多功能定时监视系统的研究与实现
|
摘要
随着家庭和小型办公系统的财产和电气设备不断增加,安全防范和火灾监测成为现代家庭和小型办公系统必须考虑的一个重要问题。大型的楼宇都有楼宇自动化装置,而小型的办公系统或者家庭基本没有防控系统。为了满足小型办公系统和家庭对安全和防火的需要,本文以便携性好、成本低廉、安装方便为设计目标,研究并实现了基于单片机的多功能定时监视系统。
文章从硬件和软件两方面讨论,提出了硬件系统的基本架构和软件系统的组成模块。硬件由MCU、图像采集、人机交互界面、外部存储、年历/时间读写和通信接口六部分组成。软件由特征提取模块、工作控制模块、操作界面、时钟模块和通信模块五部分组成。
系统的核心模块为特征提取模块。模块实现两种功能——运动物体监测和火灾监测。通过对运动物体的监测,可以发现被监测区域是否有可疑的异常现象。针对MCU内存空间小和处理速度慢的缺点,提出了基于Sobel边缘提取和基于帧间差分法两种算法,实现运动物体监测;对于火灾监测,采用基于帧间差分法实现。基于Sobel边缘提取算法先对区域内的每帧图像进行物体的轮廓提取和筛选,用八邻域搜索法去噪,然后通过对比连续两帧图像中物体的轮廓的变化,判定被监测区域是否有物体运动。基于帧间差分法的算法先求得区域中连续两帧输入图像的差分图像,并对差分图像进行二值化,再用八邻域快速扫描的方法判定图像中是否存在物体的运动轨迹,进而判断物体的运动情况。基于帧间差分法的火灾监测算法,对一系列输入图像中的每一帧图像和前后五帧图像求取平均强度值,若平均强度值变化较小,表示被监测系统没有火灾发生,算法结束;否则对连续帧图像进行求差运算和中值滤波去噪,利用瞬时时间值求得强度变化门限;最后统计差分图像的强度变化分布,判定被监测区域是否有火灾现象发生。
为了便于用户对监视系统的功能进行设置和查询,系统需要对大量的定时提醒信息和异常情况进行存储管理。定时提醒信息通过系统的万年历时钟模块管理。在时钟管理模块中,遵循尽可能减少数据移动和节省MCU处理时间的原则,设计了基于双地址映射机制的存储、搜索、删除、新增和编辑方法的定时提醒信息。双地址映射机制对定时提醒信息的操作无须大量移动或清空数据,节省了用于移动或清空数据的处理时间,在单片机系统应用方面具有一定的优势。
本文设计的系统和提出的监测算法进行了功能测试。测试结果表明系统不仅能实现运动物体和火灾的监测,而且算法的时间、空间复杂度低,具有一定的实际应用意义。
With increasingly expanding electrical equipment and property at home and small office systems, safety precautions and fire monitoring has been considered to be an important issue for a modern home and small office systems. Large buildings have building automation devices, while small office systems or family basically have no prevention and control system. In order to meet the requirements for small office systems and home security and fire protection, this paper study and realize the multi-purpose microcomputer-based timing surveillance system. The proposed system has many virtues like good portability, low cost and easy installation.
The article is discussed from both the hardware and software aspects, and put forward the basic structure of hardware and principle components of software systems. The hardware system is composed by 6 parts: MCU, image acquisition, human-computer interface, external storage and calendar / time reading, writing and communication interfaces. And software has five parts: feature extraction module, job control module, operator interface, the clock module and communication module.
The system's core module is feature extraction module, which can achieve two functions- moving object monitoring and fire monitoring. Through monitoring the moving objects, suspicious anomalies can be found. To compensate the small MCU memory space and its slow processing speed, the paper proposes two kinds of algorithms to achieve a moving object monitoring. One is Sobel edge detection, and the other is inter-frame difference method. As to fire monitoring, the inter-frame difference method can achieve this goal. Sobel based edge detection algorithm first conducts object extraction and screening of each frame on the region, de-noises with 8 neighborhood search method, and then compares two consecutive images of objects in the outline to find changes in the region and to determine whether there is object motion. The inter-frame difference method based algorithm obtains the difference image of the two consecutive input image, conducts the binarization, and then determine whether there are objects in the image trajectory by eight neighborhood fast-scanning method, thus to determine the movement of objects. The average intensity value of the 5 consecutive images can be calculated by a inter-frame difference method based fire monitoring algorithm. If changes in the average intensity of the value is relatively small, the monitored system is considered no fire and the algorithm ends; otherwise the system conduct subtraction and median filter denoising for continuous frames images. Threshold intensity is obtained using the instantaneous time value changes, finally difference image intensity change distribution is counted to determine whether there is a fire phenomenon.
In order to facilitate the monitoring function of the system for users to set and query, the system needs to store and manage a lot of timing information and exceptions. Reminding time information is managed through the system calendar clock module. The management module follows the principle of minimizing data movement and processing time of MCU and designs dual-address based mapping mechanism to store, search, delete, add and edit information of timing alerts. Dual-address mapping mechanism does not require a large number of moving or empty data in reminding operation and thus save the time for mobile or empty data processing, rendering certain advantages for applications in single-chip system.
This monitoring system and the proposed algorithm have passed through functional tests. Test results show that the system not only enables monitoring of moving objects and fire, but also is low in algorithm's time and space complexity, showing a certain significance in practical applications.
文章从硬件和软件两方面讨论,提出了硬件系统的基本架构和软件系统的组成模块。硬件由MCU、图像采集、人机交互界面、外部存储、年历/时间读写和通信接口六部分组成。软件由特征提取模块、工作控制模块、操作界面、时钟模块和通信模块五部分组成。
系统的核心模块为特征提取模块。模块实现两种功能——运动物体监测和火灾监测。通过对运动物体的监测,可以发现被监测区域是否有可疑的异常现象。针对MCU内存空间小和处理速度慢的缺点,提出了基于Sobel边缘提取和基于帧间差分法两种算法,实现运动物体监测;对于火灾监测,采用基于帧间差分法实现。基于Sobel边缘提取算法先对区域内的每帧图像进行物体的轮廓提取和筛选,用八邻域搜索法去噪,然后通过对比连续两帧图像中物体的轮廓的变化,判定被监测区域是否有物体运动。基于帧间差分法的算法先求得区域中连续两帧输入图像的差分图像,并对差分图像进行二值化,再用八邻域快速扫描的方法判定图像中是否存在物体的运动轨迹,进而判断物体的运动情况。基于帧间差分法的火灾监测算法,对一系列输入图像中的每一帧图像和前后五帧图像求取平均强度值,若平均强度值变化较小,表示被监测系统没有火灾发生,算法结束;否则对连续帧图像进行求差运算和中值滤波去噪,利用瞬时时间值求得强度变化门限;最后统计差分图像的强度变化分布,判定被监测区域是否有火灾现象发生。
为了便于用户对监视系统的功能进行设置和查询,系统需要对大量的定时提醒信息和异常情况进行存储管理。定时提醒信息通过系统的万年历时钟模块管理。在时钟管理模块中,遵循尽可能减少数据移动和节省MCU处理时间的原则,设计了基于双地址映射机制的存储、搜索、删除、新增和编辑方法的定时提醒信息。双地址映射机制对定时提醒信息的操作无须大量移动或清空数据,节省了用于移动或清空数据的处理时间,在单片机系统应用方面具有一定的优势。
本文设计的系统和提出的监测算法进行了功能测试。测试结果表明系统不仅能实现运动物体和火灾的监测,而且算法的时间、空间复杂度低,具有一定的实际应用意义。
With increasingly expanding electrical equipment and property at home and small office systems, safety precautions and fire monitoring has been considered to be an important issue for a modern home and small office systems. Large buildings have building automation devices, while small office systems or family basically have no prevention and control system. In order to meet the requirements for small office systems and home security and fire protection, this paper study and realize the multi-purpose microcomputer-based timing surveillance system. The proposed system has many virtues like good portability, low cost and easy installation.
The article is discussed from both the hardware and software aspects, and put forward the basic structure of hardware and principle components of software systems. The hardware system is composed by 6 parts: MCU, image acquisition, human-computer interface, external storage and calendar / time reading, writing and communication interfaces. And software has five parts: feature extraction module, job control module, operator interface, the clock module and communication module.
The system's core module is feature extraction module, which can achieve two functions- moving object monitoring and fire monitoring. Through monitoring the moving objects, suspicious anomalies can be found. To compensate the small MCU memory space and its slow processing speed, the paper proposes two kinds of algorithms to achieve a moving object monitoring. One is Sobel edge detection, and the other is inter-frame difference method. As to fire monitoring, the inter-frame difference method can achieve this goal. Sobel based edge detection algorithm first conducts object extraction and screening of each frame on the region, de-noises with 8 neighborhood search method, and then compares two consecutive images of objects in the outline to find changes in the region and to determine whether there is object motion. The inter-frame difference method based algorithm obtains the difference image of the two consecutive input image, conducts the binarization, and then determine whether there are objects in the image trajectory by eight neighborhood fast-scanning method, thus to determine the movement of objects. The average intensity value of the 5 consecutive images can be calculated by a inter-frame difference method based fire monitoring algorithm. If changes in the average intensity of the value is relatively small, the monitored system is considered no fire and the algorithm ends; otherwise the system conduct subtraction and median filter denoising for continuous frames images. Threshold intensity is obtained using the instantaneous time value changes, finally difference image intensity change distribution is counted to determine whether there is a fire phenomenon.
In order to facilitate the monitoring function of the system for users to set and query, the system needs to store and manage a lot of timing information and exceptions. Reminding time information is managed through the system calendar clock module. The management module follows the principle of minimizing data movement and processing time of MCU and designs dual-address based mapping mechanism to store, search, delete, add and edit information of timing alerts. Dual-address mapping mechanism does not require a large number of moving or empty data in reminding operation and thus save the time for mobile or empty data processing, rendering certain advantages for applications in single-chip system.
This monitoring system and the proposed algorithm have passed through functional tests. Test results show that the system not only enables monitoring of moving objects and fire, but also is low in algorithm's time and space complexity, showing a certain significance in practical applications.
引文
[1]何东晓.智能视频监控中的运动目标检测方法研究.硕士学位论文.青岛:中国海洋大学,2008,1-5.
[2]Nick Kanopoulos,Nagesh Vasanthavada,and Robert L.Baker.Design of an Image Edge Detection Filter Using the Sobel Operator.IEEE Journal of Solid-State Circuits,1988,23(2):358-367.
[3]初秀琴,曾祥永,李玉山.一种新型的实时图像处理机结构及Sobel电路设计.仪器仪表学报,2003,24(5):506-508.
[4]Yau-Hwang Kuo,Chang-Shing Lee,and Chao-Chin Liu.A New Fuzzy Edge Detection Method for Image Enhancement.In:Fuzzy Systems,1997,Proceedings of the Sixth IEEE International Conference,1997,2(1):1069-1074.
[5]Sathyadev V.Uppala,and John D.Sahr.On the Design of Quadratic Filters with Application to Image Processing.IEEE Transactions on Image Processing,1997,6(4):618-614.
[6]陆宗骐,梁诚.用Sobel算子细化边缘.中国图像图形学报,2000,5(6):516-520.
[7]Natalia Kazakova,Martin Margala,and Nelson G.Durdle.Sobel Edge Detection Processor for a Real-time Volume Rendering System.Circuits and Systems,2004.ISCAS '04.Proceedings of the 2004 International Symposium on,2004,2(23):Ⅱ913-Ⅱ916.
[8]郑敏,王有熙,税冬东.基于Sobel算子含噪低对比度图像的边缘检测方法.石河子大学学报(自然科学版),2008,26(1):117-119.
[9]王植,贺赛先.一种基于Canny理论的自适应边缘检测方法.中国图像图形学报,2004,9(8):957-962.
[10]Canny J.F.Finding Edges and Lines in Images.MIT AI-TR-720,1983.
[11]Bergholm F.Edge Focusing.IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(9):726-741.
[12]Wilkinson M.H.F.Optimizing Edge Detectors for Robust Automatic Threshold Selection:Coping with Edge Curvature and Noise.Graphical Models and Image Processing,1998,60(4):385-401.
[13]Chanda B.,Kundu M.K.,and Vanipadmaja Y.A Multi-Scale Morphologic Edge Detector.Pattern Recognition,1998,31(10):1469-1478.
[14]Pavlidis T.,and Liow Y.T.Integrating Region Growing and Edge Detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(3): 225-233.
[15]左奇,史忠科.一种基于数学形态学的实时车牌图像分割方法.中国图象图形学报,2003,8A(3):281-285.
[16]Mallat S.A Theory for Multi-resolution Signal Decomposition:the Wavelet Representation.IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[17]Kass M.,Witkin A.,and Terzopoulos D.Snakes:Active Contour Models.International Journal of Computer Vision,1988,1(4):321-331.
[18]Mclnerney T.,and Terzopoulos D.Deformable Models in Medical Image Analysis:A Survey.Medical Image Analysis,1996,1(2):91-108.
[19]Montagnat J.,Delingette H.,and Ayache N.A Review of Deformable Surfaces:Topology,Geometry and Deformation.Image and Vision Computing,19(14):1023-1040.
[20]XU C.,and Prince J.Snakes,Shapes,and Gradient Vector Flow.IEEE Transactions on Image Processing,1998,7(3):359-368.
[21]Gunn S.R.,and Nixon M.S.A Robust Snake Implementation:A Dual Active Contour.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(1):63-68.
[22]Wang H.,and Ghosh B.Geometric Active Deformable Models in Shape Modeling.IEEE Transactions on Image Processing,2000,9(2):302-308.
[23]Wei Qian,Laurence P.Clarke,Maria Kallergi,et al.Tree-Structured Nonlinear Filters in Digital Mammography.IEEE Transactions on Medical Imaging,1994,13(1):25-36.
[24]Wei Zhang,Xiang Zhong Fang,Xiaokang K.Yang,et ai.Moving Cast Shadows Detection Using Ratio Edge.IEEE Transactions on Multimedia,2007,9(6):1202-1214.
[25]Sohail Nadimi,and Bir Bhanu.Physical Models for Moving Shadow and Object Detection in Video.IEEE Transaction on Pattern Analysis and Machine Intelligence,2004,26(8):1079-1087.
[26]Rita Cucchiara,Costantino Grana,Massimo Piccardi,et al.Detecting Moving Objects,Ghosts,and Shadows in Video Streams.IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(10):1337-1342.
[27]Sohail Nadimi,and Bir Bhanu.Physical Models for Moving Shadow and Object Detection in Video.IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(8):1079-1087.
[28]Rui Zhang,Sizhu Zhang,and Songyu Yu.Moving Objects Detection Method Based on Brightness Distortion and Chromaticity Distortion.IEEE Transactions on Consumer Electronics,2007,53(3):1177-1185.
[29]Hong Yang,Jinwen Tian,Ying Chu,et al.Spatiotemporal Smooth Model for Moving Object Detection.IEEE Signal Processing Letters,2008,15:497-500.
[30]Michal Irani,and P.Anandan.A Unified Approach to Moving Object Detection in 2D and 3D Scenes.IEEE Transactions on Pattern Analysis and Machine Intelligence.1998,20(6):577-589.
[31]Liquan Shen,Zhi Liu,and Zhaoyang Zhang.Fast Inter Mode Decision Using Spatial Property of Motion Field.IEEE Transactions on Multimedia,2008,10(6):1208-1214.
[32]J.F.Yang,S.-C.Chang,and C.-Y.Chen.Computation Reduction for Motion Search in low Rate Video Coders.IEEE Transactions on Circuits System Video Technology,2002,12(10):948-951.
[33]X.Jing,and L.P.Chau.Fast Approach for H.264 inter Mode Decision.Electronical Letters,2002,12(10):948-951.
[34]Naoya Oshima,Takeshi Saitoh,and Ryosuke Konishi.Automatic Moving Object Detection and Tracking with Mean Shift for Surveillance System.2006International Symposium on Intelligent Signal Processing and Communication Systems.Yonago Convention Center,Tottori,Japan.578-581.
[35]Richard J.Radke,Srinivas Andra,and Omar Al-Kofahi.Image Change Detection Algorithms:A Systematic Survey.IEEE Transactions on Image Processing,2005,14(3):294-307.
[36]Lixin Shen,Manos Papadakis,Ioannis A.Kakadiaris,et al.Image Denoising Using a Tight Frame.IEEE Transactions on Image Processing.2006,15(5):1254-1263.
[37]肖本贤,陆诚,陈昊,等.基于帧间差分法和不变矩特征的运动目标检测与识别.第27届中国控制会议.昆明:2008:578-581.
[38]Ying Li,Anthony Vodacek,Robert L.Kremens,et al.A Hybrid Contextual Approach to Wildland Fire Detection Using Multispectral Imagery.IEEE Transactions on Geoscience and Remote Sensing,2005,43(9):2115-2126.
[39]Ivan A.Csiszar,and Wilfrid Schroeder.Short-Term Observations of the Temporal Development of Active Fires From Consecutive Same-Day ETM+and ASTER Imagery in the Amazon:Implications for Active Fire Product Validation.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2008,1(4):248-253.
[40]Simon Y.Foo.A Fuzzy Logic Approach to Fire Detection in Aircraft Dry Bays and Engine Compartments.IEEE Transaction on Industrial Electronics,2000,47(5):1161-1171.
[41]Takamoto Watanabe,and Shigenori Yamauch.An All-Digital PLL for Frequency Multiplication by 4 to 1022 With Seven-Cycle Lock Time.IEEE Journal of Solid-State Circuits,2003,38(2):198-204.
[42]Soo Chin Liew,Agnes Lim,and Leong Keong Kwoh.A Stochastic Model for Active Fire Detection Using the Thermal Bands of MODIS Data.IEEE Geoscience and Remote Sensing Letters,2005,2(3):337-341.
[43]Kuo L.Su.Automatic Fire Detection System Using Adaptive Fusion Algorithm for Fire Fighting Robot.IEEE International Conference on Systems,Man,and Cybernetics,2006,966-971.
[44]Yu Qiongfang,Zheng Dezhong,Fu Yongli,et al.Intelligent Fire Alarm System Based on Fuzzy Neural Network.International Workshop on Intelligent Systems and Applications,2009,1-4.
[45]李春鑫,王孝通,徐晓刚.基于积分边缘强度局部均值的红外目标跟踪.激光与红外.2009,39(7):773-775.
[46]Bo-Ho Cho,Jong-Wook Bae,and Sung-Hwan Jung.Image Processing-based Fire Detection System using Statistic Color Model.International Conference on Advanced Language Processing and Web Information Technology,2008,245-250.
[47]金华彪.基于数字图像处理的火灾探测技术.消防科学与技术.2003,(3):46-47.
[48]Emory C.Thomas.A Pneumatic Sampling Fire Detection System in an Underground Haulage way.IEEE Transactions on Industry Applications,1983,IA-19(3):440-444.
[49]Christina Arnold,Michael Harms,and Joachim Goschnick.Air Quality Monitoring and Fire Detection With The Karlsruhe Electronic Micronose KAMINA.IEEE Sensors Journal,2002,2(3):179-188.
[50]R.L.P.Custer,D.M.Demarest,P.H.Dobson,et al.Detecting and Minimizing Potential Impacts From Valve Hall Fires.Transactions on Power Delivery,1992,7(1):281-286.
[51]Oh-Hyun Kwon,Sung-Min Cho,and Sun-Myung Hwang.Design and Implementation of Fire Detection System.Advanced Software Engineering and Its Applications,2008,233-236.
[52]Federico Alimenti,Stefania Bonafoni,Salvatore Leone,et al.A Low-Cost Microwave Radiometer for the Detection of Fire in Forest Environments.IEEE Transactions on Geoscience and Remote Sensing,2008,46(9):2632-2643.
[53]徐树梅.基于单片机PIC16F877的智能检测装置.硕士学位论文.太原:太原理工大学,2003,6-34.
[54]A.J.AI-Khalili,D.AI-Khalili,and M.S.Khassem.Multiple Single-chip Microcomputer Approach to Fire Detection and Monitoring System.IEE Proceedings,1988,135(1):1-10.
[55]Jang-Hun Yeh,Raymond K.Kostuk,and Kun-Yii Tu.Board Level H-Tree Optical Clock Distribution with Substrate Mode Holograms.Journal of Lightwave Technology,1995,13(7):1566-1578.
[56]Hong-Wen Lin,Shao-Qing Yang,Zhi-Jun Xia,et al.A Moving Objects Detection Approach for Smart Sensor.Proceedings of the Fifth International Conference on Machine Learning and Cybernetics.Dalian:2006:3751-3754.
[57]郭文华.基于I~2C总线的串行E~2PROM及其应用.常熟理工学院学报(自然科学),2008,22(10):70-74.
[58]李素敏,万燕,曾培峰,等.n链码对异形纤维特征参数的作用.东华大学学报(自然科学版),2008,34(5):608-613.
[59]Borgefors G.Distance Transformations in Digital Images.Computer Vision,Graphics and Image Processing,1986,34(3):334-371.
[60]De Assis Zampirolli F.,and De Alencar Lotufo R,Classification of the Distance Transformation Algorithms under the Mathematical Morphology Approach.In Gramado,ⅩⅢ Brizilian Symposium on Computer Graphics and Image Processing(SIBGRAPI'00),Washington DC,IEEE Computer Society,2000:292-299.
[61]陆宗骐.C/C++图像处理编程.北京:清华大学出版社,2005.
[2]Nick Kanopoulos,Nagesh Vasanthavada,and Robert L.Baker.Design of an Image Edge Detection Filter Using the Sobel Operator.IEEE Journal of Solid-State Circuits,1988,23(2):358-367.
[3]初秀琴,曾祥永,李玉山.一种新型的实时图像处理机结构及Sobel电路设计.仪器仪表学报,2003,24(5):506-508.
[4]Yau-Hwang Kuo,Chang-Shing Lee,and Chao-Chin Liu.A New Fuzzy Edge Detection Method for Image Enhancement.In:Fuzzy Systems,1997,Proceedings of the Sixth IEEE International Conference,1997,2(1):1069-1074.
[5]Sathyadev V.Uppala,and John D.Sahr.On the Design of Quadratic Filters with Application to Image Processing.IEEE Transactions on Image Processing,1997,6(4):618-614.
[6]陆宗骐,梁诚.用Sobel算子细化边缘.中国图像图形学报,2000,5(6):516-520.
[7]Natalia Kazakova,Martin Margala,and Nelson G.Durdle.Sobel Edge Detection Processor for a Real-time Volume Rendering System.Circuits and Systems,2004.ISCAS '04.Proceedings of the 2004 International Symposium on,2004,2(23):Ⅱ913-Ⅱ916.
[8]郑敏,王有熙,税冬东.基于Sobel算子含噪低对比度图像的边缘检测方法.石河子大学学报(自然科学版),2008,26(1):117-119.
[9]王植,贺赛先.一种基于Canny理论的自适应边缘检测方法.中国图像图形学报,2004,9(8):957-962.
[10]Canny J.F.Finding Edges and Lines in Images.MIT AI-TR-720,1983.
[11]Bergholm F.Edge Focusing.IEEE Transactions on Pattern Analysis and Machine Intelligence,1987,9(9):726-741.
[12]Wilkinson M.H.F.Optimizing Edge Detectors for Robust Automatic Threshold Selection:Coping with Edge Curvature and Noise.Graphical Models and Image Processing,1998,60(4):385-401.
[13]Chanda B.,Kundu M.K.,and Vanipadmaja Y.A Multi-Scale Morphologic Edge Detector.Pattern Recognition,1998,31(10):1469-1478.
[14]Pavlidis T.,and Liow Y.T.Integrating Region Growing and Edge Detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(3): 225-233.
[15]左奇,史忠科.一种基于数学形态学的实时车牌图像分割方法.中国图象图形学报,2003,8A(3):281-285.
[16]Mallat S.A Theory for Multi-resolution Signal Decomposition:the Wavelet Representation.IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[17]Kass M.,Witkin A.,and Terzopoulos D.Snakes:Active Contour Models.International Journal of Computer Vision,1988,1(4):321-331.
[18]Mclnerney T.,and Terzopoulos D.Deformable Models in Medical Image Analysis:A Survey.Medical Image Analysis,1996,1(2):91-108.
[19]Montagnat J.,Delingette H.,and Ayache N.A Review of Deformable Surfaces:Topology,Geometry and Deformation.Image and Vision Computing,19(14):1023-1040.
[20]XU C.,and Prince J.Snakes,Shapes,and Gradient Vector Flow.IEEE Transactions on Image Processing,1998,7(3):359-368.
[21]Gunn S.R.,and Nixon M.S.A Robust Snake Implementation:A Dual Active Contour.IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(1):63-68.
[22]Wang H.,and Ghosh B.Geometric Active Deformable Models in Shape Modeling.IEEE Transactions on Image Processing,2000,9(2):302-308.
[23]Wei Qian,Laurence P.Clarke,Maria Kallergi,et al.Tree-Structured Nonlinear Filters in Digital Mammography.IEEE Transactions on Medical Imaging,1994,13(1):25-36.
[24]Wei Zhang,Xiang Zhong Fang,Xiaokang K.Yang,et ai.Moving Cast Shadows Detection Using Ratio Edge.IEEE Transactions on Multimedia,2007,9(6):1202-1214.
[25]Sohail Nadimi,and Bir Bhanu.Physical Models for Moving Shadow and Object Detection in Video.IEEE Transaction on Pattern Analysis and Machine Intelligence,2004,26(8):1079-1087.
[26]Rita Cucchiara,Costantino Grana,Massimo Piccardi,et al.Detecting Moving Objects,Ghosts,and Shadows in Video Streams.IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,25(10):1337-1342.
[27]Sohail Nadimi,and Bir Bhanu.Physical Models for Moving Shadow and Object Detection in Video.IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,26(8):1079-1087.
[28]Rui Zhang,Sizhu Zhang,and Songyu Yu.Moving Objects Detection Method Based on Brightness Distortion and Chromaticity Distortion.IEEE Transactions on Consumer Electronics,2007,53(3):1177-1185.
[29]Hong Yang,Jinwen Tian,Ying Chu,et al.Spatiotemporal Smooth Model for Moving Object Detection.IEEE Signal Processing Letters,2008,15:497-500.
[30]Michal Irani,and P.Anandan.A Unified Approach to Moving Object Detection in 2D and 3D Scenes.IEEE Transactions on Pattern Analysis and Machine Intelligence.1998,20(6):577-589.
[31]Liquan Shen,Zhi Liu,and Zhaoyang Zhang.Fast Inter Mode Decision Using Spatial Property of Motion Field.IEEE Transactions on Multimedia,2008,10(6):1208-1214.
[32]J.F.Yang,S.-C.Chang,and C.-Y.Chen.Computation Reduction for Motion Search in low Rate Video Coders.IEEE Transactions on Circuits System Video Technology,2002,12(10):948-951.
[33]X.Jing,and L.P.Chau.Fast Approach for H.264 inter Mode Decision.Electronical Letters,2002,12(10):948-951.
[34]Naoya Oshima,Takeshi Saitoh,and Ryosuke Konishi.Automatic Moving Object Detection and Tracking with Mean Shift for Surveillance System.2006International Symposium on Intelligent Signal Processing and Communication Systems.Yonago Convention Center,Tottori,Japan.578-581.
[35]Richard J.Radke,Srinivas Andra,and Omar Al-Kofahi.Image Change Detection Algorithms:A Systematic Survey.IEEE Transactions on Image Processing,2005,14(3):294-307.
[36]Lixin Shen,Manos Papadakis,Ioannis A.Kakadiaris,et al.Image Denoising Using a Tight Frame.IEEE Transactions on Image Processing.2006,15(5):1254-1263.
[37]肖本贤,陆诚,陈昊,等.基于帧间差分法和不变矩特征的运动目标检测与识别.第27届中国控制会议.昆明:2008:578-581.
[38]Ying Li,Anthony Vodacek,Robert L.Kremens,et al.A Hybrid Contextual Approach to Wildland Fire Detection Using Multispectral Imagery.IEEE Transactions on Geoscience and Remote Sensing,2005,43(9):2115-2126.
[39]Ivan A.Csiszar,and Wilfrid Schroeder.Short-Term Observations of the Temporal Development of Active Fires From Consecutive Same-Day ETM+and ASTER Imagery in the Amazon:Implications for Active Fire Product Validation.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2008,1(4):248-253.
[40]Simon Y.Foo.A Fuzzy Logic Approach to Fire Detection in Aircraft Dry Bays and Engine Compartments.IEEE Transaction on Industrial Electronics,2000,47(5):1161-1171.
[41]Takamoto Watanabe,and Shigenori Yamauch.An All-Digital PLL for Frequency Multiplication by 4 to 1022 With Seven-Cycle Lock Time.IEEE Journal of Solid-State Circuits,2003,38(2):198-204.
[42]Soo Chin Liew,Agnes Lim,and Leong Keong Kwoh.A Stochastic Model for Active Fire Detection Using the Thermal Bands of MODIS Data.IEEE Geoscience and Remote Sensing Letters,2005,2(3):337-341.
[43]Kuo L.Su.Automatic Fire Detection System Using Adaptive Fusion Algorithm for Fire Fighting Robot.IEEE International Conference on Systems,Man,and Cybernetics,2006,966-971.
[44]Yu Qiongfang,Zheng Dezhong,Fu Yongli,et al.Intelligent Fire Alarm System Based on Fuzzy Neural Network.International Workshop on Intelligent Systems and Applications,2009,1-4.
[45]李春鑫,王孝通,徐晓刚.基于积分边缘强度局部均值的红外目标跟踪.激光与红外.2009,39(7):773-775.
[46]Bo-Ho Cho,Jong-Wook Bae,and Sung-Hwan Jung.Image Processing-based Fire Detection System using Statistic Color Model.International Conference on Advanced Language Processing and Web Information Technology,2008,245-250.
[47]金华彪.基于数字图像处理的火灾探测技术.消防科学与技术.2003,(3):46-47.
[48]Emory C.Thomas.A Pneumatic Sampling Fire Detection System in an Underground Haulage way.IEEE Transactions on Industry Applications,1983,IA-19(3):440-444.
[49]Christina Arnold,Michael Harms,and Joachim Goschnick.Air Quality Monitoring and Fire Detection With The Karlsruhe Electronic Micronose KAMINA.IEEE Sensors Journal,2002,2(3):179-188.
[50]R.L.P.Custer,D.M.Demarest,P.H.Dobson,et al.Detecting and Minimizing Potential Impacts From Valve Hall Fires.Transactions on Power Delivery,1992,7(1):281-286.
[51]Oh-Hyun Kwon,Sung-Min Cho,and Sun-Myung Hwang.Design and Implementation of Fire Detection System.Advanced Software Engineering and Its Applications,2008,233-236.
[52]Federico Alimenti,Stefania Bonafoni,Salvatore Leone,et al.A Low-Cost Microwave Radiometer for the Detection of Fire in Forest Environments.IEEE Transactions on Geoscience and Remote Sensing,2008,46(9):2632-2643.
[53]徐树梅.基于单片机PIC16F877的智能检测装置.硕士学位论文.太原:太原理工大学,2003,6-34.
[54]A.J.AI-Khalili,D.AI-Khalili,and M.S.Khassem.Multiple Single-chip Microcomputer Approach to Fire Detection and Monitoring System.IEE Proceedings,1988,135(1):1-10.
[55]Jang-Hun Yeh,Raymond K.Kostuk,and Kun-Yii Tu.Board Level H-Tree Optical Clock Distribution with Substrate Mode Holograms.Journal of Lightwave Technology,1995,13(7):1566-1578.
[56]Hong-Wen Lin,Shao-Qing Yang,Zhi-Jun Xia,et al.A Moving Objects Detection Approach for Smart Sensor.Proceedings of the Fifth International Conference on Machine Learning and Cybernetics.Dalian:2006:3751-3754.
[57]郭文华.基于I~2C总线的串行E~2PROM及其应用.常熟理工学院学报(自然科学),2008,22(10):70-74.
[58]李素敏,万燕,曾培峰,等.n链码对异形纤维特征参数的作用.东华大学学报(自然科学版),2008,34(5):608-613.
[59]Borgefors G.Distance Transformations in Digital Images.Computer Vision,Graphics and Image Processing,1986,34(3):334-371.
[60]De Assis Zampirolli F.,and De Alencar Lotufo R,Classification of the Distance Transformation Algorithms under the Mathematical Morphology Approach.In Gramado,ⅩⅢ Brizilian Symposium on Computer Graphics and Image Processing(SIBGRAPI'00),Washington DC,IEEE Computer Society,2000:292-299.
[61]陆宗骐.C/C++图像处理编程.北京:清华大学出版社,2005.