视频监控系统中一些关键技术的研究
|
摘要
视频监控系统作为人类视觉功能的延伸，在许多应用中发挥着越来越重要的作用，有着十分重要的价值。近年来，随着电子信息技术特别是计算机视觉与多媒体技术、通信技术的兴起与发展，视频监控领域的研究取得了许多重大突破，视频信息的智能分析与处理、视频信息的压缩与传输是最活跃的两个方面，其中值得研究的问题是开放和无止境的。本文对视频监控系统中的人脸识别、视频增强和视频编码优化三个基本问题进行了较为深入的研究，并提出了一些新的方法。
第一，人脸识别在视频监控系统中扮演着关键的角色。虽然自动人脸识别（Automatic Face Recognition，AFR）技术发展到今天已经非常成熟，但是在非可控环境下仍然存在许多问题，尤其是光照条件发生变化时，识别效果会明显下降。视频监控系统全天候工作的特点使得采集的人脸图像无法避免光照的影响，因此克服光照变化对人脸识别的影响在视频监控应用中就尤为重要。本文首先深入研究了人脸识别中的光照问题，然后基于人类视觉系统的人脸光照模型，提出了一种新的光照不变人脸特征描述方法，主要原理是采用轮廓波变换（ContourletTransform，CT）处理对数域的人脸图像，分解得到低频子带和高频子带，然后利用轮廓波去噪模型，保持低频系数不变，改变高频系数后使用轮廓波逆变换（Inverse Contourlet Transform，ICT）重构图像，进而估计光照模型中的光照不变成分。本文还提出一种基于混合投影函数的人脸特征描述方法，该方法对人脸图像进行分块处理并利用混合投影函数和图像熵构建特征矩阵，由于混合投影函数对光照变化和随机噪声不敏感，图像的分块处理能减弱人脸图像局部变化的影响，因此是一种良好的光照鲁棒性人脸特征描述方法。实验表明，这两种方法均能有效提高光照变化条件下的识别率。
第二，视频增强是视频监控系统中的关键技术之一。通常视频监控系统要求能够24小时工作，由于夜间亮度不足造成监控视频质量低下，给后续的分析与识别带来很多困难，因此提高夜间视频的视觉质量就非常重要。本文提出一种使用小波变换（Wavelet Transform，WT）的夜间视频增强算法，该方法首先结合色彩空间转换和小波变换来分离视频的光照成分，并对监控视频作运动检测和背景估计，借助相同场景的白天高质量视频，提出一种新的融合规则来进行视频增强，最后重构彩色的图像和视频。实验表明，该方法能非常有效的提高夜间视频的视觉质量。
第三，视频编码优化在视频监控系统中也非常重要。新一代视频编码标准H.264/AVC及可伸缩视频编码（Scalable Video Coding，SVC）的提出使得视频数据的压缩格式越来越先进，但这是以提高计算复杂度为代价的。视频监控系统中海量的视频数据与有限的存储和网络资源的矛盾，实时性处理的需求与嵌入式设备有限的计算能力和能耗的矛盾，成为视频监控系统的瓶颈，这就使得提高视频的编码速度非常必要。本文首先提出一种适宜于H.264/AVC的帧间快速视频编码算法，该方法首先利用运动和纹理预测可能的编码模式，排除可能性较小的模式，然后利用时间和空间的相关性预测各种模式的可能性大小及编码顺序，并结合相关性和量化参数利用率失真和残差系数提前终止。实验表明，该算法能够有效的提高编码的速度，同时对编码效率的影响很小。本文还基于质量可伸缩编码的特征，并结合层间相关性、残差系数和运动向量，提出了一种质量可伸缩编码中增强层帧间快速编码算法。实验表明，该算法在编码效率损失很小的情况下能有效的提高编码速度，且特别适合应用于运动复杂的视频序列。
As the extension of the human visual system, video surveillance system is ofincreasing importance in many applications and has extremely significant realisticvalue today. Most recently, with the development of electronics and informationtechnology, especially computer vision and multimedia technology, communicationstechnology, numerous significant breakthroughs are achieved in the research area ofvideo surveillance. Especially, intelligent analysis and processing of video information,and video compression and transmission, which are two of the most fundamentalaspects in video surveillance, cover a number of valuable research problems which areopen and endless. In this dissertation, we investigate several key problems and proposesome new methods for video surveillance systems. Three basic problems are covered:face recognition, video enhancement and the optimization of video codec systems.
Firstly, face recognition plays a key role in video surveillance. Although automaticface recognition technique has been mature, however, most existing face recognitionsystems succeeded by restricting themselves to controlled environments, especiallywhen the lighting condition changes, the recognition rate was significantly decreased.The all-weather video surveillance systems make the collection of facial image unableto avoid the influence of the light conditions, and thus it is very necessary to overcomethe bad effects of illumination change for face recognition in video surveillance. Thisdissertation firstly makes an intensive study on the illumination robust automatic facerecognition. Afterwards, based on the facial illumination model from human visualsystem, a new illumination invariant facial feature description method is proposed. Thismethod adopts contourlet transform to process human facial images in Logarithmicdomain to obtain low frequency subbands and high frequency subbands, and then keepthe low frequency subband coefficients unchanged and utilizes contourlet denoisingmodel to modify the high frequency subband coefficients, and finally applies inversecontourlet transform to reconstruct the facial images and to get the estimation of theillumination invariant components. In addition, this dissertation proposes another facial feature description method base on hybrid projection function. This method divides thefacial images into several non-overlapping blocks so that the local image distortion willless affect the recognition result, and then uses the hybrid projection function andimage entropy to extract the facial features and construct the feature vector forrecognition. Since the hybrid projection function is not sensitive to illuminationchanges and random noises, this facial feature description method is illumination robustfor face recognition. The experimental results show that both methods can effectivelyimprove the recognition rate under illumination changes.
Secondly, video enhancement is one of the most important core technologies invideo surveillance. The video surveillance systems should work for24hours a day,while the poor light conditions in night bring lots of problems for the later analysis andrecognition tasks. Therefore, it is important to improve the visual perception quality ofnighttime video. In this dissertation, a new nighttime video enhancement algorithmusing wavelet transform is proposed. This method applies a new approach whichcombines the color space transform and wavelet transform to separate the illuminationcomponents from the video and makes motion detection and background estimation,then proposes a new video fusion rule for enhancement by exploiting the context from ahigh quality video captured in daytime from the same view, and finally reconstructs thecolor images and video. The experimental results demonstrate that the new method iseffective and competitive.
Thirdly, video codec optimization is very important in video surveillance systems.As the new generation of video coding standard, H.264/AVC and SVC achieve highcompression efficiency. However, this performance gain comes at the cost of anincreased computational complexity. The contradictions between the massive videodata and limited storage and network resources, the demand of real-time processing andlimited computing power and the energy consumption of the embedded devices,become the bottleneck for video surveillance systems, which makes the improvementof the video coding speed very necessary. This dissertation proposes a fast inter-frameprediction algorithm applied in H.264/AVC. This algorithm predicts the candidatemodes based on moving and texture for the current macro-block to exclude the codingmode with low probability at first, then predicts the possibility and the coding order forthe candidate modes of macro-blocks, at last early terminates coding by combining the correlation and the quantization parameters. The experimental results show that theencoding speed can be improved effectively with negligible coding efficiency loss.Besides, by exploiting the characteristics of SVC, a fast inter-frame predictionalgorithm in enhancement layer of quality scalable video coding is proposed. Thisalgorithm combines the mode-distribution correlation between the base layer andenhancement layers, residuals and motion vectors, which is very suitable for theenhancement layer coding. The experimental results demonstrate that the new methodcan improve the coding speed with negligible loss and it is especially suitable for thecomplex video sequences.
第一，人脸识别在视频监控系统中扮演着关键的角色。虽然自动人脸识别（Automatic Face Recognition，AFR）技术发展到今天已经非常成熟，但是在非可控环境下仍然存在许多问题，尤其是光照条件发生变化时，识别效果会明显下降。视频监控系统全天候工作的特点使得采集的人脸图像无法避免光照的影响，因此克服光照变化对人脸识别的影响在视频监控应用中就尤为重要。本文首先深入研究了人脸识别中的光照问题，然后基于人类视觉系统的人脸光照模型，提出了一种新的光照不变人脸特征描述方法，主要原理是采用轮廓波变换（ContourletTransform，CT）处理对数域的人脸图像，分解得到低频子带和高频子带，然后利用轮廓波去噪模型，保持低频系数不变，改变高频系数后使用轮廓波逆变换（Inverse Contourlet Transform，ICT）重构图像，进而估计光照模型中的光照不变成分。本文还提出一种基于混合投影函数的人脸特征描述方法，该方法对人脸图像进行分块处理并利用混合投影函数和图像熵构建特征矩阵，由于混合投影函数对光照变化和随机噪声不敏感，图像的分块处理能减弱人脸图像局部变化的影响，因此是一种良好的光照鲁棒性人脸特征描述方法。实验表明，这两种方法均能有效提高光照变化条件下的识别率。
第二，视频增强是视频监控系统中的关键技术之一。通常视频监控系统要求能够24小时工作，由于夜间亮度不足造成监控视频质量低下，给后续的分析与识别带来很多困难，因此提高夜间视频的视觉质量就非常重要。本文提出一种使用小波变换（Wavelet Transform，WT）的夜间视频增强算法，该方法首先结合色彩空间转换和小波变换来分离视频的光照成分，并对监控视频作运动检测和背景估计，借助相同场景的白天高质量视频，提出一种新的融合规则来进行视频增强，最后重构彩色的图像和视频。实验表明，该方法能非常有效的提高夜间视频的视觉质量。
第三，视频编码优化在视频监控系统中也非常重要。新一代视频编码标准H.264/AVC及可伸缩视频编码（Scalable Video Coding，SVC）的提出使得视频数据的压缩格式越来越先进，但这是以提高计算复杂度为代价的。视频监控系统中海量的视频数据与有限的存储和网络资源的矛盾，实时性处理的需求与嵌入式设备有限的计算能力和能耗的矛盾，成为视频监控系统的瓶颈，这就使得提高视频的编码速度非常必要。本文首先提出一种适宜于H.264/AVC的帧间快速视频编码算法，该方法首先利用运动和纹理预测可能的编码模式，排除可能性较小的模式，然后利用时间和空间的相关性预测各种模式的可能性大小及编码顺序，并结合相关性和量化参数利用率失真和残差系数提前终止。实验表明，该算法能够有效的提高编码的速度，同时对编码效率的影响很小。本文还基于质量可伸缩编码的特征，并结合层间相关性、残差系数和运动向量，提出了一种质量可伸缩编码中增强层帧间快速编码算法。实验表明，该算法在编码效率损失很小的情况下能有效的提高编码速度，且特别适合应用于运动复杂的视频序列。
As the extension of the human visual system, video surveillance system is ofincreasing importance in many applications and has extremely significant realisticvalue today. Most recently, with the development of electronics and informationtechnology, especially computer vision and multimedia technology, communicationstechnology, numerous significant breakthroughs are achieved in the research area ofvideo surveillance. Especially, intelligent analysis and processing of video information,and video compression and transmission, which are two of the most fundamentalaspects in video surveillance, cover a number of valuable research problems which areopen and endless. In this dissertation, we investigate several key problems and proposesome new methods for video surveillance systems. Three basic problems are covered:face recognition, video enhancement and the optimization of video codec systems.
Firstly, face recognition plays a key role in video surveillance. Although automaticface recognition technique has been mature, however, most existing face recognitionsystems succeeded by restricting themselves to controlled environments, especiallywhen the lighting condition changes, the recognition rate was significantly decreased.The all-weather video surveillance systems make the collection of facial image unableto avoid the influence of the light conditions, and thus it is very necessary to overcomethe bad effects of illumination change for face recognition in video surveillance. Thisdissertation firstly makes an intensive study on the illumination robust automatic facerecognition. Afterwards, based on the facial illumination model from human visualsystem, a new illumination invariant facial feature description method is proposed. Thismethod adopts contourlet transform to process human facial images in Logarithmicdomain to obtain low frequency subbands and high frequency subbands, and then keepthe low frequency subband coefficients unchanged and utilizes contourlet denoisingmodel to modify the high frequency subband coefficients, and finally applies inversecontourlet transform to reconstruct the facial images and to get the estimation of theillumination invariant components. In addition, this dissertation proposes another facial feature description method base on hybrid projection function. This method divides thefacial images into several non-overlapping blocks so that the local image distortion willless affect the recognition result, and then uses the hybrid projection function andimage entropy to extract the facial features and construct the feature vector forrecognition. Since the hybrid projection function is not sensitive to illuminationchanges and random noises, this facial feature description method is illumination robustfor face recognition. The experimental results show that both methods can effectivelyimprove the recognition rate under illumination changes.
Secondly, video enhancement is one of the most important core technologies invideo surveillance. The video surveillance systems should work for24hours a day,while the poor light conditions in night bring lots of problems for the later analysis andrecognition tasks. Therefore, it is important to improve the visual perception quality ofnighttime video. In this dissertation, a new nighttime video enhancement algorithmusing wavelet transform is proposed. This method applies a new approach whichcombines the color space transform and wavelet transform to separate the illuminationcomponents from the video and makes motion detection and background estimation,then proposes a new video fusion rule for enhancement by exploiting the context from ahigh quality video captured in daytime from the same view, and finally reconstructs thecolor images and video. The experimental results demonstrate that the new method iseffective and competitive.
Thirdly, video codec optimization is very important in video surveillance systems.As the new generation of video coding standard, H.264/AVC and SVC achieve highcompression efficiency. However, this performance gain comes at the cost of anincreased computational complexity. The contradictions between the massive videodata and limited storage and network resources, the demand of real-time processing andlimited computing power and the energy consumption of the embedded devices,become the bottleneck for video surveillance systems, which makes the improvementof the video coding speed very necessary. This dissertation proposes a fast inter-frameprediction algorithm applied in H.264/AVC. This algorithm predicts the candidatemodes based on moving and texture for the current macro-block to exclude the codingmode with low probability at first, then predicts the possibility and the coding order forthe candidate modes of macro-blocks, at last early terminates coding by combining the correlation and the quantization parameters. The experimental results show that theencoding speed can be improved effectively with negligible coding efficiency loss.Besides, by exploiting the characteristics of SVC, a fast inter-frame predictionalgorithm in enhancement layer of quality scalable video coding is proposed. Thisalgorithm combines the mode-distribution correlation between the base layer andenhancement layers, residuals and motion vectors, which is very suitable for theenhancement layer coding. The experimental results demonstrate that the new methodcan improve the coding speed with negligible loss and it is especially suitable for thecomplex video sequences.
引文
[1] P. Remagnino, S. A. Velastin, G. L. Foresti,et al. Novel concepts and challenges for the nextgeneration of video surveillance systems[J]. Machine Vision and Applications,2007,18:135-137
[2]陈兵旗，孙明．实用数字图像处理与分析[M].北京：中国农业大学出版社、清华大学出版社，2007,1-257
[3]雷玉堂．安防视频监控实用技术[M].北京：电子工业出版社，2011,1-72
[4] W. Lin. New technologies for video surveillance systems[D]. Seattle: University ofWashington,2010
[5] R. T. Collins, A. J. Lipton, T. Kanade, et al. A system for video surveillance and monitoring:VSAM final report[R]. Pittsburgh: Carnegie Mellon University,2000
[6] A. Y. Johnson, A. F. Bobick. A multi-view method for gait recognition using static bodyparameters[C]. International Conference on Audio-and Video-Based Biometric PersonAuthentication, Halmstad, Sweden,2001,301-311
[7] Y. Tian, A. W. Senior, A. Hampapur, et al. IBM Smart Surveillance System (S3): eventbased video surveillance system with an open and extensible framework[J]. Machine Visionand Applications,2008,19:315-327
[8]张一．智能视频监控中的目标识别与异常行为建模与分析[D].上海：上海交通大学，2010
[9]宋波涛．智能视频监控系统的设计与实现[D].长春：吉林大学，2009
[10]孔晓东．智能视频监控技术研究[D].上海：上海交通大学，2008
[11]肖广．基于IP的视频监控若干关键技术研究[D].上海：华东师范大学，2008
[12]秦莉娟．基于内容的自动视频监控研究[D].杭州：浙江大学，2006
[13]杨广林．主动视频监控中若干问题的研究[D].秦皇岛：燕山大学，2006
[14]范金刚．智能视频监控系统若干关键技术的研究与实现[D].成都：电子科技大学，2006
[15] M. Gren.视频监控十五年发展回顾和未来趋势[J].中国公共安全（综合版），2011,11:132-135
[16]王素玉，沈兰荪．智能视觉监控技术研究进展[J].中国图像图形学报，2007,9(12):1506-1510
[17]马颂德，张正友．计算机视觉：计算理论与算法基础[M].北京：科学出版社，1998
[18]高文，陈熙霖．计算机视觉-算法与系统原理[M].北京：清华大学出版社，1999
[19] A. K. Jain, A. Ross, S. Prabhakar. An introduction to biometric recognition[J]. IEEETransactions on Circuits and Systems for Video Technology,2004,14(1):4-20
[20] A. Jain, L. Hong, S. Pankanti. Biometrics: promising frontiers for emerging identificationmarket[J]. Communications of The ACM,2000,43(2):90-98
[21]李建平，翟江涛，许川容．小波分析电子签名系统[J].后勤工程学院学报，2001,3:5-8
[22] R. Chellappa,C. L. Wilson, S. Sirohey.Human and machine recognition of faces:a survey[J].Proeeedings of the IEEE,1995,83(5):705-741
[23] W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld. Face recognition: a literature survey[J].ACM Computing Surveys,2003,35(4):399-458
[24] M. Yang, J. K. David, A. Narendra.Detecting faces in images: a survey[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2002,24(1):34-58
[25] J. G. Daugman. Face and gesture recognition:overview[J]. IEEE Transactions on PatternAnalysis and Machine Intelligence,1997,19(7):675-676
[26] P. J. Phillips, P. J. Flynn, T. Scruggs, et al. Overview of the face recognition grandchallenge[C]. IEEE Computer Society Conference on Computer Vision and PatternRecognition, San Diego, CA, USA,2005,947-954
[27] S. Z. Li, A. K. Jain. Handbook of face recognition[M]. Berlin:Springer,2005
[28]周激流，张晔．人脸识别理论研究进展[J].中国计算机辅助设计与图形学学报，1999,12(2):180-184
[29]周杰，卢春雨，张长水，等．人脸自动识别方法综述[J].电子学报，2000,28(4):102-106
[30]刘党辉，沈兰荪，K. Lam.人脸识别研究进展[J].电路与系统学报，2004,9(1):85-94
[31] R. Brunelli, T. Poggio Face recognition: features versus templates[J]. IEEE Transactions onPattern Analysis and Machine Intelligence,1993,15(10):1042-1052
[32]严严，章毓晋．基于视频的人脸识别研究进展[J].计算机学报，2009,32(5):878-886
[33] S. Zhou, V. Krueger, R. Chellappa. Probabilistic recognition of human faces from video[J].Computer Vision and Image Understanding,2003,91(1):214-245
[34]段锦，周春光，刘小华．三维人脸识别研究进展[J].小型微型计算机系统,2004,25(5):886-890
[35] L. Wiskott, J. Fellous, N. Kruger, et al. Face recognition by elastic graph matching[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,1997,19(7):775-779
[36] A. M. Martinez. Recognizing imprecisely localized, partially occluded, and expressionvariant faces from a single sample per class[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2002,25(6):748-763
[37]刘青山，卢汉清，马颂德．综述人脸识别中的子空间方法[J].自动化学报，2003,129(16):900-911
[38] M. A. Turk, A. P. Pentland. Eigenfaces for recognition[J]. Journal of CognitiveNeuroscience,1991,13(1):71-86
[39] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman. Eigenfaces vs.Fisherfaces:recognitionusing class specific linear projection[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1997,19(7):711-720
[40] X. He, S. Yan, Y. Hu, et al. Face recognition using Laplacianfaces[J]. IEEE Transactions onPattern Analysis and Machine Intelligence,2005,27(3):328-340
[41] M. S. Bartlett, J. R. Movellan, T. J. Sejnowski. Face recognition by independent componentanalysis [J]. IEEE Transactions on Neural Networks,2002,13(6):1450-1464
[42] J. Yang, D. Zhang. Two-dimensional PCA: A new approach to appearance-based facerepresentation and recognition [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2004,26(1):131-137
[43] G. Rajkiran, K. Vijayan. An improved face recognition technique based on modular PCAapproach [J]. Pattern Recognition Letters,2004,4:429-436
[44] S. Chen, Y. Zhu. Subpattern-based principal component analysis[J]. Pattern Recognition,2004,37(1):1081-1083
[45] G. C. Feng, P. C. Uuen, D. Q. Dai. Human face recognition using PCA on waveletsubband[J].SPIE Journal of Electronic Imaging,2000,9(2):226-233
[46] L. Dryden Ian, B. Li, J. Christopher Brignell, et al. Factored principal components analysis,with applications to face recognition[J]. Statistics and Computing,2009,19(3):229-238
[47] S. Lawrence, C. L. Giles, A. C. Tsoi, et al. Face recognition: a convolutional neural-networkapproach[J]. IEEE Transactions on Neural Networks,1997,8(1):98-113
[48]甘俊英，张有为．基于BP神经网络的人脸识别[J].系统工程与电子技术，2003,25(1):113-115
[49] S. Lin, S. Kung, L. Lin, Face recognition/detection by probabilistic decision-based neuralnetwork[J]. IEEE Transactions on Neural Networks,1997,8(1):114-132
[50] J. Lin, J. Ming, D. Crookes. Robust face recognition using posterior union model basedneural networks[J]. IET Computer Vision,2009,3(3):130-142
[51] A. K. Jain, P. W. Duin Robert, J. C. Mao. Statistical pattern recognition: a review [J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2000,22(1):4-37
[52] F. S. Samaria. Face recognition using hidden markov model[D]. Cambridge:University ofCambridge,1994
[53] A. V. Nefian, M. H. Hayes. Hidden markov models for face recognition[C]. IEEEInternational Conference on Acoustics, Speech and Signals Processings, Seattle, Washington,USA,1998,5:2721-2724
[54] G. Guo, S. Z. Li, K. L. Chan. Face recognition by support vector machines[C]. IEEEInternational Conference on Automatic Face and Gesture Recognition, Grenoble,2000,196-201
[55] A. Tefas, C. Kotropoulos, I. Pitas. Ioannis. Using support vector machines to enhance theperformance of elastic graph matching for frontal face authentication[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2001,23(7):735-746
[56] A. M. Bronstein, M. M. Bronstein, R. Kimmel. Expression-invariant representations offaces[J]. IEEE Transactions on Image Processing,2007,16(1):188-197
[57] Y. Adini, Y. Moses, S. Ullman. Face recognition: the problem of compensating for changesin illumination direction[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1997,19(7):721-732
[58] D. W. Jaeobs, P. N. Belhumeur, R. Basri. Comparing images under variable illumination[C].IEEE Computer Society Conference on Computer Vision and pattern Recognition, SantaBarbara, CA, USA,1998,610-617
[59]庄连生.复杂光照条件下人脸识别关键算法研究[D].合肥：中国科学技术大学，2006
[60]曹雪．小波理论在人脸识别中的应用研究[D].南京：南京理工大学，2011
[61]刘中华．光照变化条件下的人脸特征抽取算法研究[D].南京：南京理工大学，2011
[62] R. C. Gonzalez, R. E. Woods. Digital image processing[M]. Person Prentice Hall, NewJersey,2008
[63] M. Savvides, V. Kumar. Illumination normalization using logarithm transforms for faceauthentication[C]. International Conference on Audio-and Video-Based Biometric PersonAuthentication, Guildford, UK,2003,549-556
[64] S. M. Pizer, E. P. Amburm. Adaptive histogram equalization and its variation[J]. Computervision,graphics,and image processing,1987,39:355-368
[65] J. Kim, L. Kim, S. Hwang. An advanced contrast enhancement using partially overlappedsub-block histogram equalization [J]. IEEE Transactions on Circuits and Systems for VideoTechnology,2001,11(4):475-484
[66] P. Hsieh, P. Tung. Shadow compensation based on facial symmetry and image average forrobust face recognition[J]. Neurocomputing,2010,73(13-15):2708-2717
[67] A. S. Georghiades, P. N. Belhumeur. Illumination cone models for face recognition undervariable lighting Face[C]. IEEE Computer Society Conference on Computer Vision andPattern Recognition, Santa Barbara, CA, USA,1998,52-59
[68] A. S. Georghiades, P. N. Belhumeur, Kriegman, et al.From few to many: Illumination conemodels for face recognition under variable lighting and pose[J]. IEEE Transactions on PatternAnalysis and Machine Intelligence,2001,23(6):643-660
[69] R. Basri, D. W. Jacobs. Lambertian reflectance and linear subspaces[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2003,25(2):218-233
[70] V. Blanz, T. Vetter. Face recognition based on fitting a3D morphable model[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2003,25(9):1063-1074
[71] Y. Wang, L. Zhang, Z. C. Liu, et al. Face Relighting from a Single Image under ArbitraryUnknown Lighting Conditions[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence.2009,31(11):1968-1984
[72] S. Z. Li, R. Chu, S. Liao, et al. Illumination invariant face recognition using near-infraredimages[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2007,29(4):627-639
[73] S. G. Kong, J. Heo, F. Boughorbel. Multiscale fusion of visible and thermal IR images forillumination-invariant face recognition[J]. International Journal of Computer Vision,2007,71(2):215-233
[74] W. Di, L. Zhang, D. Zhang, et al. Studies on hyperspectral face recognition in visiblespectrum with feature band selection[J]. IEEE Transactions on Systems, Man andCybernetics, Part A: Systems and Humans,2010,6(40):1354-1361
[75] G. Pan, S. Han, Z. Wu, et al.3D Face recognition using mapped depth images[C]. IEEEConference on Computer Vision and Pattern Recognition-Workshops, San Diego, CA, USA,2005,1-7
[76] H. F. Chen, P. N. Belhumeur, D. W. Jacobs. In search of illumination invariants[C]. IEEEConference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA,2000,1:1254-1261
[77] J. Ruiz-del-Solar, J. Quinteros. Illumination compensation and normalization ineigenspace-based face recognition: A comparative study of different pre-processingapproaches[J]. Pattern Recognition Letters,2008,29:1966-1979
[78]李粉兰，段海峰，郝建国，等．人脸识别中光照补偿问题的实验研究[J].工程图学学报，2009,3:113-120
[79] E. H. Land, J. J. McCann. Lightness and retinex theory[J]. Journal of the Optical Society ofAmerica,1971,61(1):1-11
[80] E. H. Land. The retinex theory of color vision[J]. Scientific American,1977,237(6):108-128
[81] D. J. Jobson, Z. Rahman, G. A. Woodell. Properties and performance of a center/surroundRetinex[J]. IEEE Transactions on Image Processing,1997,6(3):451-462
[82] D. J. Jobson, Z. Rahman, G. A. Woodell. A multiscale Retinex for bridging the gap betweencolor images and the human observation of scenes[J]. IEEE Transactions on ImageProcessing,1997,6(7):965-976
[83] Y. K. Park, S. L. Park, J. K. Kim. Retinex method based on adaptive smoothing forillumination invariant face recognition[J]. Signal Processing,2008,88:1929-1945
[84] M. Bertalmío, V. Caselles, E. Provenzi. Issues about retinex theory and contrastEnhancement[J]. International Journal of Computer Vision,2009,83:101-119
[85] A. Shashua, T. R. Raviv. The quotient image:Class-based recognition and synthesis undervarying illumination conditions[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2001,23(2):129-139
[86] H. Wang, S. J. Li,Y. Wang. Face recognition under varying lighting conditions using selfquotient image[C]. IEEE International Conference on Automatic Face and GestureRecognition, Seoul, Korea,2004,819-824
[87] T. Chen, W. Yin, X. Zhou, et al. Total variation models for variable lighting facerecognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(9):1519-1524
[88] X. He, J. Tian, L. Wu, et al. lllumination normalization with morphological quotient image[J].软件学报，2007,18(9):2318-2325
[89] T. Zhang, Y. Tang, B. Fang, et al. Face recognition under varying illumination usingGradientfaces[J]. IEEE Transactions on Image Processing,2009,18(11):2599-2606
[90] B. Zhang, H. Zhang, S. Ge. Face recognition by applying wavelet subband representation andkernel associative memory[J]. IEEE Transactions on Neural Networks,2004,15(1):166-177
[91] S. Du, R. K. Ward. Adaptive region-based image enhancement method for robust facerecognition under variable illumination conditions[J]. IEEE Transactions on Circuits andSystems for Video Technology,2010,99:1-12
[92] Y. Z. Goh, B. J. Andrew, K. O. Michael. Wavelet based illumination invariant preprocessingin face recognition[C]. International Congress on Image and Signal Processing, Sanya,2008,3:421-425
[93]龚卫国，杨利平，辜小花，等．基于多级小波分解的人脸图像光照补偿方法[J].光学精密工程，2008,16(8):1459-1464
[94] T. P. Zhang, B. Fang, Y. Yuan, et al. Multiscale facial structure representation for facerecognition under varying illumination[J]. Pattern Recognition,2009,42(2):251-258
[95]曹雪，余立功，杨静宇.基于小波变换和去噪模型的光照不变人脸识别[J].计算机应用，2011,31(8):2126-2129
[96] B. Takacs. Comparing face images using the modified Hausdorff distance[J]. PatternRecognition,1998,31:1873-1881
[97] T. Ojala, M. Pietikainen, M. Topi. Multiresolution gray-scale and rotation invariant textureclassification with local binary patterns[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2002,24(7):971-987
[98] X. Tan, Bill Triggs. Enhanced local texture feature sets for face recognition under difficultlighting conditions[J]. IEEE Transactions on Image Processing,2010,19(6):1635-1650
[99] W. Chen, M. J. Er, S. Wu. Illumination compensation and normalization for robust facerecognition using discrete cosine transform in logarithm domain[J]. IEEE Transactions onSystems, Man, and Cybernetics, Part B: Cybernetics,2006,36(2):458-466
[100] B. Zhang, S. Shan, X. Chen, et al. Histogram of Gabor phase patterns (HGPP): a novel objectrepresentation approach for face recognition [J]. IEEE Transactions on Image Processing,2007,16(1):57-68
[101] Y. Y. Huang, J. P. Li, G. D. Duan, et al. Face recognition using illumination invariantfeatures in contourlet domain[C]. IEEE International Conference on ApperceivingComputing and Intelligence Analysis, Chengdu,2010,294-297
[102] S. G Mallat. A theory for multiresolution signal decomposition: the wavelet representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligences,1989,11(7):674-693
[103] Ingrid Daubechies.小波十讲[M].(李建平，杨万年).北京：国防工业出版社，2004
[104] S. Jaffard, Y. Meyer, R. D. Ryan.科学技术中的小波分析[M].(李建平等).北京：国防工业出版社，2006
[105]李建平.非常规小波变换与军事生物信息安全[M].成都：电子科技大学出版社，2008
[106] M. N. Do, M. Vetterli. The Contourlet transform: an efficient directional multiresolutionimage representation[J]. IEEE Transactions on Image Processing,2005,14(12):2091-2106
[107]戴维，于盛林，孙栓.基于Contourlet变换自适应阈值的图像去噪算法[J].电子学报，2007,35(10):1939-1943
[108]易文娟，郁梅，蒋刚毅.Contourlet：一种有效的方向多尺度变换分析方法[J].计算机应用研究，2006,9:18-22
[109]焦李成，谭山.图像的多尺度几何分析：回顾和展望[J].电子学报，2004,31(B12):1975-1981
[110]李蓓蓓. Contourlet变换及其在图像降噪中的应用[D].长春：吉林大学，2007
[111] D. L. Donoho, I. M. Johnstone. Adapting to unknown smoothness via wavelet shrinkage[J].Journal of the American Statistical Association,1995,90(432):1200-1224
[112] D. L. Donoho. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory,1995,41(3):613-627
[113] S. Chang, B. Yu, M. Vetterli. Adaptive wavelet thresholding for image denoising andcompression[J]. IEEE Transactions on Image Processing,2000,9(9):1532-1546
[114] T. Sim, S. Baker, M. Bsat. The CMU pose, illumination, and expression (PIE) database[C].IEEE International Conference on Automatic Face and Gesture Recognition, Washington,DC, USA,2002,46-51
[115] T. M. Cover, P. E. Hart. Nearest neighbor pattern classification[J]. IEEE Transactions onInformation Theory,1967,13(1):21-27
[116]黄源源，李建平．多尺度变换域内混合投影熵的人脸特征描述[J].计算机工程与应用，2011,47(12):5-6
[117] J. Zhang, C. Chen. Local variance projection log energy entropy features for illuminationrobust face recognition[C]. International Symposium on Biometrics and SecurityTechnologies, Islamabad, Pakistan,2008,1-5
[118] T. Kanade. Picture processing by computer complex and recognition of human faces[D].Kyoto: Kyoto University,1973
[119] G. C. Feng, P. C. Yuen. Variance projection function and its application to eye detection forhuman face recognition[J]. Pattern Recognition Letters,1998,19(9):899-906
[120]耿新，周志华，陈世福．基于混合投影函数的眼睛定位[J].软件学报,2002,14(8):1394-1400
[121] Z. Zhou, X. Geng, Projection functions for eye detection[J]. Pattern Recognition,2004,37:1049-1056
[122]邵平，杨路明，蒋永忠．广义行－列积分图及其在快速投影算法中的应用[J].小型微型计算机系统，2008,29(5):954-958
[123] C. E. Shannon. A mathematical theory for communication[J]. Bell System Technical Journal,1948,27:379-423
[124]张永亮，卢焕章．基于图像局部熵的红外图像分割方法[J].红外技术，2006,28(11):656-660
[125] Y. Y. Huang, J. P. Li, J. Lin, et al. Robust face recognition by combining waveletdecomposition and local hybrid projection entropy[C]. IEEE International Conference onApperceiving Computing and Intelligence Analysis, Chengdu,2009,325-328
[126] R. M. Haralick, Dinstein, K. Shanmugam. Textural features for image classification[J]. IEEETransactions on Systems, Man, and Cybernetics,1973,3(6):610-621
[127] Y. Y. Huang, J. Z. Chen, J. P. Li, et al. Face recognition based on texture features usinghybrid projection function[C]. International Conference on Wavelet Active MediaTechnology and Information Processing, Chengdu,2012,82-85
[128]饶云波.夜间视频增强的关键技术研究[D].成都：电子科技大学，2012
[129] Y. Rao, W. Lin, L. Chen. A global motion estimation based method for nighttime videoenhancement[J]. Optical Engineering,2011,50(5):1-7
[130] Q. Wang, R. K. Ward. Fast image/video contrast enhancement based on weightedthresholded histogram equalization[J]. IEEE Transactions on Consumer Electronics,2007,53(2):757-764
[131] T. Arici, S. Dikbas, Y. Altunbasak. A histogram modification framework and its applicationfor image contrast enhancement[J]. IEEE Transactions on Image Processing,2009,18(9):1921-1935
[132] E. P. Bennett, L. McMillan. Video enhancement using per-pixel virtual exposures[J]. ACMTransactions on Graphics,2005,24(3):845-852
[133]刘贵喜，赵曙光，陈文锦.红外与可见光图像融合的多分辨率方法[J].光电子·激光，2004,15(8):980-984
[134] A. Ilie, R. Raskar, J. Yu. Gradient domain context enhancement for fixed cameras[J].International Journal of Pattern Recognition and Artificial Intelligence,2005,19(4):533-549
[135] Y. Rao, W. Lin, L. Chen. Image-based fusion for video enhancement of nighttimesurveillance[J]. Optic Engineering,2010,49(12):1-3
[136] Y. Cai, K. Huang, T. Tan, et al. Context enhancement of nighttime surveillance by imagefusion[C]. IEEE International Conference on Pattern Recognition, Hong Kong,2006,1:980-983
[137]侯雷，饶云波.一种亮度融合的视频增强方法[J].光电工程，2011,38(8):132-138
[138]邓加琼.复杂背景下的彩色人脸检测算法与实现[D].成都：电子科技大学，2005
[139] G. Mittal, S. Locharam, S. Sasi, et al. An efficient video enhancement method using LA*B*analysis[C]. IEEE International Conference on Video and Signal based Surveillance, Sydney,Australia,2006,66-71
[140]卢官明，郎苏娟.基于YCbCr颜色空间的背景建模及运动目标检测[J].南京邮电大学学报（自然科学版），2009,29(6):17-22
[141] P. Kumar, K. Sengupta, A. Lee. A comparative study of different color spaces for foregroundand shadow detection for traffic monitoring system[C]. IEEE International Conference onIntelligent Transportation systems, Singapore,2002,100-105
[142]钟庆，戴礼荣，宋彦.融合两种运动信息的分级运动检测算法[J].模式识别与人工智能，2002,18(5):552-557
[143]钟庆．智能视频监控中的运动检测算法研究[D].合肥：中国科学技术大学，2004
[144]王德松．基于生物特征信息隐藏与身份认证及其应用研究[D].成都：电子科技大学，2012
[145]孙延奎．小波分析及其应用[M].北京：机械工业出版社，2005,89-117
[146]陈恒鑫.基于多尺度分析的变化光照下人脸识别研究[D].重庆：重庆大学，2010.
[147]谢杰成，张大力，徐文立.小波图象去噪综述[J].中国图象图形学报,2002,7A(3):209-217
[148] A. Yilmaz, O. Javed, M. Shah. Object Tracking: A Survey[J]. ACM Computing Surveys,2006,38(4):1-45
[149] W. Hu, T. Tan, L. Wang, et al. A survey on visual surveillance of object motion andbehaviors[J]. IEEE Transactions on System, Man and Cybernetics, Part C: Applications andReviews,2004,34(3):334-352
[150]史志龙，陈一民，徐杰.视频监控中人的运动检测方法研究与实现[J].计算机工程与应用，2008,44(19):206-209
[151] S. S. Beauchemin, J. L. Barron. The computation of optical flow[J]. ACM computing surveys,1995,27(3):433-466
[152] S. Negahdaripour. Revised definition of optical flow: Integration of radiometric andgeometric cues for dynamic scene analysis[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1998,20(9):961-979
[153] C. Stauffer, W.E.L. Grimson. Adaptive background mixture models for real-time tracking[C].IEEE Computer Society International Conference on Computer Vision and PatternRecognition, Fort Collins, CO, USA,1999,2:2246-2252
[154] Y. Tian, R. Feris, H. Liu, et al. Robust detection of abandoned and removed objects incomplex surveillance videos[J]. IEEE Transactions on Systems, Man, and Cybernetics, PartC: Applications and Reviews,2011,41(5):565-576
[155]陈功．鲁棒的智能视频监控方法研究[D].合肥：中国科技大学，2008
[156] Y. Rao, Z. Chen, M. T. Sun, et al. An effective nighttime video enhancement algorithm[C].Visual Communications and Image Processing, Tainan,2011,1-4
[157]潘瑜，孙权森，夏德深．基于PCA分解的图像融合框架[J].计算机工程，2011,37(13):210-212
[158] Y. Zhou. Principal component analysis based image fusion routine with application tostamping split detection[D]. Clemson:Clemson University,2010
[159] Q. Miao，B. Wang. A novel image fusion method using WBCT and PCA[J]. Chinese OpticsLetters,2008,6(3):104-107
[160]毕厚杰．新一代视频压缩编码标准-H.264/AVC [M].北京：人民邮电出版社，2007
[161]王正宁．H.264/AVC视频编码快速算法研究[D].成都：西南交通大学，2007
[162]王嵩．H.264/AVC视频编码标准研究及其编码器优化[D].杭州：浙江大学，2004
[163]丁媛媛．H.264/AVC视频编码关键技术的研究[D].长春：吉林大学，2009
[164]汪大勇．可伸缩视频编码的快速算法研究[D].成都：电子科技大学，2010
[165]董彧焘．H.264/AVC编码优化算法研究[D].上海：上海交通大学，2008
[166] T. Wiegand, G. J. Sullivan, Gisle Bj ntegaard,et al. Overview of the H.264/AVC VideoCoding Standard[J]. IEEE Transactions on Circuits and Systems for VideoTechnology,2003,13(7):560-576
[167] H. Schwarz, D. Marpe, T. Wiegand. Overview of the scalable video coding extension of theH.264/AVC standard[J]. IEEE Transactions on Circuits and Systems for Video Technology,2007,17(9):1103-1120
[168] M. Wien, S. Aachen,O. Heiko. Performance analysis of SVC[J]. IEEE Transactions onCircuits and Systems for Video Technology,2007,17(9):1194-1203
[169] J. R. Ohm. Advances in scalable video coding [J]. Proceedings of the IEEE,2005,93(1):42-56
[170]汪大勇，舒剑．适宜于质量可伸缩帧间编码的快速算法[J].通信学报，2011,32(6):148-156
[171]黄源源，汪大勇，李建平．一种适宜于H.264/AVC的帧间快速编码算法[J].光电子·激光，2013,24(2):344-352
[172]汪大勇，孙世新，杨浩淼，等．适合于空间可伸缩编码的帧内快速算法[J].计算机辅助设计与图形学学报，2010,22(11):1959-1965
[173]王贺，滕国伟，王国中，等．一种低资源消耗的运动估计VLSI实现算法[J].光电子·激光，2010,21(2):265-269
[174]商飞，孔德仁，田地．基于边界约束的样品视频非对称运动估计方法[J].光电子·激光，2012,23(4):768-774
[175]徐静，周兵，黄雪莉，等．基于空域特征的H.264快速多参考帧选择算法[J].通信学报，2010,31(7):40-45
[176] B. Meng, M. Li, Y. Ren. Fast mode selection for H.264/AVC based on MB motioncharacteristics[C]. International Conference on Intelligent Computing and Integrated Systems,Guilin,2010,205-208
[177] P. Chen, H. Chen, M. Shie, et al. Adaptive fast block mode decision algorithm forH.264/AVC[C]. IEEE Conference on Industrial Electronics and Applications, Taichung,2010,2002-2007
[178] H. Wang, S. Kwong, C. Kok. Efficient prediction algorithm of integer DCT coefficients forH.264/AVC optimization[J]. IEEE Transactions on Circuits and Systems for VideoTechnology,2006,16(4):547-552
[179] H. Wang, S. Kwong. Hybrid model to detect zero quantized DCT coefficients in H.264[J].IEEE Transactions on Multimedia,2007,9(4):728-735
[180] H. Wang, S. Kwong, C. Kok. An efficient mode decision algorithm for H.264/AVC encodingoptimization[J]. IEEE Transactions on Multimedia,2007,9(4):882-888
[181]汪大勇，孙世新，杨浩淼．一种基于残差系数的快速编码算法[J].电子与信息学报，2010,32(11):2541-2546
[182] J. Ren, N. Kehtarnavaz, M. Budagavi, et al. Computationally efficient mode selection inH.264/AVC video coding[J]. IEEE Transactions on Consumer Electronics,2008,54(2):877-886
[183]刘鹏宇，何絮，贾克斌．对特定模式进行预判的H.264帧间快速编码算法[J].兵工学报，2011,32(4):439-444
[184]朱辉，李在铭，蔡毅．基于运动窗生成的时空视频分割[J].电子学报，2004,32(3):480-484
[185] M. Kim, J. G. Choi, D. Kim, et al. A VOP generation tool: automatic segmentation ofmoving objects in image sequences based on spatio-temporal information [J]. IEEETransactions on Circuits and Systems for Video Technology.1999,9(8):1216-1226
[186] H. Wang, S. Kwong, C. Kok. Efficient predictive model of zero quantized DCT coefficientsfor fast video encoding [J]. Image and Vision Computing,2007(25):922-933
[187]黄源源，汪大勇，李建平．一种质量可伸缩编码中增强层帧间快速算法[J].电子测量与仪器学报，2011,25(2):153-158
[188] H. Li, Z. G. Li, C. Wen, et al. Fast mode decision algorithm for inter-frame coding in fullyscalable video coding [J]. IEEE Transactions on Circuit and System on Video Technology.2006,16(7):889-895
[189] H. Li, Z. G. Li, C. Wen. Fast mode decision for coarse grain SNR scalable video coding[C].IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse,2006,2:545-548
[190] H. Lin, W. Peng, H. Hang, et al. Layer-adaptive mode decision and motion search forscalable video coding with combined coarse granular scalability (CGS) and temporalscalability[C]. IEEE International Conference on Image Processing, San Antonio, Texas,USA,2007,2:289-292
[191] B. Lee, M. Kim, S. Hahm, et al. A low complexity encoding scheme for coarse grain scalablevideo coding[C]. International Conference on Visual Information Engineering, Xi’an,2008,753-758
[192]王正宁，彭强，诸昌铃．一种基于残差分析的H.264/AVC帧间块划分尺寸选择快速预测算法[J].中国图象图形学报，2005,10(12):1584-1590
[193]莫林剑，卜佳俊，陈纯，等．H.264中基于预测的快速模式决策算法[J].浙江大学学报（工学版），2008,42(4):315-321
[194]汪大勇，舒剑．质量可伸缩编码中增强层的帧内快速算法[J].通信学报，2011,32(12):161-170
[195]杨浩淼，汪大勇．适宜于空间可伸缩帧间编码的快速算法[J].光电子·激光，2012,23(4):776-781
[196] Y. Y. Huang, J. P. Li, F. L. Xu, et al. Intelligent fault diagnosis system in large industrialnetworks[C]. IEEE International Conference on Apperceiving Computing and IntelligenceAnalysis, Chengdu,2008,319-323
[197] H. M. Yang, Y. Y. Huang, J. Li. Early detection of all-zero coefficients in H.264based onDCT coefficients distribution[C]. IEEE International Conference on ApperceivingComputing and Intelligence Analysis, Chengdu,2009,333-336
[198]汪大勇，舒剑．适宜于粗粒度质量可伸缩的帧间编码快速算法[J].光电子·激光，2011,22(7):1079-1084
[199] Y. Y. Huang, H. M. Yang, P. Huang. Action recognition using HOG feature in differentresolution video sequences[C]. International Conference on Computer Distributed Controland Intelligent Enviromental Monitoring(CDCIEM’12), Zhangjiajie,2012,85-88
[200] P. K. Turaga, R. Chellappa, V. S. Subrahmanian, et al. Machine recognition of humanactivities: a survey[J]. IEEE Transactions on Circuits and Systems for Video Technology,2008,18(11):71-86
[201] R. Poppe. A survey on vision-based human action recognition[J]. Image and VisionComputing,2010,28(6):976-990
[202] L. L. Cao, Z. C. Liu, T. S. Huang. Cross-dataset action detection[C]. IEEE Conference onComputer Vision and Pattern Recognition, San Francisco, CA, USA,2010,1998-2005.
[203] N. Dalal, B. Triggs. Histograms of oriented gradients for human detection[C]. IEEEComputer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA,USA,2005,1:886-893
[204]嵇晓强．图像快速去雾与清晰度恢复技术研究[D].长春：中国科学院研究生院长春光学精密机械与物理研究所，2012
[205]程勇．人脸识别中光照不变量提取算法研究[D].南京：南京理工大学，2010
[2]陈兵旗，孙明．实用数字图像处理与分析[M].北京：中国农业大学出版社、清华大学出版社，2007,1-257
[3]雷玉堂．安防视频监控实用技术[M].北京：电子工业出版社，2011,1-72
[4] W. Lin. New technologies for video surveillance systems[D]. Seattle: University ofWashington,2010
[5] R. T. Collins, A. J. Lipton, T. Kanade, et al. A system for video surveillance and monitoring:VSAM final report[R]. Pittsburgh: Carnegie Mellon University,2000
[6] A. Y. Johnson, A. F. Bobick. A multi-view method for gait recognition using static bodyparameters[C]. International Conference on Audio-and Video-Based Biometric PersonAuthentication, Halmstad, Sweden,2001,301-311
[7] Y. Tian, A. W. Senior, A. Hampapur, et al. IBM Smart Surveillance System (S3): eventbased video surveillance system with an open and extensible framework[J]. Machine Visionand Applications,2008,19:315-327
[8]张一．智能视频监控中的目标识别与异常行为建模与分析[D].上海：上海交通大学，2010
[9]宋波涛．智能视频监控系统的设计与实现[D].长春：吉林大学，2009
[10]孔晓东．智能视频监控技术研究[D].上海：上海交通大学，2008
[11]肖广．基于IP的视频监控若干关键技术研究[D].上海：华东师范大学，2008
[12]秦莉娟．基于内容的自动视频监控研究[D].杭州：浙江大学，2006
[13]杨广林．主动视频监控中若干问题的研究[D].秦皇岛：燕山大学，2006
[14]范金刚．智能视频监控系统若干关键技术的研究与实现[D].成都：电子科技大学，2006
[15] M. Gren.视频监控十五年发展回顾和未来趋势[J].中国公共安全（综合版），2011,11:132-135
[16]王素玉，沈兰荪．智能视觉监控技术研究进展[J].中国图像图形学报，2007,9(12):1506-1510
[17]马颂德，张正友．计算机视觉：计算理论与算法基础[M].北京：科学出版社，1998
[18]高文，陈熙霖．计算机视觉-算法与系统原理[M].北京：清华大学出版社，1999
[19] A. K. Jain, A. Ross, S. Prabhakar. An introduction to biometric recognition[J]. IEEETransactions on Circuits and Systems for Video Technology,2004,14(1):4-20
[20] A. Jain, L. Hong, S. Pankanti. Biometrics: promising frontiers for emerging identificationmarket[J]. Communications of The ACM,2000,43(2):90-98
[21]李建平，翟江涛，许川容．小波分析电子签名系统[J].后勤工程学院学报，2001,3:5-8
[22] R. Chellappa,C. L. Wilson, S. Sirohey.Human and machine recognition of faces:a survey[J].Proeeedings of the IEEE,1995,83(5):705-741
[23] W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld. Face recognition: a literature survey[J].ACM Computing Surveys,2003,35(4):399-458
[24] M. Yang, J. K. David, A. Narendra.Detecting faces in images: a survey[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2002,24(1):34-58
[25] J. G. Daugman. Face and gesture recognition:overview[J]. IEEE Transactions on PatternAnalysis and Machine Intelligence,1997,19(7):675-676
[26] P. J. Phillips, P. J. Flynn, T. Scruggs, et al. Overview of the face recognition grandchallenge[C]. IEEE Computer Society Conference on Computer Vision and PatternRecognition, San Diego, CA, USA,2005,947-954
[27] S. Z. Li, A. K. Jain. Handbook of face recognition[M]. Berlin:Springer,2005
[28]周激流，张晔．人脸识别理论研究进展[J].中国计算机辅助设计与图形学学报，1999,12(2):180-184
[29]周杰，卢春雨，张长水，等．人脸自动识别方法综述[J].电子学报，2000,28(4):102-106
[30]刘党辉，沈兰荪，K. Lam.人脸识别研究进展[J].电路与系统学报，2004,9(1):85-94
[31] R. Brunelli, T. Poggio Face recognition: features versus templates[J]. IEEE Transactions onPattern Analysis and Machine Intelligence,1993,15(10):1042-1052
[32]严严，章毓晋．基于视频的人脸识别研究进展[J].计算机学报，2009,32(5):878-886
[33] S. Zhou, V. Krueger, R. Chellappa. Probabilistic recognition of human faces from video[J].Computer Vision and Image Understanding,2003,91(1):214-245
[34]段锦，周春光，刘小华．三维人脸识别研究进展[J].小型微型计算机系统,2004,25(5):886-890
[35] L. Wiskott, J. Fellous, N. Kruger, et al. Face recognition by elastic graph matching[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,1997,19(7):775-779
[36] A. M. Martinez. Recognizing imprecisely localized, partially occluded, and expressionvariant faces from a single sample per class[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2002,25(6):748-763
[37]刘青山，卢汉清，马颂德．综述人脸识别中的子空间方法[J].自动化学报，2003,129(16):900-911
[38] M. A. Turk, A. P. Pentland. Eigenfaces for recognition[J]. Journal of CognitiveNeuroscience,1991,13(1):71-86
[39] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman. Eigenfaces vs.Fisherfaces:recognitionusing class specific linear projection[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1997,19(7):711-720
[40] X. He, S. Yan, Y. Hu, et al. Face recognition using Laplacianfaces[J]. IEEE Transactions onPattern Analysis and Machine Intelligence,2005,27(3):328-340
[41] M. S. Bartlett, J. R. Movellan, T. J. Sejnowski. Face recognition by independent componentanalysis [J]. IEEE Transactions on Neural Networks,2002,13(6):1450-1464
[42] J. Yang, D. Zhang. Two-dimensional PCA: A new approach to appearance-based facerepresentation and recognition [J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2004,26(1):131-137
[43] G. Rajkiran, K. Vijayan. An improved face recognition technique based on modular PCAapproach [J]. Pattern Recognition Letters,2004,4:429-436
[44] S. Chen, Y. Zhu. Subpattern-based principal component analysis[J]. Pattern Recognition,2004,37(1):1081-1083
[45] G. C. Feng, P. C. Uuen, D. Q. Dai. Human face recognition using PCA on waveletsubband[J].SPIE Journal of Electronic Imaging,2000,9(2):226-233
[46] L. Dryden Ian, B. Li, J. Christopher Brignell, et al. Factored principal components analysis,with applications to face recognition[J]. Statistics and Computing,2009,19(3):229-238
[47] S. Lawrence, C. L. Giles, A. C. Tsoi, et al. Face recognition: a convolutional neural-networkapproach[J]. IEEE Transactions on Neural Networks,1997,8(1):98-113
[48]甘俊英，张有为．基于BP神经网络的人脸识别[J].系统工程与电子技术，2003,25(1):113-115
[49] S. Lin, S. Kung, L. Lin, Face recognition/detection by probabilistic decision-based neuralnetwork[J]. IEEE Transactions on Neural Networks,1997,8(1):114-132
[50] J. Lin, J. Ming, D. Crookes. Robust face recognition using posterior union model basedneural networks[J]. IET Computer Vision,2009,3(3):130-142
[51] A. K. Jain, P. W. Duin Robert, J. C. Mao. Statistical pattern recognition: a review [J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2000,22(1):4-37
[52] F. S. Samaria. Face recognition using hidden markov model[D]. Cambridge:University ofCambridge,1994
[53] A. V. Nefian, M. H. Hayes. Hidden markov models for face recognition[C]. IEEEInternational Conference on Acoustics, Speech and Signals Processings, Seattle, Washington,USA,1998,5:2721-2724
[54] G. Guo, S. Z. Li, K. L. Chan. Face recognition by support vector machines[C]. IEEEInternational Conference on Automatic Face and Gesture Recognition, Grenoble,2000,196-201
[55] A. Tefas, C. Kotropoulos, I. Pitas. Ioannis. Using support vector machines to enhance theperformance of elastic graph matching for frontal face authentication[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2001,23(7):735-746
[56] A. M. Bronstein, M. M. Bronstein, R. Kimmel. Expression-invariant representations offaces[J]. IEEE Transactions on Image Processing,2007,16(1):188-197
[57] Y. Adini, Y. Moses, S. Ullman. Face recognition: the problem of compensating for changesin illumination direction[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,1997,19(7):721-732
[58] D. W. Jaeobs, P. N. Belhumeur, R. Basri. Comparing images under variable illumination[C].IEEE Computer Society Conference on Computer Vision and pattern Recognition, SantaBarbara, CA, USA,1998,610-617
[59]庄连生.复杂光照条件下人脸识别关键算法研究[D].合肥：中国科学技术大学，2006
[60]曹雪．小波理论在人脸识别中的应用研究[D].南京：南京理工大学，2011
[61]刘中华．光照变化条件下的人脸特征抽取算法研究[D].南京：南京理工大学，2011
[62] R. C. Gonzalez, R. E. Woods. Digital image processing[M]. Person Prentice Hall, NewJersey,2008
[63] M. Savvides, V. Kumar. Illumination normalization using logarithm transforms for faceauthentication[C]. International Conference on Audio-and Video-Based Biometric PersonAuthentication, Guildford, UK,2003,549-556
[64] S. M. Pizer, E. P. Amburm. Adaptive histogram equalization and its variation[J]. Computervision,graphics,and image processing,1987,39:355-368
[65] J. Kim, L. Kim, S. Hwang. An advanced contrast enhancement using partially overlappedsub-block histogram equalization [J]. IEEE Transactions on Circuits and Systems for VideoTechnology,2001,11(4):475-484
[66] P. Hsieh, P. Tung. Shadow compensation based on facial symmetry and image average forrobust face recognition[J]. Neurocomputing,2010,73(13-15):2708-2717
[67] A. S. Georghiades, P. N. Belhumeur. Illumination cone models for face recognition undervariable lighting Face[C]. IEEE Computer Society Conference on Computer Vision andPattern Recognition, Santa Barbara, CA, USA,1998,52-59
[68] A. S. Georghiades, P. N. Belhumeur, Kriegman, et al.From few to many: Illumination conemodels for face recognition under variable lighting and pose[J]. IEEE Transactions on PatternAnalysis and Machine Intelligence,2001,23(6):643-660
[69] R. Basri, D. W. Jacobs. Lambertian reflectance and linear subspaces[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence,2003,25(2):218-233
[70] V. Blanz, T. Vetter. Face recognition based on fitting a3D morphable model[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2003,25(9):1063-1074
[71] Y. Wang, L. Zhang, Z. C. Liu, et al. Face Relighting from a Single Image under ArbitraryUnknown Lighting Conditions[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence.2009,31(11):1968-1984
[72] S. Z. Li, R. Chu, S. Liao, et al. Illumination invariant face recognition using near-infraredimages[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2007,29(4):627-639
[73] S. G. Kong, J. Heo, F. Boughorbel. Multiscale fusion of visible and thermal IR images forillumination-invariant face recognition[J]. International Journal of Computer Vision,2007,71(2):215-233
[74] W. Di, L. Zhang, D. Zhang, et al. Studies on hyperspectral face recognition in visiblespectrum with feature band selection[J]. IEEE Transactions on Systems, Man andCybernetics, Part A: Systems and Humans,2010,6(40):1354-1361
[75] G. Pan, S. Han, Z. Wu, et al.3D Face recognition using mapped depth images[C]. IEEEConference on Computer Vision and Pattern Recognition-Workshops, San Diego, CA, USA,2005,1-7
[76] H. F. Chen, P. N. Belhumeur, D. W. Jacobs. In search of illumination invariants[C]. IEEEConference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, USA,2000,1:1254-1261
[77] J. Ruiz-del-Solar, J. Quinteros. Illumination compensation and normalization ineigenspace-based face recognition: A comparative study of different pre-processingapproaches[J]. Pattern Recognition Letters,2008,29:1966-1979
[78]李粉兰，段海峰，郝建国，等．人脸识别中光照补偿问题的实验研究[J].工程图学学报，2009,3:113-120
[79] E. H. Land, J. J. McCann. Lightness and retinex theory[J]. Journal of the Optical Society ofAmerica,1971,61(1):1-11
[80] E. H. Land. The retinex theory of color vision[J]. Scientific American,1977,237(6):108-128
[81] D. J. Jobson, Z. Rahman, G. A. Woodell. Properties and performance of a center/surroundRetinex[J]. IEEE Transactions on Image Processing,1997,6(3):451-462
[82] D. J. Jobson, Z. Rahman, G. A. Woodell. A multiscale Retinex for bridging the gap betweencolor images and the human observation of scenes[J]. IEEE Transactions on ImageProcessing,1997,6(7):965-976
[83] Y. K. Park, S. L. Park, J. K. Kim. Retinex method based on adaptive smoothing forillumination invariant face recognition[J]. Signal Processing,2008,88:1929-1945
[84] M. Bertalmío, V. Caselles, E. Provenzi. Issues about retinex theory and contrastEnhancement[J]. International Journal of Computer Vision,2009,83:101-119
[85] A. Shashua, T. R. Raviv. The quotient image:Class-based recognition and synthesis undervarying illumination conditions[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence,2001,23(2):129-139
[86] H. Wang, S. J. Li,Y. Wang. Face recognition under varying lighting conditions using selfquotient image[C]. IEEE International Conference on Automatic Face and GestureRecognition, Seoul, Korea,2004,819-824
[87] T. Chen, W. Yin, X. Zhou, et al. Total variation models for variable lighting facerecognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(9):1519-1524
[88] X. He, J. Tian, L. Wu, et al. lllumination normalization with morphological quotient image[J].软件学报，2007,18(9):2318-2325
[89] T. Zhang, Y. Tang, B. Fang, et al. Face recognition under varying illumination usingGradientfaces[J]. IEEE Transactions on Image Processing,2009,18(11):2599-2606
[90] B. Zhang, H. Zhang, S. Ge. Face recognition by applying wavelet subband representation andkernel associative memory[J]. IEEE Transactions on Neural Networks,2004,15(1):166-177
[91] S. Du, R. K. Ward. Adaptive region-based image enhancement method for robust facerecognition under variable illumination conditions[J]. IEEE Transactions on Circuits andSystems for Video Technology,2010,99:1-12
[92] Y. Z. Goh, B. J. Andrew, K. O. Michael. Wavelet based illumination invariant preprocessingin face recognition[C]. International Congress on Image and Signal Processing, Sanya,2008,3:421-425
[93]龚卫国，杨利平，辜小花，等．基于多级小波分解的人脸图像光照补偿方法[J].光学精密工程，2008,16(8):1459-1464
[94] T. P. Zhang, B. Fang, Y. Yuan, et al. Multiscale facial structure representation for facerecognition under varying illumination[J]. Pattern Recognition,2009,42(2):251-258
[95]曹雪，余立功，杨静宇.基于小波变换和去噪模型的光照不变人脸识别[J].计算机应用，2011,31(8):2126-2129
[96] B. Takacs. Comparing face images using the modified Hausdorff distance[J]. PatternRecognition,1998,31:1873-1881
[97] T. Ojala, M. Pietikainen, M. Topi. Multiresolution gray-scale and rotation invariant textureclassification with local binary patterns[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2002,24(7):971-987
[98] X. Tan, Bill Triggs. Enhanced local texture feature sets for face recognition under difficultlighting conditions[J]. IEEE Transactions on Image Processing,2010,19(6):1635-1650
[99] W. Chen, M. J. Er, S. Wu. Illumination compensation and normalization for robust facerecognition using discrete cosine transform in logarithm domain[J]. IEEE Transactions onSystems, Man, and Cybernetics, Part B: Cybernetics,2006,36(2):458-466
[100] B. Zhang, S. Shan, X. Chen, et al. Histogram of Gabor phase patterns (HGPP): a novel objectrepresentation approach for face recognition [J]. IEEE Transactions on Image Processing,2007,16(1):57-68
[101] Y. Y. Huang, J. P. Li, G. D. Duan, et al. Face recognition using illumination invariantfeatures in contourlet domain[C]. IEEE International Conference on ApperceivingComputing and Intelligence Analysis, Chengdu,2010,294-297
[102] S. G Mallat. A theory for multiresolution signal decomposition: the wavelet representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligences,1989,11(7):674-693
[103] Ingrid Daubechies.小波十讲[M].(李建平，杨万年).北京：国防工业出版社，2004
[104] S. Jaffard, Y. Meyer, R. D. Ryan.科学技术中的小波分析[M].(李建平等).北京：国防工业出版社，2006
[105]李建平.非常规小波变换与军事生物信息安全[M].成都：电子科技大学出版社，2008
[106] M. N. Do, M. Vetterli. The Contourlet transform: an efficient directional multiresolutionimage representation[J]. IEEE Transactions on Image Processing,2005,14(12):2091-2106
[107]戴维，于盛林，孙栓.基于Contourlet变换自适应阈值的图像去噪算法[J].电子学报，2007,35(10):1939-1943
[108]易文娟，郁梅，蒋刚毅.Contourlet：一种有效的方向多尺度变换分析方法[J].计算机应用研究，2006,9:18-22
[109]焦李成，谭山.图像的多尺度几何分析：回顾和展望[J].电子学报，2004,31(B12):1975-1981
[110]李蓓蓓. Contourlet变换及其在图像降噪中的应用[D].长春：吉林大学，2007
[111] D. L. Donoho, I. M. Johnstone. Adapting to unknown smoothness via wavelet shrinkage[J].Journal of the American Statistical Association,1995,90(432):1200-1224
[112] D. L. Donoho. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory,1995,41(3):613-627
[113] S. Chang, B. Yu, M. Vetterli. Adaptive wavelet thresholding for image denoising andcompression[J]. IEEE Transactions on Image Processing,2000,9(9):1532-1546
[114] T. Sim, S. Baker, M. Bsat. The CMU pose, illumination, and expression (PIE) database[C].IEEE International Conference on Automatic Face and Gesture Recognition, Washington,DC, USA,2002,46-51
[115] T. M. Cover, P. E. Hart. Nearest neighbor pattern classification[J]. IEEE Transactions onInformation Theory,1967,13(1):21-27
[116]黄源源，李建平．多尺度变换域内混合投影熵的人脸特征描述[J].计算机工程与应用，2011,47(12):5-6
[117] J. Zhang, C. Chen. Local variance projection log energy entropy features for illuminationrobust face recognition[C]. International Symposium on Biometrics and SecurityTechnologies, Islamabad, Pakistan,2008,1-5
[118] T. Kanade. Picture processing by computer complex and recognition of human faces[D].Kyoto: Kyoto University,1973
[119] G. C. Feng, P. C. Yuen. Variance projection function and its application to eye detection forhuman face recognition[J]. Pattern Recognition Letters,1998,19(9):899-906
[120]耿新，周志华，陈世福．基于混合投影函数的眼睛定位[J].软件学报,2002,14(8):1394-1400
[121] Z. Zhou, X. Geng, Projection functions for eye detection[J]. Pattern Recognition,2004,37:1049-1056
[122]邵平，杨路明，蒋永忠．广义行－列积分图及其在快速投影算法中的应用[J].小型微型计算机系统，2008,29(5):954-958
[123] C. E. Shannon. A mathematical theory for communication[J]. Bell System Technical Journal,1948,27:379-423
[124]张永亮，卢焕章．基于图像局部熵的红外图像分割方法[J].红外技术，2006,28(11):656-660
[125] Y. Y. Huang, J. P. Li, J. Lin, et al. Robust face recognition by combining waveletdecomposition and local hybrid projection entropy[C]. IEEE International Conference onApperceiving Computing and Intelligence Analysis, Chengdu,2009,325-328
[126] R. M. Haralick, Dinstein, K. Shanmugam. Textural features for image classification[J]. IEEETransactions on Systems, Man, and Cybernetics,1973,3(6):610-621
[127] Y. Y. Huang, J. Z. Chen, J. P. Li, et al. Face recognition based on texture features usinghybrid projection function[C]. International Conference on Wavelet Active MediaTechnology and Information Processing, Chengdu,2012,82-85
[128]饶云波.夜间视频增强的关键技术研究[D].成都：电子科技大学，2012
[129] Y. Rao, W. Lin, L. Chen. A global motion estimation based method for nighttime videoenhancement[J]. Optical Engineering,2011,50(5):1-7
[130] Q. Wang, R. K. Ward. Fast image/video contrast enhancement based on weightedthresholded histogram equalization[J]. IEEE Transactions on Consumer Electronics,2007,53(2):757-764
[131] T. Arici, S. Dikbas, Y. Altunbasak. A histogram modification framework and its applicationfor image contrast enhancement[J]. IEEE Transactions on Image Processing,2009,18(9):1921-1935
[132] E. P. Bennett, L. McMillan. Video enhancement using per-pixel virtual exposures[J]. ACMTransactions on Graphics,2005,24(3):845-852
[133]刘贵喜，赵曙光，陈文锦.红外与可见光图像融合的多分辨率方法[J].光电子·激光，2004,15(8):980-984
[134] A. Ilie, R. Raskar, J. Yu. Gradient domain context enhancement for fixed cameras[J].International Journal of Pattern Recognition and Artificial Intelligence,2005,19(4):533-549
[135] Y. Rao, W. Lin, L. Chen. Image-based fusion for video enhancement of nighttimesurveillance[J]. Optic Engineering,2010,49(12):1-3
[136] Y. Cai, K. Huang, T. Tan, et al. Context enhancement of nighttime surveillance by imagefusion[C]. IEEE International Conference on Pattern Recognition, Hong Kong,2006,1:980-983
[137]侯雷，饶云波.一种亮度融合的视频增强方法[J].光电工程，2011,38(8):132-138
[138]邓加琼.复杂背景下的彩色人脸检测算法与实现[D].成都：电子科技大学，2005
[139] G. Mittal, S. Locharam, S. Sasi, et al. An efficient video enhancement method using LA*B*analysis[C]. IEEE International Conference on Video and Signal based Surveillance, Sydney,Australia,2006,66-71
[140]卢官明，郎苏娟.基于YCbCr颜色空间的背景建模及运动目标检测[J].南京邮电大学学报（自然科学版），2009,29(6):17-22
[141] P. Kumar, K. Sengupta, A. Lee. A comparative study of different color spaces for foregroundand shadow detection for traffic monitoring system[C]. IEEE International Conference onIntelligent Transportation systems, Singapore,2002,100-105
[142]钟庆，戴礼荣，宋彦.融合两种运动信息的分级运动检测算法[J].模式识别与人工智能，2002,18(5):552-557
[143]钟庆．智能视频监控中的运动检测算法研究[D].合肥：中国科学技术大学，2004
[144]王德松．基于生物特征信息隐藏与身份认证及其应用研究[D].成都：电子科技大学，2012
[145]孙延奎．小波分析及其应用[M].北京：机械工业出版社，2005,89-117
[146]陈恒鑫.基于多尺度分析的变化光照下人脸识别研究[D].重庆：重庆大学，2010.
[147]谢杰成，张大力，徐文立.小波图象去噪综述[J].中国图象图形学报,2002,7A(3):209-217
[148] A. Yilmaz, O. Javed, M. Shah. Object Tracking: A Survey[J]. ACM Computing Surveys,2006,38(4):1-45
[149] W. Hu, T. Tan, L. Wang, et al. A survey on visual surveillance of object motion andbehaviors[J]. IEEE Transactions on System, Man and Cybernetics, Part C: Applications andReviews,2004,34(3):334-352
[150]史志龙，陈一民，徐杰.视频监控中人的运动检测方法研究与实现[J].计算机工程与应用，2008,44(19):206-209
[151] S. S. Beauchemin, J. L. Barron. The computation of optical flow[J]. ACM computing surveys,1995,27(3):433-466
[152] S. Negahdaripour. Revised definition of optical flow: Integration of radiometric andgeometric cues for dynamic scene analysis[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1998,20(9):961-979
[153] C. Stauffer, W.E.L. Grimson. Adaptive background mixture models for real-time tracking[C].IEEE Computer Society International Conference on Computer Vision and PatternRecognition, Fort Collins, CO, USA,1999,2:2246-2252
[154] Y. Tian, R. Feris, H. Liu, et al. Robust detection of abandoned and removed objects incomplex surveillance videos[J]. IEEE Transactions on Systems, Man, and Cybernetics, PartC: Applications and Reviews,2011,41(5):565-576
[155]陈功．鲁棒的智能视频监控方法研究[D].合肥：中国科技大学，2008
[156] Y. Rao, Z. Chen, M. T. Sun, et al. An effective nighttime video enhancement algorithm[C].Visual Communications and Image Processing, Tainan,2011,1-4
[157]潘瑜，孙权森，夏德深．基于PCA分解的图像融合框架[J].计算机工程，2011,37(13):210-212
[158] Y. Zhou. Principal component analysis based image fusion routine with application tostamping split detection[D]. Clemson:Clemson University,2010
[159] Q. Miao，B. Wang. A novel image fusion method using WBCT and PCA[J]. Chinese OpticsLetters,2008,6(3):104-107
[160]毕厚杰．新一代视频压缩编码标准-H.264/AVC [M].北京：人民邮电出版社，2007
[161]王正宁．H.264/AVC视频编码快速算法研究[D].成都：西南交通大学，2007
[162]王嵩．H.264/AVC视频编码标准研究及其编码器优化[D].杭州：浙江大学，2004
[163]丁媛媛．H.264/AVC视频编码关键技术的研究[D].长春：吉林大学，2009
[164]汪大勇．可伸缩视频编码的快速算法研究[D].成都：电子科技大学，2010
[165]董彧焘．H.264/AVC编码优化算法研究[D].上海：上海交通大学，2008
[166] T. Wiegand, G. J. Sullivan, Gisle Bj ntegaard,et al. Overview of the H.264/AVC VideoCoding Standard[J]. IEEE Transactions on Circuits and Systems for VideoTechnology,2003,13(7):560-576
[167] H. Schwarz, D. Marpe, T. Wiegand. Overview of the scalable video coding extension of theH.264/AVC standard[J]. IEEE Transactions on Circuits and Systems for Video Technology,2007,17(9):1103-1120
[168] M. Wien, S. Aachen,O. Heiko. Performance analysis of SVC[J]. IEEE Transactions onCircuits and Systems for Video Technology,2007,17(9):1194-1203
[169] J. R. Ohm. Advances in scalable video coding [J]. Proceedings of the IEEE,2005,93(1):42-56
[170]汪大勇，舒剑．适宜于质量可伸缩帧间编码的快速算法[J].通信学报，2011,32(6):148-156
[171]黄源源，汪大勇，李建平．一种适宜于H.264/AVC的帧间快速编码算法[J].光电子·激光，2013,24(2):344-352
[172]汪大勇，孙世新，杨浩淼，等．适合于空间可伸缩编码的帧内快速算法[J].计算机辅助设计与图形学学报，2010,22(11):1959-1965
[173]王贺，滕国伟，王国中，等．一种低资源消耗的运动估计VLSI实现算法[J].光电子·激光，2010,21(2):265-269
[174]商飞，孔德仁，田地．基于边界约束的样品视频非对称运动估计方法[J].光电子·激光，2012,23(4):768-774
[175]徐静，周兵，黄雪莉，等．基于空域特征的H.264快速多参考帧选择算法[J].通信学报，2010,31(7):40-45
[176] B. Meng, M. Li, Y. Ren. Fast mode selection for H.264/AVC based on MB motioncharacteristics[C]. International Conference on Intelligent Computing and Integrated Systems,Guilin,2010,205-208
[177] P. Chen, H. Chen, M. Shie, et al. Adaptive fast block mode decision algorithm forH.264/AVC[C]. IEEE Conference on Industrial Electronics and Applications, Taichung,2010,2002-2007
[178] H. Wang, S. Kwong, C. Kok. Efficient prediction algorithm of integer DCT coefficients forH.264/AVC optimization[J]. IEEE Transactions on Circuits and Systems for VideoTechnology,2006,16(4):547-552
[179] H. Wang, S. Kwong. Hybrid model to detect zero quantized DCT coefficients in H.264[J].IEEE Transactions on Multimedia,2007,9(4):728-735
[180] H. Wang, S. Kwong, C. Kok. An efficient mode decision algorithm for H.264/AVC encodingoptimization[J]. IEEE Transactions on Multimedia,2007,9(4):882-888
[181]汪大勇，孙世新，杨浩淼．一种基于残差系数的快速编码算法[J].电子与信息学报，2010,32(11):2541-2546
[182] J. Ren, N. Kehtarnavaz, M. Budagavi, et al. Computationally efficient mode selection inH.264/AVC video coding[J]. IEEE Transactions on Consumer Electronics,2008,54(2):877-886
[183]刘鹏宇，何絮，贾克斌．对特定模式进行预判的H.264帧间快速编码算法[J].兵工学报，2011,32(4):439-444
[184]朱辉，李在铭，蔡毅．基于运动窗生成的时空视频分割[J].电子学报，2004,32(3):480-484
[185] M. Kim, J. G. Choi, D. Kim, et al. A VOP generation tool: automatic segmentation ofmoving objects in image sequences based on spatio-temporal information [J]. IEEETransactions on Circuits and Systems for Video Technology.1999,9(8):1216-1226
[186] H. Wang, S. Kwong, C. Kok. Efficient predictive model of zero quantized DCT coefficientsfor fast video encoding [J]. Image and Vision Computing,2007(25):922-933
[187]黄源源，汪大勇，李建平．一种质量可伸缩编码中增强层帧间快速算法[J].电子测量与仪器学报，2011,25(2):153-158
[188] H. Li, Z. G. Li, C. Wen, et al. Fast mode decision algorithm for inter-frame coding in fullyscalable video coding [J]. IEEE Transactions on Circuit and System on Video Technology.2006,16(7):889-895
[189] H. Li, Z. G. Li, C. Wen. Fast mode decision for coarse grain SNR scalable video coding[C].IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse,2006,2:545-548
[190] H. Lin, W. Peng, H. Hang, et al. Layer-adaptive mode decision and motion search forscalable video coding with combined coarse granular scalability (CGS) and temporalscalability[C]. IEEE International Conference on Image Processing, San Antonio, Texas,USA,2007,2:289-292
[191] B. Lee, M. Kim, S. Hahm, et al. A low complexity encoding scheme for coarse grain scalablevideo coding[C]. International Conference on Visual Information Engineering, Xi’an,2008,753-758
[192]王正宁，彭强，诸昌铃．一种基于残差分析的H.264/AVC帧间块划分尺寸选择快速预测算法[J].中国图象图形学报，2005,10(12):1584-1590
[193]莫林剑，卜佳俊，陈纯，等．H.264中基于预测的快速模式决策算法[J].浙江大学学报（工学版），2008,42(4):315-321
[194]汪大勇，舒剑．质量可伸缩编码中增强层的帧内快速算法[J].通信学报，2011,32(12):161-170
[195]杨浩淼，汪大勇．适宜于空间可伸缩帧间编码的快速算法[J].光电子·激光，2012,23(4):776-781
[196] Y. Y. Huang, J. P. Li, F. L. Xu, et al. Intelligent fault diagnosis system in large industrialnetworks[C]. IEEE International Conference on Apperceiving Computing and IntelligenceAnalysis, Chengdu,2008,319-323
[197] H. M. Yang, Y. Y. Huang, J. Li. Early detection of all-zero coefficients in H.264based onDCT coefficients distribution[C]. IEEE International Conference on ApperceivingComputing and Intelligence Analysis, Chengdu,2009,333-336
[198]汪大勇，舒剑．适宜于粗粒度质量可伸缩的帧间编码快速算法[J].光电子·激光，2011,22(7):1079-1084
[199] Y. Y. Huang, H. M. Yang, P. Huang. Action recognition using HOG feature in differentresolution video sequences[C]. International Conference on Computer Distributed Controland Intelligent Enviromental Monitoring(CDCIEM’12), Zhangjiajie,2012,85-88
[200] P. K. Turaga, R. Chellappa, V. S. Subrahmanian, et al. Machine recognition of humanactivities: a survey[J]. IEEE Transactions on Circuits and Systems for Video Technology,2008,18(11):71-86
[201] R. Poppe. A survey on vision-based human action recognition[J]. Image and VisionComputing,2010,28(6):976-990
[202] L. L. Cao, Z. C. Liu, T. S. Huang. Cross-dataset action detection[C]. IEEE Conference onComputer Vision and Pattern Recognition, San Francisco, CA, USA,2010,1998-2005.
[203] N. Dalal, B. Triggs. Histograms of oriented gradients for human detection[C]. IEEEComputer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA,USA,2005,1:886-893
[204]嵇晓强．图像快速去雾与清晰度恢复技术研究[D].长春：中国科学院研究生院长春光学精密机械与物理研究所，2012
[205]程勇．人脸识别中光照不变量提取算法研究[D].南京：南京理工大学，2010