仿人眼颈视觉系统的理论与应用研究
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摘要
机器视觉是一门新兴的学科,是从信息处理的层次研究视觉信息的认知过
程;机器视觉系统则是能够实现某些视觉功能的硬件和软件的综合。通过广大研
究人员的努力,机器视觉已取得了很大的进展,开始从实验室走向实际应用阶段。
但是客观地讲,机器视觉不论是在理论上,还是实际应用中,都存在着较多不足,
还处于不成熟的研究阶段,有待进一步的改进与深入研究。本论文以仿人眼颈系
统为研究对象,建立了仿人眼颈视觉系统的体系结构和实现平台,研究相关基础
理论、技术与应用。主要包括系统模型、复杂背景下单目运动视觉分析、主动目
标跟踪、通用的摄像机标定技术、双目立体视觉的人脸重建、DSP视觉处理与实
现技术等。
论文介绍了仿人眼颈系统、机器视觉系统理论及发展历程;综述了运动视觉
分析和立体视觉系统的研究概况,指出机器视觉相关研究存在的问题与发展趋
势,并给出论文的主要研究内容。
建立了仿人眼颈视觉系统的数学模型,包括仿人眼颈系统的运动学数学模型
和摄像机数学模型。在深入分析摄像机数学模型的基础上,提出了一种基于直线
校正的摄像机标定方法,并且在摄像机镜头畸变参数的求解方法上,提出了一种
线性的求解方法,与非线性最小二乘求解相比,避免了非线性优化存在的依赖于
初始值、易收敛于局部极小值等缺陷,而且精度与非线性求解算法相当。
对运动视觉分析进行了详细的研究。提出了一种复杂背景下运动目标的检测
算法,通过对背景的高斯建模,进行运动目标检测,考虑目标影子的存在及环境
光照等条件有可能变化,提出了相应的影子消除及背景自适应更新算法,大大提
高了目标检测的可靠性。
针对场景中存在多种可能的目标,提出基于支持向量机的人体目标识别方
法。针对不同场景下,运动目标受到遮挡的程度不同,提出了基于头肩模型和星
形向量表示法的人体目标特征向量抽取方法。通过样本采集、特征抽取、支持向
量机训练得到最终的人体目标识别分类器。实验结果表明,基于现代统计学习理
论的支持向量机非常适合于有限样本条件下的目标分类。
研究运动目标定位、跟踪原理和实现方法。提出将背景匹配与帧间差分技术
相结合的方法,解决摄像机运动-目标运动模式下的运动目标检测难题。为了使
运动目标的跟踪更为平稳,提出了利用卡尔曼预测器对目标的位置进行再估计,
并在此基础上,研究了单目视觉跟踪和双目视觉协调跟踪的控制方法。
对基于双目立体视觉的特定人脸重建进行了详细的理论分析和实验研究。建
立了双目立体视觉测距的数学模型,分析了基于双目立体视觉进行人脸重建的原
理,为三维重建提供理论基础。提出了金字塔结构相关计算方法和活动轮廓模型
相结合的视差抽取方法,并在活动轮廓模型中采用了一种新的能量最小化方程,
解决了立体视觉中立体匹配的难点,成功地实现了人脸的三维重建。
本文还研制成功一种通用的DSP视觉处理平台。利用它,可以完成仿人眼
颈视觉处理及其它一些图象处理,为脱离PC机实现图象处理提供一种途径。详
细介绍系统的整体设计,主处理器及外围元器件的选择,各子模块和电路的设计
及一些实际经验和体会。
最后,介绍了本文研究成果的两个相关应用实例——目标自动跟踪一体化智
Machine vision is a newly developed, composite and multi-disciplinary subject. It studies on the cognition science of vision information from the level of information processing. Machine vision system is a synthesis of hardware and software, which can perform certain vision functions. After researchers' several decades' effort, machine vision develops rapidly, and it goes into practical application from laboratory. But to be objectively speaking, machine vision is still in immature stage. Either theory or practical application is deficient. This thesis concentrates on the human's eye and neck vision simulation system. It constructs the framework of the simulation system, studies on the basic theory, technology and application. The primary work includes system model, monocular motion analysis under complex background, objects active tracking, versatile camera calibration technology, specific face reconstruction based stereovision and DSP technology based vision processing.In this thesis the human's eye and neck vision simulation system, machine vision theory system and development are introduced. The state-of-the-art of the motion vision analysis and stereovision system is summarized. The existing problem and development trend, which are related with machine vision, are presented.The math models, which include kinematics math model of the human's eye and neck system and math model of the camera, are established. With analysis of the math model of camera in detail, a new camera calibration method based on line rectification is proposed. A linear algorithm is provided to obtain the solution. Compared with non-linear methods, it does not require a good initial guess to guarantee convergence and it does not have the problem of converging to a local minimum. The accuracy is the same as that of non-linear methods.Motion vision analysis is studied in detail. A motion objects detection algorithm under complex background is proposed. Objects are detected through scene Gaussian modeling. A series of methods are proposed to weaken the influence, which is brought by the shadow of the objects and lighting condition variations.A human body recognition method based Support Vector Machine is proposed to distinguish the different possible objects. Feature abstracts based on head and shoulder model and center radiating vector representation are used in different application. Human objects recognition classifier is obtained through samples collecting, feature abstract and SVM training. Experiments show that SVM suits for objects classifying under the condition of limited training samples.It studies on motion objects position, principle of tracking and implement method. The math model of the human's eye and neck system is established. A background match combined with inter-frame differential method is used to solve the difficulty of objects detection under the situation of both camera and objects moving. A Kalman predictor is used to estimate the object's position in order to make object tracking more steadily. Monocular tracking and binocular tracking control methods are also studied.
Theory analysis and experiment are performed to human's specific face recon struction based binocular vision. The math model of binocular vision length meas urement is set up. The principle of the face reconstruction based on binocular vision i: introduced. A new energy minimization equation is proposed in stereo matching. Th< disparity is obtained by using correlation calculation with pyramid structure and active contour model. The specific face is reconstructed successfully.A universal vision-processing platform based on DSP technology is developed The human's eye and neck vision processing task can be implemented with this platform. It is a new way for vision processing without personal computers. The systen design, the main processor and peripherals selection, modules and circuit design anc some experiments are introduced in detail.At last, two implementation examples using some achievements are introduced One is the object auto tracking integrated intelligent dome camera, the other is thu status monitoring and fault diagnosis pneumatic system based on vison technology.
程;机器视觉系统则是能够实现某些视觉功能的硬件和软件的综合。通过广大研
究人员的努力,机器视觉已取得了很大的进展,开始从实验室走向实际应用阶段。
但是客观地讲,机器视觉不论是在理论上,还是实际应用中,都存在着较多不足,
还处于不成熟的研究阶段,有待进一步的改进与深入研究。本论文以仿人眼颈系
统为研究对象,建立了仿人眼颈视觉系统的体系结构和实现平台,研究相关基础
理论、技术与应用。主要包括系统模型、复杂背景下单目运动视觉分析、主动目
标跟踪、通用的摄像机标定技术、双目立体视觉的人脸重建、DSP视觉处理与实
现技术等。
论文介绍了仿人眼颈系统、机器视觉系统理论及发展历程;综述了运动视觉
分析和立体视觉系统的研究概况,指出机器视觉相关研究存在的问题与发展趋
势,并给出论文的主要研究内容。
建立了仿人眼颈视觉系统的数学模型,包括仿人眼颈系统的运动学数学模型
和摄像机数学模型。在深入分析摄像机数学模型的基础上,提出了一种基于直线
校正的摄像机标定方法,并且在摄像机镜头畸变参数的求解方法上,提出了一种
线性的求解方法,与非线性最小二乘求解相比,避免了非线性优化存在的依赖于
初始值、易收敛于局部极小值等缺陷,而且精度与非线性求解算法相当。
对运动视觉分析进行了详细的研究。提出了一种复杂背景下运动目标的检测
算法,通过对背景的高斯建模,进行运动目标检测,考虑目标影子的存在及环境
光照等条件有可能变化,提出了相应的影子消除及背景自适应更新算法,大大提
高了目标检测的可靠性。
针对场景中存在多种可能的目标,提出基于支持向量机的人体目标识别方
法。针对不同场景下,运动目标受到遮挡的程度不同,提出了基于头肩模型和星
形向量表示法的人体目标特征向量抽取方法。通过样本采集、特征抽取、支持向
量机训练得到最终的人体目标识别分类器。实验结果表明,基于现代统计学习理
论的支持向量机非常适合于有限样本条件下的目标分类。
研究运动目标定位、跟踪原理和实现方法。提出将背景匹配与帧间差分技术
相结合的方法,解决摄像机运动-目标运动模式下的运动目标检测难题。为了使
运动目标的跟踪更为平稳,提出了利用卡尔曼预测器对目标的位置进行再估计,
并在此基础上,研究了单目视觉跟踪和双目视觉协调跟踪的控制方法。
对基于双目立体视觉的特定人脸重建进行了详细的理论分析和实验研究。建
立了双目立体视觉测距的数学模型,分析了基于双目立体视觉进行人脸重建的原
理,为三维重建提供理论基础。提出了金字塔结构相关计算方法和活动轮廓模型
相结合的视差抽取方法,并在活动轮廓模型中采用了一种新的能量最小化方程,
解决了立体视觉中立体匹配的难点,成功地实现了人脸的三维重建。
本文还研制成功一种通用的DSP视觉处理平台。利用它,可以完成仿人眼
颈视觉处理及其它一些图象处理,为脱离PC机实现图象处理提供一种途径。详
细介绍系统的整体设计,主处理器及外围元器件的选择,各子模块和电路的设计
及一些实际经验和体会。
最后,介绍了本文研究成果的两个相关应用实例——目标自动跟踪一体化智
Machine vision is a newly developed, composite and multi-disciplinary subject. It studies on the cognition science of vision information from the level of information processing. Machine vision system is a synthesis of hardware and software, which can perform certain vision functions. After researchers' several decades' effort, machine vision develops rapidly, and it goes into practical application from laboratory. But to be objectively speaking, machine vision is still in immature stage. Either theory or practical application is deficient. This thesis concentrates on the human's eye and neck vision simulation system. It constructs the framework of the simulation system, studies on the basic theory, technology and application. The primary work includes system model, monocular motion analysis under complex background, objects active tracking, versatile camera calibration technology, specific face reconstruction based stereovision and DSP technology based vision processing.In this thesis the human's eye and neck vision simulation system, machine vision theory system and development are introduced. The state-of-the-art of the motion vision analysis and stereovision system is summarized. The existing problem and development trend, which are related with machine vision, are presented.The math models, which include kinematics math model of the human's eye and neck system and math model of the camera, are established. With analysis of the math model of camera in detail, a new camera calibration method based on line rectification is proposed. A linear algorithm is provided to obtain the solution. Compared with non-linear methods, it does not require a good initial guess to guarantee convergence and it does not have the problem of converging to a local minimum. The accuracy is the same as that of non-linear methods.Motion vision analysis is studied in detail. A motion objects detection algorithm under complex background is proposed. Objects are detected through scene Gaussian modeling. A series of methods are proposed to weaken the influence, which is brought by the shadow of the objects and lighting condition variations.A human body recognition method based Support Vector Machine is proposed to distinguish the different possible objects. Feature abstracts based on head and shoulder model and center radiating vector representation are used in different application. Human objects recognition classifier is obtained through samples collecting, feature abstract and SVM training. Experiments show that SVM suits for objects classifying under the condition of limited training samples.It studies on motion objects position, principle of tracking and implement method. The math model of the human's eye and neck system is established. A background match combined with inter-frame differential method is used to solve the difficulty of objects detection under the situation of both camera and objects moving. A Kalman predictor is used to estimate the object's position in order to make object tracking more steadily. Monocular tracking and binocular tracking control methods are also studied.
Theory analysis and experiment are performed to human's specific face recon struction based binocular vision. The math model of binocular vision length meas urement is set up. The principle of the face reconstruction based on binocular vision i: introduced. A new energy minimization equation is proposed in stereo matching. Th< disparity is obtained by using correlation calculation with pyramid structure and active contour model. The specific face is reconstructed successfully.A universal vision-processing platform based on DSP technology is developed The human's eye and neck vision processing task can be implemented with this platform. It is a new way for vision processing without personal computers. The systen design, the main processor and peripherals selection, modules and circuit design anc some experiments are introduced in detail.At last, two implementation examples using some achievements are introduced One is the object auto tracking integrated intelligent dome camera, the other is thu status monitoring and fault diagnosis pneumatic system based on vison technology.
引文
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