微机视觉系统相关理论及技术研究
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摘要
计算机视觉作为一个多学科交叉领域，其研究内容和应用领域都很广泛。
近年来，硬件设备价格的不断降低，计算机性能的逐步提高，为计算机视觉的
研究和应用准备了很好的条件。因此，对计算机视觉的进一步研究，不但具有
重要的学术意义，而且具有重要的实用价值。
计算机视觉是通过一幅或多幅图像提取、识别、解释和重建三维环境信息
的过程。主要包括五个方面：视频捕获、摄像机标定、图像预处理和特征提取、
立体匹配以及三维重建。本文主要围绕计算机视觉这几方面的内容展开研究。
计算机视觉直接处理的对象是对三维环境采集到的二维图像，因此视频捕
捉是计算机视觉后续处理的前提。视觉系统往往有实时性的要求，但一般随视
频卡附带的应用程序并不能满足实时性的要求。本文分析探讨Windows环境
下视频捕捉的一般系统框架和技术，开发了一个Windows环境下的视频捕捉
系统，实现了视频数据的直接内存访问，能够很好地满足视觉系统实时性的要
求。
空间物体表面某点的三维几何位置与其在图像中对应点之间的相互关系
是由摄像机成像的几何模型决定的。这些几何模型的参数就是摄像机参数，摄
像机标定就是确定摄像机的这些参数，是计算机视觉的一个重要内容。本文对
现有的摄像机标定技术进行了分类研究，针对一般的工业需求，基于一阶径向
畸变的小孔摄像机模型，提出一种新的逐步分解的摄像机线性标定方法，合理
分解摄像机参数，全部求解过程均采用线性算法，避免了非线性优化的不稳定
性，简单实用。实验结果表明，该算法具有较好的标定精度和较快的处理速度。
利用小波变换良好的局部化特性和多分辨率特征，提出基于小波变换的图
像去噪方法和边缘轮廓提取方法。噪声和信号在小波变换域中有不同的表现特
性。本文提出的移位冗余平均处理的小波去噪方法，在去除噪声的同时，具有
良好的保持边缘特性的能力。在利用小波变换提取边缘轮廓中，采用自适应边
缘链的平均幅度阈值，有利于根据图像特征提取图像边缘。
如何准确确定两幅或多幅图像中的对应点，即立体匹配，是计算机立体视
觉中的一个难点。本文提出一种双阈值的分阶段匹配方法，以图像边缘点为匹
配基元，选择米字条形窗口作为相关窗口，加快了相关运算的速度，采用双阈
值进行判决，可在第一层将大量可能匹配特征检测出来，减少下一层匹配的搜
索空间。
最后，本文论述了作者参与开发的一个足球机器人视觉识别系统，这是一
个实时的计算机视觉系统。为满足足球机器人对抗比赛的要求，视觉系统必须
实时快速准确地确定机器人小车和足球的瞬时位置和运动参数。本文提出一种
基于区域投影的视觉识别新算法，充分利用系统中的颜色和形状特征，可快速
准确地识别全部机器人小车和小球。实验结果表明，识别位置精度误差不超过
IllllllQ piXCIS人采用 Pll350的 PC，识别速度可达到每帧仅用时 2毫秒，能
够很好地满足该系统的速度和精度要求。
Computer Vision has been evolving as a multidisciplinary subject. Its contours blend into those of artificial intelligence, robotics, signal processing, pattern recognition, control theory, psychology, neuroscience, and other fields. Its research area and application area spread widely. Recently, price of the video devices is going down, and performance of computer is going up. All of these promote the research and application of computer vision to a new level. Studying on computer vision has important significance not only on science but also on practical application.
Computer vision is a synthesis and cognition science of 3D world from one or more digital images. Generally, the key techniques of a computer vision system is composed of five parts, which are video capture, camera calibration, image pre-processing and feature extraction, stereo correspondence and 3D reconstruction. This dissertation deals with these key problems in computer vision, and presented some new ideas, approaches and come to some valuable results.
The original objects processed in computer vision are digital images captured by digital video devices. System structure and technical details in video capture are studied. Based on Windows platform, a real time video capture system is developed by accessing to buffer directly. It can satisfy the need of real time vision system well.
As one of the basic tasks in computer vision, camera calibration determines the internal and external parameters of a vision system. In this dissertation, based on modified pine-hole camera model with one order radial lens deformation, a new linear multi-step method for camera calibration is developed. With some appropriate transformation and arrangement, the camera parameters can be calculated by solving these linear equations sequentially only. The running result of this new method shows that the algorithm is simple and efficient, and the precision is good enough. A image distortion correction method for radial lens distortion with segment slope is also described. Both simulated image and real image running results show that this correction method is robust and accurate.
There has been growing interest of wavelet-based denoising schemes and edge detection techniques recently. Such popularity is mainly due to that wavelet provides an appropriate basis for separating noise signal from image signal and makes noise and edge show different attributes. A denoising algorithm based on wavelet transform with redundant representation is proposed, which can not only
make improved denoising performance, but also suppress the Gibbs phenomena. A method of edge detection based on wavelets transform is also presented, in which a self-adapted method for selecting the threshold of edge chain evenness is adopted to detect edge well.
Stereo correspondence is to determine that an item in one image corresponds to another item in other images of the same scene. Based on edge points, a two-stage stereo matching method with two thresholds is proposed.
At last, a real time vision recognition system of MIROSOT is developed to recognize all vehicle robots and football. In this system, the rapid processing speed and high recognizing accuracy are needed. To meet such requirements, a recognition algorithm based on object projection is presented. The vision recognition is processed with two stages. In the first stage, a grid search scanning to find an object point inside an unrecognizing object is processed; then a following precision analyzing and recognizing procedure based on object region projection is processed for determining the robots and football's position and also the robots' orientation in the second stage. The recognition algorithm is robust in treating noisy image captured by CCD, even in the situation while multiple vehicle robots are closely adjacent each other. The algorithm is also very efficient, it takes only 2ms running in PII350 PC for recognizing and identifying all objects in a captured frame.
近年来，硬件设备价格的不断降低，计算机性能的逐步提高，为计算机视觉的
研究和应用准备了很好的条件。因此，对计算机视觉的进一步研究，不但具有
重要的学术意义，而且具有重要的实用价值。
计算机视觉是通过一幅或多幅图像提取、识别、解释和重建三维环境信息
的过程。主要包括五个方面：视频捕获、摄像机标定、图像预处理和特征提取、
立体匹配以及三维重建。本文主要围绕计算机视觉这几方面的内容展开研究。
计算机视觉直接处理的对象是对三维环境采集到的二维图像，因此视频捕
捉是计算机视觉后续处理的前提。视觉系统往往有实时性的要求，但一般随视
频卡附带的应用程序并不能满足实时性的要求。本文分析探讨Windows环境
下视频捕捉的一般系统框架和技术，开发了一个Windows环境下的视频捕捉
系统，实现了视频数据的直接内存访问，能够很好地满足视觉系统实时性的要
求。
空间物体表面某点的三维几何位置与其在图像中对应点之间的相互关系
是由摄像机成像的几何模型决定的。这些几何模型的参数就是摄像机参数，摄
像机标定就是确定摄像机的这些参数，是计算机视觉的一个重要内容。本文对
现有的摄像机标定技术进行了分类研究，针对一般的工业需求，基于一阶径向
畸变的小孔摄像机模型，提出一种新的逐步分解的摄像机线性标定方法，合理
分解摄像机参数，全部求解过程均采用线性算法，避免了非线性优化的不稳定
性，简单实用。实验结果表明，该算法具有较好的标定精度和较快的处理速度。
利用小波变换良好的局部化特性和多分辨率特征，提出基于小波变换的图
像去噪方法和边缘轮廓提取方法。噪声和信号在小波变换域中有不同的表现特
性。本文提出的移位冗余平均处理的小波去噪方法，在去除噪声的同时，具有
良好的保持边缘特性的能力。在利用小波变换提取边缘轮廓中，采用自适应边
缘链的平均幅度阈值，有利于根据图像特征提取图像边缘。
如何准确确定两幅或多幅图像中的对应点，即立体匹配，是计算机立体视
觉中的一个难点。本文提出一种双阈值的分阶段匹配方法，以图像边缘点为匹
配基元，选择米字条形窗口作为相关窗口，加快了相关运算的速度，采用双阈
值进行判决，可在第一层将大量可能匹配特征检测出来，减少下一层匹配的搜
索空间。
最后，本文论述了作者参与开发的一个足球机器人视觉识别系统，这是一
个实时的计算机视觉系统。为满足足球机器人对抗比赛的要求，视觉系统必须
实时快速准确地确定机器人小车和足球的瞬时位置和运动参数。本文提出一种
基于区域投影的视觉识别新算法，充分利用系统中的颜色和形状特征，可快速
准确地识别全部机器人小车和小球。实验结果表明，识别位置精度误差不超过
IllllllQ piXCIS人采用 Pll350的 PC，识别速度可达到每帧仅用时 2毫秒，能
够很好地满足该系统的速度和精度要求。
Computer Vision has been evolving as a multidisciplinary subject. Its contours blend into those of artificial intelligence, robotics, signal processing, pattern recognition, control theory, psychology, neuroscience, and other fields. Its research area and application area spread widely. Recently, price of the video devices is going down, and performance of computer is going up. All of these promote the research and application of computer vision to a new level. Studying on computer vision has important significance not only on science but also on practical application.
Computer vision is a synthesis and cognition science of 3D world from one or more digital images. Generally, the key techniques of a computer vision system is composed of five parts, which are video capture, camera calibration, image pre-processing and feature extraction, stereo correspondence and 3D reconstruction. This dissertation deals with these key problems in computer vision, and presented some new ideas, approaches and come to some valuable results.
The original objects processed in computer vision are digital images captured by digital video devices. System structure and technical details in video capture are studied. Based on Windows platform, a real time video capture system is developed by accessing to buffer directly. It can satisfy the need of real time vision system well.
As one of the basic tasks in computer vision, camera calibration determines the internal and external parameters of a vision system. In this dissertation, based on modified pine-hole camera model with one order radial lens deformation, a new linear multi-step method for camera calibration is developed. With some appropriate transformation and arrangement, the camera parameters can be calculated by solving these linear equations sequentially only. The running result of this new method shows that the algorithm is simple and efficient, and the precision is good enough. A image distortion correction method for radial lens distortion with segment slope is also described. Both simulated image and real image running results show that this correction method is robust and accurate.
There has been growing interest of wavelet-based denoising schemes and edge detection techniques recently. Such popularity is mainly due to that wavelet provides an appropriate basis for separating noise signal from image signal and makes noise and edge show different attributes. A denoising algorithm based on wavelet transform with redundant representation is proposed, which can not only
make improved denoising performance, but also suppress the Gibbs phenomena. A method of edge detection based on wavelets transform is also presented, in which a self-adapted method for selecting the threshold of edge chain evenness is adopted to detect edge well.
Stereo correspondence is to determine that an item in one image corresponds to another item in other images of the same scene. Based on edge points, a two-stage stereo matching method with two thresholds is proposed.
At last, a real time vision recognition system of MIROSOT is developed to recognize all vehicle robots and football. In this system, the rapid processing speed and high recognizing accuracy are needed. To meet such requirements, a recognition algorithm based on object projection is presented. The vision recognition is processed with two stages. In the first stage, a grid search scanning to find an object point inside an unrecognizing object is processed; then a following precision analyzing and recognizing procedure based on object region projection is processed for determining the robots and football's position and also the robots' orientation in the second stage. The recognition algorithm is robust in treating noisy image captured by CCD, even in the situation while multiple vehicle robots are closely adjacent each other. The algorithm is also very efficient, it takes only 2ms running in PII350 PC for recognizing and identifying all objects in a captured frame.
引文
[1] 郑南宁，计算机视觉与模式识别，国防工业出版社，北京，1998．
[2] Emanuele Trucco, Alessandro Verri. Introductory techniques for 3-D computer vision. Prentice Hall, 1998.
[3] 马颂德，张正友．计算机视觉-计算理论与实践，科学出版社，1998．
[4] 吴立德．计算机视觉．复旦大学出版社，上海，1993．
[5] Ohya A,Kosaka A. Vision-based navigation by a mobile robot with obstacle avoidance using single camera vision and ultrasonic sensing. IEEE Trans. on RA,1998,14: 969～978.
[6] Guiducci A. 3D road reconstruction from a single view. CVGIP:Computer Vision and Image Understanding, 1998,70:212～226.
[7] Thorpe C E. Vision and navigation the Carnegie Mello Navlab, Kluwer Academic Publishers, 1990.
[8] Pien H,Karl W C. Special issue-processing of MR images of the human brain. Int. J. of Pattern Recognition and Artificial Intelligence, 1997,11:1157～1401.
[9] Taxt T, Lundervold. Advances in medical imaging. Int. Conf. On Pattern Recognition, 1998,86: 505～508.
[10] Medical Imaging 1998,Proc. SPIE:3335～3340.
[11] Marr D. Vision. W.H.Freeman and Company, 1982.
[12] Pentland A P. A new sense of depth of field. IEEE Trans on PAMI, 1987,9:523～531.
[13] Robert M Haralick, Linda G Shapiro. Computer and Robot Vision. Addison-Wesley Publishing Company, 1992: 48～55.
[14] 孙龙祥，程义民等编著．深度图像分析．电子工业出版社，北京，1996．
[15] 田思，袁占亭．计算机视觉系统框架的新构思．计算机工程于应用，2000，6：57～59．
[16] Ozeki O,et al. Real time range measurement device for 3D object recognition. IEEE Trans. on PAMI, 1986,8: 550～553.
[17] Landman M M,et al. A flexible industrial system for automated 3D inspection. Proc. of SPIE, 1986,728: 203～209.
[18] Binger N, et al. The application of laser radar technology. Sensors, 1987,4(4): 42～44.
[19] Ikeuchi K, Horn B K P. Numerical shape from shaping and occluding boundaries. Artificial Intelligence, 1971, 17:141～184.
[20] Barnard S, et al. Computational stereo. ACM Computing Surveys, 1982,14:553～572.
[21] 贾云德．机器视觉．科学出版社，北京，2000．
[22] 陈熙霖．视觉计算-人类感知能力的延伸．测控技术，2000，19(5)：7～14．
[23] 刘曙光，刘明远等．机器视觉机器应用．机械制造，2000，Vol38(431)：20～23．
[24] 游素亚，徐光佑．立体视觉研究的现状与进展．中国图象图形学报，1997，Vol2(1)：17～24．
[25] 邱茂林，马颂德．计算机视觉中摄像机定标综述．自动化学报，2000，26(1)：43～55．
[26] Tsai R.Y. A versatile camera calibration technique for high-accuracy 3D machine vision metroiogy using off-the-shelf TV camera and lenses. IEEE Journal of Automation, 1987, 3(4): 323～334.
[27] 数字图像处理，K R Castleman著，朱志刚等译，电子工业出版社，1998．
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[2] Emanuele Trucco, Alessandro Verri. Introductory techniques for 3-D computer vision. Prentice Hall, 1998.
[3] 马颂德，张正友．计算机视觉-计算理论与实践，科学出版社，1998．
[4] 吴立德．计算机视觉．复旦大学出版社，上海，1993．
[5] Ohya A,Kosaka A. Vision-based navigation by a mobile robot with obstacle avoidance using single camera vision and ultrasonic sensing. IEEE Trans. on RA,1998,14: 969～978.
[6] Guiducci A. 3D road reconstruction from a single view. CVGIP:Computer Vision and Image Understanding, 1998,70:212～226.
[7] Thorpe C E. Vision and navigation the Carnegie Mello Navlab, Kluwer Academic Publishers, 1990.
[8] Pien H,Karl W C. Special issue-processing of MR images of the human brain. Int. J. of Pattern Recognition and Artificial Intelligence, 1997,11:1157～1401.
[9] Taxt T, Lundervold. Advances in medical imaging. Int. Conf. On Pattern Recognition, 1998,86: 505～508.
[10] Medical Imaging 1998,Proc. SPIE:3335～3340.
[11] Marr D. Vision. W.H.Freeman and Company, 1982.
[12] Pentland A P. A new sense of depth of field. IEEE Trans on PAMI, 1987,9:523～531.
[13] Robert M Haralick, Linda G Shapiro. Computer and Robot Vision. Addison-Wesley Publishing Company, 1992: 48～55.
[14] 孙龙祥，程义民等编著．深度图像分析．电子工业出版社，北京，1996．
[15] 田思，袁占亭．计算机视觉系统框架的新构思．计算机工程于应用，2000，6：57～59．
[16] Ozeki O,et al. Real time range measurement device for 3D object recognition. IEEE Trans. on PAMI, 1986,8: 550～553.
[17] Landman M M,et al. A flexible industrial system for automated 3D inspection. Proc. of SPIE, 1986,728: 203～209.
[18] Binger N, et al. The application of laser radar technology. Sensors, 1987,4(4): 42～44.
[19] Ikeuchi K, Horn B K P. Numerical shape from shaping and occluding boundaries. Artificial Intelligence, 1971, 17:141～184.
[20] Barnard S, et al. Computational stereo. ACM Computing Surveys, 1982,14:553～572.
[21] 贾云德．机器视觉．科学出版社，北京，2000．
[22] 陈熙霖．视觉计算-人类感知能力的延伸．测控技术，2000，19(5)：7～14．
[23] 刘曙光，刘明远等．机器视觉机器应用．机械制造，2000，Vol38(431)：20～23．
[24] 游素亚，徐光佑．立体视觉研究的现状与进展．中国图象图形学报，1997，Vol2(1)：17～24．
[25] 邱茂林，马颂德．计算机视觉中摄像机定标综述．自动化学报，2000，26(1)：43～55．
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