基于人脸识别和移动检测的视频监控系统
|
摘要
本论文是基于人脸识别与移动检测的视频监控系统而完成的,为了在视频中自动对人脸进行实时的检测、识别,以及对重点监控区域的移动进行检测。本文的主要研究内容为:
利用人脸的肤色特征和人眼像素在人脸区域的特殊性和对称性,完成了一个快速人脸检测算法的设计;利用离散余弦变换(DiscreteCosine Transform,DCT)参数进行提取观察向量,并将观察向量用于隐马尔可夫模型(Hidden Markov Model,HMM)模型,完成人脸训练和识别;针对传统移动检测算法对微小移动不敏感的缺陷,提出了一种改进的背景差的算法;将设计好的人脸识别和移动检测算法应用于软件系统当中,能够让用户根据不同的监控条件采用不同的移动检测方法,并设计了录像、警报、记录查看等辅助性的功能模块,方便用户查看以往出现的人脸图像、经过识别后的识别效果以及检测到移动后记录的画面,使系统的功能更加完善;利用设计好的软件系统平台,通过大量的实验,对系统使用的人脸检测和识别算法的效果进行了测试,并将改进背景差算法与传统算法进行了比较,分析它们的适用条件。
本系统整体设计结构清晰、功能完善;人脸检测与识别算法效果快速准确,完全适合实时的监控环境;改进背景差算法相对传统的相邻帧差算法更加适合于微小移动的检测,完全可以在设计中将两种方法用于不同的检测场景。
This thesis was finished on the basis of a video surveillance system, which includes face recognition and motion detection algorithm and is designed to search and recognize faces appearing in a video sequence and detect motion in selected region.
By using skin-color feature, especial location and pixel features of eyes in face area, an efficient face detection algorithm was designed. After face detection, Discrete Cosine Transform (DCT) was used to extract a set of observation, which is provided to train and recognize faces in the way of Hidden Markov Model (HMM). In order to solve the shortcoming that traditional motion detection algorithm can not be used to detect slow moving objects from an image sequence, an improved method was proposed by rebuilding the background. It was coded in the system and users can decide which motion detection method detects slow or fast target. Several modules were added to make the system perfect, including video camera module, alarm module and records reviewing module, etc. On the platform of the completed system, the effects of face detection and recognition have been displayed. Comparison between traditional algorithm of frame difference and improved one of background difference has been tested. The result showed advantage of the improved one, which can be applied in detecting slow moving objects.
The face detection and recognition algorithm used in this thesis is real-time and precise, can search and recognize faces quickly.The improved method of background difference is obviously better than the traditional one when slow targets are moving. The two methods can be used in different condition.
In the summary, the completed system with detailed structure and function is suitable to be used to real-time video surveillance.
利用人脸的肤色特征和人眼像素在人脸区域的特殊性和对称性,完成了一个快速人脸检测算法的设计;利用离散余弦变换(DiscreteCosine Transform,DCT)参数进行提取观察向量,并将观察向量用于隐马尔可夫模型(Hidden Markov Model,HMM)模型,完成人脸训练和识别;针对传统移动检测算法对微小移动不敏感的缺陷,提出了一种改进的背景差的算法;将设计好的人脸识别和移动检测算法应用于软件系统当中,能够让用户根据不同的监控条件采用不同的移动检测方法,并设计了录像、警报、记录查看等辅助性的功能模块,方便用户查看以往出现的人脸图像、经过识别后的识别效果以及检测到移动后记录的画面,使系统的功能更加完善;利用设计好的软件系统平台,通过大量的实验,对系统使用的人脸检测和识别算法的效果进行了测试,并将改进背景差算法与传统算法进行了比较,分析它们的适用条件。
本系统整体设计结构清晰、功能完善;人脸检测与识别算法效果快速准确,完全适合实时的监控环境;改进背景差算法相对传统的相邻帧差算法更加适合于微小移动的检测,完全可以在设计中将两种方法用于不同的检测场景。
This thesis was finished on the basis of a video surveillance system, which includes face recognition and motion detection algorithm and is designed to search and recognize faces appearing in a video sequence and detect motion in selected region.
By using skin-color feature, especial location and pixel features of eyes in face area, an efficient face detection algorithm was designed. After face detection, Discrete Cosine Transform (DCT) was used to extract a set of observation, which is provided to train and recognize faces in the way of Hidden Markov Model (HMM). In order to solve the shortcoming that traditional motion detection algorithm can not be used to detect slow moving objects from an image sequence, an improved method was proposed by rebuilding the background. It was coded in the system and users can decide which motion detection method detects slow or fast target. Several modules were added to make the system perfect, including video camera module, alarm module and records reviewing module, etc. On the platform of the completed system, the effects of face detection and recognition have been displayed. Comparison between traditional algorithm of frame difference and improved one of background difference has been tested. The result showed advantage of the improved one, which can be applied in detecting slow moving objects.
The face detection and recognition algorithm used in this thesis is real-time and precise, can search and recognize faces quickly.The improved method of background difference is obviously better than the traditional one when slow targets are moving. The two methods can be used in different condition.
In the summary, the completed system with detailed structure and function is suitable to be used to real-time video surveillance.
引文
[1]Ali Reza Mirhosseini,Hong Yan,Kin-Man Lam et al.Human Face Image Recognition:An Evidence Aggregation Approach[J].Computer Vision and Image Understanding,1998.8,71(2):213-230
[2]闫娟,程武山,孙鑫.人脸识别的技术研究与发展概况[J].视频技术应用与工程,2006,(4):81
[3]PHILLIPS P J,MOON H.The FERET Evaluation Methodology for Recognition Algorithms[J].IEEE Tmnaactiom on Pattern Analysis and Machine Intelligence,2000,22(10):1090-1104
[4]COLLINS R T,LIPTON A J,KANADE T.Introduction to the special section on video surveillance[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):745-746
[5]杜峥,张桂林,洪靖云.视频监控系统中的一种运动检测算法[J].机械与电于,2003,(6):46-47
[6]李刚,邱尚斌等.基于背景差法和帧间差法的运动目标检测方法[J].仪器仪表学报,2006,27(8):961-963
[7]李然,高会生,王成儒.基于视频监视系统的运动目标检测技术的实现[J].电力系统通信,2003,(7):1-2
[8]代科学,李国辉,涂丹,袁见.监控视频运动目标检测减背景技术的研究现状和展望[J].中国图像图形学报,2006,11(7):922-924
[9]刘丽华.人脸识别方法综述[J].云南师范大学学报,2005,25(6):13
[10]Batur A U,M H I Hayes.Linear Subspaces for Illumination Robust Face Recognition[A].Proceedings of the2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C].2001,2(2):296 -301
I11]史东承,杨冬,姜文翰,周小飞,一种基于隐马尔可夫模型的人像识别算法[J].长春工业大学学报,2002,23:44-45
[12]Chellappa R,Wilson C,Sirohey S.Human and machine recognition of faces:a survey[J].Proceedings of IEEE,1995,83(5):705-740
[13]Shakunaga T,Shigenari K.Decomposed Eigenface for Face Recognition under Various Lighting Conditions[A].Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C].2001,01(1):864-871
[14]Takeyuki Takano,Katsumi Ushita.Intruder detection system by image processing[J].IEEE Annual International Carnahan Conference on Security Technology Proceedings,1994,(10):12-14
[15]艾英山,张德贤.人脸识别方法的综述与展望[J].计算机与数字工程,2005,33(10):25-26
[16]PHILLIPS P J,GROTHER P J.Face Recognition Vendor Test 2002:Evaluation Report,Technical Repont[R].Gaithersbur,2003.
[17]Osuna E,Freund R.Training support vector machines:An Application to Face Detection [A].Processing of Computer Vision and Pattern Recognition[C].San Juan,Puerto Rico,IEEE Computer Soc,1997:130-136.
[18]Guang-zheng Yang,Thomas S Huang.Human face Detection In a Complex Background[J].Pattern Recognition.1994,27(1):53-63
[19]Gloria Chow,Xiao- bo Li.Towards A System for Automatic Facial Feature Detection[J].Pattern Recognition,1993,26(12):1739-1755
[20]Shao-hua Zhou,Krueger V,Chellappa R.Face recognition from video:CONDENSATION approach[A].Proceedings.Fifth IEEE International Conference on Automatic Face and Gesture Recognition[C].2002,212-217
[21]Moses Y,Ullman S.Limitation of non - model -based recognition schemes[A].Proc.ECCV-92,Sandini G.ed.[C].Snringer-Verlag.1992:820-828
[22]陈熙霖,山世光,高文.多姿态人脸识别[J].中国图象图形学报,1999,4(10):818-824
[23]Beymer D.Pose - invariant face recognition using real and virtual views[D].EECS,MIT,1995,8
[24]马勇,丁晓青.基于层次型支持向量机的人脸检测[J].清华大学学报,2003,43(1):35-38
[25]刘冰,朱仁龙,孙殿平.序列图像运动目标检测的一种快速算法[J].上海师范大学学报(自然科学版),2006.10,35(5):42
[26]Li Y,Gong S,Liddell H.Video - based online face recognition using identity surfaces[A].Proc.The Second International Workshop on Recognition,Analysis and Tracking of Faces and Gestures in Real - time Systems[C].Vancouver,Canada,2001,40 - 46.
[27]Chellappa R,Wilson CL,Sirohey S.Human and machine recognition of faces:A survey[J].Proceedings of the IEEE,1995,83(5):705-740
[28]Zhou J,Lu CY,Zhang CS.A survey of automatic face recognition[J].ACTA Electronica Sinica,2000,28(4):102-106
[29]周杰,卢春雨,张长水等.人脸自动识别方法综述[J].电子学,2000,28(4):102-106
[30]梁路宏,艾海舟,徐光,张钹.人脸检测研究综述[J].计算机学报,2002,25(5):449-458
[31]Lee D S,Hull J,Erol B.A Bayesian framework for gaussian mixturebackground modeling[A].Proceedings of IEEE International Conference on Image Processing[C],Barcelona,Spain,2003,973-976.
[32]Wada T,Matsuyama T.Appearance sphere:Background model for pan-tilt-zoom camera[A].Proceedings of International Conference on Pattern Recognition[C],Vienna,Austria.1996.718-722.
[33]艾海舟,吕风军.面向视觉监视的变化检测与分割[J].计算机工程与应用,2000,37(5):75-77
[34]林洪文,涂丹,李国辉.基于统计背景模型的运动目标检测方法[J].计算机工程,2003,29(16):97-99
[35]Xiao-bo Li,Nicholas Roeder.Face Recognition Extraction From FrontView Images[J].Pattern Recognition,1995,28(8):1168-1179
[36]Jones M J,Rehg J M.Statistical color models with application to skin detection[J].In Proc.IEEE Conf.on Computer Vision and Pattern Recognition,Fort Collins,Colorado,1999,274-280
[37]Wang J G;Tan T N.A new face detection method based on shape information[J].Pattern Recognition Letters,2000,21(6-7):463-471
[38]Terrillon J C,Shirazi M N,Fukamachi H,etal.Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images[J].In:Proc.Conf.on Automatic Face and Gesture Recognition,Grenoble,France,2000,54-61
[39]Meng L,Nguyen T Q,Castanon D A.An image-based bayesian framework for face detection[J].In:Proc.IEEE Conf.on Computer Vision and Pattern Recognition,Hilton Head Island,South Carolina,USA,2000,302-307
[40]梁路宏,艾海舟,徐光祜,张钹.基于多关联模板匹配与人工神经网确认的人脸检测[J].电子学报,2001,29(6):744-747
[41]贺瑞宏.简单背景下的人脸检测[D].国防科学技术大学电子科学与工程学院,2004
[42]Yang MH,Kriegman D,Ahuja N.Detecting faces in images:A survey[J].IEEE Trans Pattern Analysis and Machine Intelligence,2002,24(1):34-58
[43]梁路宏,艾海舟,徐光佑等.人脸检测研究综述[J].计算机学报,2002,5(25):449-458
[44]章毓晋.图像工程(上、下册)[M].北京:清华大学出版社,2003
[45]张宏林.Visual C++数字图像模式识别技术及工程实践[M].北京:人民邮电出版社.2003.363-370
[46]Kin-Man Lam,Hong Yan.Location and Extraction the Eye in Human Face Image[J].Pattern Recognition 1996,29(5):771-779
[47]王洪群,彭嘉雄,于秋则.采用动态HMM概率分布模型的人眼精确定位[J].中国图象图形学报,2006.1,11(1):26-27
[48]林维训,潘刚,吴朝晖等.脸部特征定位方法[J].中国图象图形学报,2003,8(8):849-859
[49]艾海舟,粱路宏.基于肤色和模板的人脸检测[J].软件学报,2001,12(12):1784-1792
[50]陶亮,庄镇泉.复杂背景下人眼自动定位[J].计算机辅助设计与图形学学报,2003,15(1):38-42
[51]魏冬冬,谌海新,聂铁铸.人脸特征提取与识别技术研究[J].计算机与现代化.2007,(3):70-71
[52]Chun-hung Lin,Jia- ling Wu.Senior Member,IEEE:Automatic Facial Feature Extraction by Genetic Algorithms[J].IEEE Transactions on Image Processing,1999,8(6):834-841
[53]Shi-hong Jeng,Hong Yuan Mark Liao,Chin Chuan Han et al.Facial Feature Detection Using Geometrical Face Model:An Efficient Approach[J].Pattern Recognition,1998,31(3):273-282
[54]Karin Sobottka,Ioannis Pitas.A Novel Method for Automatic Face Segrnentation,Facial Feature ExtractionandTracking[J].Signal Processing:Image Communication,1998,12:263-281
[55]卢健,干晖.SVD和DCT提取特征向量的方法在人脸识别中的比较[J].武汉大学学报(信息科学版).2005.2,30(2):18-19
[56]谈吕彬,李一民.基于E-HMM的人脸识别[J].云南民族_大学学报(自然科学版).2006.10,15(4):287
[57]刘春丽,陈树中,韩安奇.隐马尔科夫模型及其在面像识别中的应用[JJ.计算机应用与软件,2004.4,21(4):68
[58]史东承,杨冬,姜文翰,周小飞.一种基于隐马尔可夫模型的人像识别算法[J].长春工业大学学报,2002.8,23:44-45
[59]陈茂林.自组织隐马尔可夫模型的人脸检测研究[J].计算机学报,2002,(11):1165-1169.
[60]刘春丽,陈树中,韩安奇.隐马尔科夫模型及其在面像识别中的应用[J].2004.4,21(4):68-69
[61]徐毅琼,李弼程,王波.基于隐马尔可夫模型的自动人脸识别方法[J].计算机应用,2004.12.24:225
[62]李泉富,董慧颖,赵全邦.基于背景差与帧间方块编码差值法的运动目标检测[J].沈阳理 工大学学报,2006.8,25(4):33
[63]胡亮,王敏珍,蒋春晓,张仲明,许斌.机内进程间通信的性能测试和评价[J].吉林大学学报(信息科学版),2003.11,21(4):420
[64]孙叶萌.单机内进程问三种通信方法效率的比较[J].长春理工大学学报,2003.9,26(3):83
[2]闫娟,程武山,孙鑫.人脸识别的技术研究与发展概况[J].视频技术应用与工程,2006,(4):81
[3]PHILLIPS P J,MOON H.The FERET Evaluation Methodology for Recognition Algorithms[J].IEEE Tmnaactiom on Pattern Analysis and Machine Intelligence,2000,22(10):1090-1104
[4]COLLINS R T,LIPTON A J,KANADE T.Introduction to the special section on video surveillance[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):745-746
[5]杜峥,张桂林,洪靖云.视频监控系统中的一种运动检测算法[J].机械与电于,2003,(6):46-47
[6]李刚,邱尚斌等.基于背景差法和帧间差法的运动目标检测方法[J].仪器仪表学报,2006,27(8):961-963
[7]李然,高会生,王成儒.基于视频监视系统的运动目标检测技术的实现[J].电力系统通信,2003,(7):1-2
[8]代科学,李国辉,涂丹,袁见.监控视频运动目标检测减背景技术的研究现状和展望[J].中国图像图形学报,2006,11(7):922-924
[9]刘丽华.人脸识别方法综述[J].云南师范大学学报,2005,25(6):13
[10]Batur A U,M H I Hayes.Linear Subspaces for Illumination Robust Face Recognition[A].Proceedings of the2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C].2001,2(2):296 -301
I11]史东承,杨冬,姜文翰,周小飞,一种基于隐马尔可夫模型的人像识别算法[J].长春工业大学学报,2002,23:44-45
[12]Chellappa R,Wilson C,Sirohey S.Human and machine recognition of faces:a survey[J].Proceedings of IEEE,1995,83(5):705-740
[13]Shakunaga T,Shigenari K.Decomposed Eigenface for Face Recognition under Various Lighting Conditions[A].Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C].2001,01(1):864-871
[14]Takeyuki Takano,Katsumi Ushita.Intruder detection system by image processing[J].IEEE Annual International Carnahan Conference on Security Technology Proceedings,1994,(10):12-14
[15]艾英山,张德贤.人脸识别方法的综述与展望[J].计算机与数字工程,2005,33(10):25-26
[16]PHILLIPS P J,GROTHER P J.Face Recognition Vendor Test 2002:Evaluation Report,Technical Repont[R].Gaithersbur,2003.
[17]Osuna E,Freund R.Training support vector machines:An Application to Face Detection [A].Processing of Computer Vision and Pattern Recognition[C].San Juan,Puerto Rico,IEEE Computer Soc,1997:130-136.
[18]Guang-zheng Yang,Thomas S Huang.Human face Detection In a Complex Background[J].Pattern Recognition.1994,27(1):53-63
[19]Gloria Chow,Xiao- bo Li.Towards A System for Automatic Facial Feature Detection[J].Pattern Recognition,1993,26(12):1739-1755
[20]Shao-hua Zhou,Krueger V,Chellappa R.Face recognition from video:CONDENSATION approach[A].Proceedings.Fifth IEEE International Conference on Automatic Face and Gesture Recognition[C].2002,212-217
[21]Moses Y,Ullman S.Limitation of non - model -based recognition schemes[A].Proc.ECCV-92,Sandini G.ed.[C].Snringer-Verlag.1992:820-828
[22]陈熙霖,山世光,高文.多姿态人脸识别[J].中国图象图形学报,1999,4(10):818-824
[23]Beymer D.Pose - invariant face recognition using real and virtual views[D].EECS,MIT,1995,8
[24]马勇,丁晓青.基于层次型支持向量机的人脸检测[J].清华大学学报,2003,43(1):35-38
[25]刘冰,朱仁龙,孙殿平.序列图像运动目标检测的一种快速算法[J].上海师范大学学报(自然科学版),2006.10,35(5):42
[26]Li Y,Gong S,Liddell H.Video - based online face recognition using identity surfaces[A].Proc.The Second International Workshop on Recognition,Analysis and Tracking of Faces and Gestures in Real - time Systems[C].Vancouver,Canada,2001,40 - 46.
[27]Chellappa R,Wilson CL,Sirohey S.Human and machine recognition of faces:A survey[J].Proceedings of the IEEE,1995,83(5):705-740
[28]Zhou J,Lu CY,Zhang CS.A survey of automatic face recognition[J].ACTA Electronica Sinica,2000,28(4):102-106
[29]周杰,卢春雨,张长水等.人脸自动识别方法综述[J].电子学,2000,28(4):102-106
[30]梁路宏,艾海舟,徐光,张钹.人脸检测研究综述[J].计算机学报,2002,25(5):449-458
[31]Lee D S,Hull J,Erol B.A Bayesian framework for gaussian mixturebackground modeling[A].Proceedings of IEEE International Conference on Image Processing[C],Barcelona,Spain,2003,973-976.
[32]Wada T,Matsuyama T.Appearance sphere:Background model for pan-tilt-zoom camera[A].Proceedings of International Conference on Pattern Recognition[C],Vienna,Austria.1996.718-722.
[33]艾海舟,吕风军.面向视觉监视的变化检测与分割[J].计算机工程与应用,2000,37(5):75-77
[34]林洪文,涂丹,李国辉.基于统计背景模型的运动目标检测方法[J].计算机工程,2003,29(16):97-99
[35]Xiao-bo Li,Nicholas Roeder.Face Recognition Extraction From FrontView Images[J].Pattern Recognition,1995,28(8):1168-1179
[36]Jones M J,Rehg J M.Statistical color models with application to skin detection[J].In Proc.IEEE Conf.on Computer Vision and Pattern Recognition,Fort Collins,Colorado,1999,274-280
[37]Wang J G;Tan T N.A new face detection method based on shape information[J].Pattern Recognition Letters,2000,21(6-7):463-471
[38]Terrillon J C,Shirazi M N,Fukamachi H,etal.Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images[J].In:Proc.Conf.on Automatic Face and Gesture Recognition,Grenoble,France,2000,54-61
[39]Meng L,Nguyen T Q,Castanon D A.An image-based bayesian framework for face detection[J].In:Proc.IEEE Conf.on Computer Vision and Pattern Recognition,Hilton Head Island,South Carolina,USA,2000,302-307
[40]梁路宏,艾海舟,徐光祜,张钹.基于多关联模板匹配与人工神经网确认的人脸检测[J].电子学报,2001,29(6):744-747
[41]贺瑞宏.简单背景下的人脸检测[D].国防科学技术大学电子科学与工程学院,2004
[42]Yang MH,Kriegman D,Ahuja N.Detecting faces in images:A survey[J].IEEE Trans Pattern Analysis and Machine Intelligence,2002,24(1):34-58
[43]梁路宏,艾海舟,徐光佑等.人脸检测研究综述[J].计算机学报,2002,5(25):449-458
[44]章毓晋.图像工程(上、下册)[M].北京:清华大学出版社,2003
[45]张宏林.Visual C++数字图像模式识别技术及工程实践[M].北京:人民邮电出版社.2003.363-370
[46]Kin-Man Lam,Hong Yan.Location and Extraction the Eye in Human Face Image[J].Pattern Recognition 1996,29(5):771-779
[47]王洪群,彭嘉雄,于秋则.采用动态HMM概率分布模型的人眼精确定位[J].中国图象图形学报,2006.1,11(1):26-27
[48]林维训,潘刚,吴朝晖等.脸部特征定位方法[J].中国图象图形学报,2003,8(8):849-859
[49]艾海舟,粱路宏.基于肤色和模板的人脸检测[J].软件学报,2001,12(12):1784-1792
[50]陶亮,庄镇泉.复杂背景下人眼自动定位[J].计算机辅助设计与图形学学报,2003,15(1):38-42
[51]魏冬冬,谌海新,聂铁铸.人脸特征提取与识别技术研究[J].计算机与现代化.2007,(3):70-71
[52]Chun-hung Lin,Jia- ling Wu.Senior Member,IEEE:Automatic Facial Feature Extraction by Genetic Algorithms[J].IEEE Transactions on Image Processing,1999,8(6):834-841
[53]Shi-hong Jeng,Hong Yuan Mark Liao,Chin Chuan Han et al.Facial Feature Detection Using Geometrical Face Model:An Efficient Approach[J].Pattern Recognition,1998,31(3):273-282
[54]Karin Sobottka,Ioannis Pitas.A Novel Method for Automatic Face Segrnentation,Facial Feature ExtractionandTracking[J].Signal Processing:Image Communication,1998,12:263-281
[55]卢健,干晖.SVD和DCT提取特征向量的方法在人脸识别中的比较[J].武汉大学学报(信息科学版).2005.2,30(2):18-19
[56]谈吕彬,李一民.基于E-HMM的人脸识别[J].云南民族_大学学报(自然科学版).2006.10,15(4):287
[57]刘春丽,陈树中,韩安奇.隐马尔科夫模型及其在面像识别中的应用[JJ.计算机应用与软件,2004.4,21(4):68
[58]史东承,杨冬,姜文翰,周小飞.一种基于隐马尔可夫模型的人像识别算法[J].长春工业大学学报,2002.8,23:44-45
[59]陈茂林.自组织隐马尔可夫模型的人脸检测研究[J].计算机学报,2002,(11):1165-1169.
[60]刘春丽,陈树中,韩安奇.隐马尔科夫模型及其在面像识别中的应用[J].2004.4,21(4):68-69
[61]徐毅琼,李弼程,王波.基于隐马尔可夫模型的自动人脸识别方法[J].计算机应用,2004.12.24:225
[62]李泉富,董慧颖,赵全邦.基于背景差与帧间方块编码差值法的运动目标检测[J].沈阳理 工大学学报,2006.8,25(4):33
[63]胡亮,王敏珍,蒋春晓,张仲明,许斌.机内进程间通信的性能测试和评价[J].吉林大学学报(信息科学版),2003.11,21(4):420
[64]孙叶萌.单机内进程问三种通信方法效率的比较[J].长春理工大学学报,2003.9,26(3):83