兵,高强,李然,等.基于黑白CCD传感器的近红外热像在线监测方法.电力系统自动化,2005,29(8):83~86
[73]付冬梅,李晓刚,唐征花,等.电器设备故障红外诊断技术及其软件系统开发.电力系统自动化,2001,25(9):55~57
[74]何洪英,姚建刚,蒋正龙,等.基于支持向量机的高压绝缘子污秽等级红外热像检测.电力系统自动化,2005,29(24):70~74,82
[75]鲁守银,钱庆林,张斌,等.变电站设备巡检机器人的研制.电力系统自动化,2006,30(13):94~98
[76]张定庆,廖俊必,袁仲凡,等.图像匹配中噪声分析和预处理.四川大学学报(工程科学版),2002,34(4):80~84
[77]张文君,缪栋,付光远,等.基于Wigner滤波的配准图去噪应用研究.计算机工程与应用,2003,39(1):81~82,137
[78]曹治国,马怡伟,桑农,等.夜间图像匹配中的斑状噪声预处理.红外与激光工程,2004,33(3):278~281
[79]刘庆华,唐伯良,董戎萍.双目立体匹配图像对的预处理研究.计算机工程与设计,2005,26(3):793~795
[80]王海南,郝重阳.基于数学形态学的PET图像背景噪声处理算法.计算机应用研究,2007,24(9):312~314
[81]薛弘晔,李言俊,张科.侧抑制在红外噪声图像配准中应用.红外技术,2008,30(4):217~220
[82]孙造宇,梁甸农,张永胜.星载InSAR复图像多级配准及其误差分析.信号处理,2008,24(3):508~511
[83] Pluim J P W, Maintz J B A, Viergever M A. Mutual-information-based registration of medical images: a survey. IEEE Transactions on Medical Imaging, 2003, 22(8): 986~1004
[84] Vidakovic B, Lozoya C. On time-dependent wavelet denoising. IEEE Transactions Signal Processing, 1998,46(9):2549~2551
[85]谢杰成,张大力,徐立文.小波图象去噪综述.中国图象图形学报(A版),2002, 7(3):209~217
[86] Donoho D L, Johnstone I M. Wavelet shrinkage: asymptopia? Journal of the Royal Statistical Society, Series B, 1995,57(2): 301~369
[87] Donoho D L, Johnstone I M. Ideal spatial adaptation via wavelet shrinkage. Biometrika, 1994, 81: 425~455
[88]林哲民,康学雷,张立明.在小波域中进行图像噪声方差估计的EM方法.红外与毫米波学报,2001,20(3):199~202
[89] Immerker J. Fast noise variance estimation. Computer Vision and Image Understanding, 1996, 64(2):300~302
[90] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989,11(7):674~693
[91] Donoho D L. De-noising by soft-thresholding. IEEE Transactions on Information Theory, 1995, 41(3):613~627
[92] Comon P. Independent component analysis, a new concept? Signal Processing, 1994, 36(3):287~314
[93] Bell A J, Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6):1129~1159
[94] Lee T, Girolami M, Sejnowski T J. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Computation, 1999,11(2):417~441
[95] Hyv?rinen A, Oja E. A fast fixed-point algorithm for independent component analysis. Neural Computation, 1997,9(7):1483~1492
[96]焦卫东,杨世锡,吴昭同.基于独立分量分析的噪声消除技术研究.浙江大学学报(工学版),2004,38(7):872~876,930
[97]杨红卫,彭煊,王炳锡.基于ICA的周期性噪声消除算法.计算机研究与发展,2004,41(11):2012~2018
[98]范羚,吴小培,龙飞,等.基于独立分量分析的图像特征提取及去噪.计算机工程与应用,2003,39(9):107~109,126
[99] Hyv?rinen A. Independent component analysis: algorithms and applications. Neural Networks, 2000,(13):411~430
[100]杨俊安,庄镇泉,钟子发,等.基于独立分量分析和遗传算法的图象分离方法研究与实现.中国图象图形学报,2003, 8(4):441~446
[101] Hyv?rinen A. Fast and robust fixed-point algorithm for independent component analysis. IEEE Transactions on Neural Networks, 1999,10(3):626~634
[102]孔凡让,刘志刚,王建平,等.图象处理在直流电机换向火花图象监测中的应用.中国电机工程学报,2001,21(6):53~57
[103] Cole-Rhodes A A, Johnson K L, LeMoigne J, et al. Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient. IEEE Transactions on Image Processing, 2003,12(12):1495~1511
[104]刘斌,彭嘉雄.图像配准的小波分解方法.计算机辅助设计与图形学学报,2003,15(9):1070~1073
[105]张见威,韩国强,张艳青,等.基于互信息和小波分解的混合优化医学图像配准.海南大学学报(自然科学版),2006,24(4):406~411
[106]汤敏.结合形态学梯度互信息和多分辨率寻优的图像配准新方法.自动化学报,2008,34(3):246~205
[107] Lagarias J C, Reeds J A, Wright M H, et al. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal of Optimization,1998,9(1):112~147
[108] Lucchese L, Leorin S, Cortelazzo G M. Estimation of two-dimensional affine transformations through polar curve matching and its application to image mosaicking and remote-sensing data registration. IEEE Transactions on Image Processing, 2006,15(10):3008~3019
[109] Huang N E, Shen Zheng, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear non-stationary time series analysis. Proceeding of the Royal Society of London A, 1998,454(1971):903~995
[110] Huang N E, Wu M L, Long S R, et al. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proceeding of the Royal Society of London A, 2003,459(2037):2317~2345
[111]李天云,高磊,聂永辉,等.基于经验模式分解处理局部放电数据的自适应直接阈值算法.中国电机工程学报,2006,26(15):29~34
[112]杨世锡,胡劲松,吴昭同,等.旋转机械振动信号基于EMD的希尔伯特变换和小波变换时频分析比较.中国电机工程学报,2003,23(6):102~107
[113]沈国际,陶利民,陈仲生.多频信号经验模式分解的理论研究及应用.振动工程学报,2005,18(1):91~94
[114]彭志科,何永勇,褚福磊.小波尺度谱在振动信号分析中的应用研究.机械工程学报,2002, 38(3):122~126
[115]李夕兵,张义平,刘志祥,等.爆破震动信号的小波分析与HHT变换.爆炸与冲击,2005, 25(6):528~535
[116] Nunes J C, Bouaoune Y, Delechelle E, et al. Image analysis by bidimensional empirical mode decomposition. Image and Vision Computing, 2003,21(12): 1019~1026
[117] Christophe D, Sylvain M, Valérie P. A fast algorithm for bidimensional EMD. IEEE Signal Processing Letters, 2005, 12(10):701~704
[118]秦勃,时鹏.经验模式多尺度图像分解.工程图学学报,2002,23(4):73~78
[119]盖强,殷福亮.二维Hilbert-Huang变换的分解方法研究.电子与信息学报,2006, 28(4):610~613
[120]陈东方,王诚.一种改进的二维经验模式分解方法.武汉科技大学学报,2007, 30(2):79~82
[121] Koenderink J J. The structure of images. Biological Cybernetics, 1984, (50): 363~396
[122] Lindeberg T. Scale-space theory: A basic tool for analysing structures at different scales. Journal of Applied Statistics, 1994, 21(2):224~270
[123] Matas J, Chum O, Urban M, et al. Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing, 2004, 22(10):761~767
[124] Tuytelaars T, Van Gool L. Matching widely separated views based on affine invariant regions. International Journal on Computer Vision, 2004, 59(1):61~85
[125] Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 2(4): 509~522
[126] Mikolajczyk K, Tuytelaars T, Schmid C, et al. A comparison of affine region detectors. International Journal of Computer Vision, 2005,65(1/2):43~72
[127] Bay H, Ess A, Tuytelaars T, et al. SURF: Speeded-Up Robust Features(SURF). Computer Vision and Image Understanding, 2008,110(3):346~359
[128]田裕鹏.红外检测与诊断技术.北京:化学工业出版社,2006. 140~145
[129]章毓晋.图像工程(中册):图像分析(第二版).北京:清华大学出版社,2006. 75~85
[130] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image automated cartography. Communications of the ACM, 1981, 24(6):381~395
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