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Jingdong book

基于序列图像的视觉检测理论与方法

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基于序列图像的视觉检测理论与方法

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本书共分八章:第1章简要回顾了摄影测量及计算机视觉的发展历程与应用现状;第2章介绍了工业零件视觉检测所涉及的数码相机标定、三维重建与视觉检测等领域的研究与发展现状;第3章阐述了相机的线性与非线性模型、多视几何的性质及其计算方法;第4章讨论了常用的点特征和线特征提取方法,并着重论述了基于最小二乘模板匹配的点、线特征高精度提取方法;第5章全面细致地分析了基于同形矩阵和二维直接线性变换并结合自检校光束法平差的非量测数码相机高精度标定理论和算法,并利用模拟和实际数据进行了试验分析;第6章重点阐述了附加刚体变换的点、线摄影测量模型,提出利用点线混合摄影测量技术重建钣金零件的精确三维模型,并讨论了粗差剔除、方差分量估计、病态矩阵无偏解算等相关问题;第7章详细讨论了基于广义点摄影测量的空间直线段、圆(含圆角矩形)及数学曲线等复杂形状的三维重建理论与方法;第8章介绍了序列图像在工业零件三维重建与视觉检测中的应用试验,并就精度和效率等方面的问题进行了深入分析。
Content Description

《基于序列图像的视觉检测理论与方法》系统地总结和阐述了基于序列图像的工业零件视觉检测理论、方法及其应用,简要回顾了摄影测量与计算机视觉的发展;分析了序列图像几何处理中所涉及的若干关键技术及国内外研究现状;介绍了相机的线性和非线性模型,讨论了基本矩阵和三视张量的几何意义及其计算方法;阐述了基于最小二乘模板匹配的影像特征点和直线段的高精度提取算法;提出基于二维直接线性变换和平面控制场的非量测数码相机标定理论和算法;重点阐述了附加刚体变换的点、线摄影测量模型,提出利用点线混合摄影测量技术重建钣金零件的精确三维模型;详细讨论了基于广义点摄影测量的空间目标三维重建理论与方法;最后介绍了序列图像在工业零件三维重建与视觉检测中的具体应用,并就检测精度和效率等方面的问题进行了深入分析。
Author Description

张永军,武汉大学教授,博士生导师。1975年3月出生于内蒙古。1997年本科毕业于原武汉测绘科技大学,2002年获武汉大学工学博士学位。2003年曾在德国汉诺威大学进行客座研究,2004年破格晋升为副教授,2006年破格晋升为教授。2002年获“王之卓创新人才奖”一等奖,2003获“湖北省第五届优秀博士学位论文”奖,2006年获“湖北省优秀自然科学论文”二等奖,2007年入选教育部“新世纪优秀人才支持计划”,2008年获“湖北省自然科学三等奖”。主要从事数字摄影测量与遥感、计算机视觉等方面的教学和研究工作,在多源影像数据联合区域网平差、卫星遥感影像的几何处理与三维信息提取、低空摄影测量、数码城市三维建模、工业零件高精度视觉检测等方面取得了较好成果。独立研制的GPS/IMU辅助光束法区域网平差系统达到国际著名同类软件相同水平。
主持和参加国家863计划、国家自然科学基金、国家科技支撑计划及国家973计划等多项国家级科研项目。以第一作者公开发表学术论文30余篇,其中SCI/EI/ISTP等收录20余篇。国家863计划和国家自然科学基金通讯评审专家,IEEE TGRS、IJGIS及CVIU等国际著名期刊及《测绘学报》、《武汉大学学,报(信息科学版)》、《遥感学报》等国内核心期刊通讯评审人。
Catalogue

第1章 绪论
1.1 摄影测量的发展与现状
1.2 计算机立体视觉的发展与现状
1.3 研究目标
1.4 本书的主要内容

第2章 相机标定、三维重建与视觉检测研究进展
2.1 数码相机标定研究进展
2.2 三维重建研究概述
2.3 视觉检测研究与应用

第3章 相机模型与多视几何
3.1 线性相机模型
3.2 非线性相机模型
3.3 双视几何
3.3.1 极线几何概述
3.3.2 基本矩阵的计算
3.3.3 基本矩阵的性质
3.4 三视几何
3.4.1 三视张量概述
3.4.2 利用一般投影矩阵计算三视张量
3.4.3 三视张量的性质

第4章 影像特征的自动提取
4.1 边缘检测概述
4.2 点特征提取算子
4.2.1 Moravec算子
4.2.2 Forstner算子
4.2.3 Harris算子
4.3 线特征提取算子
4.3.1 基于一阶微分的边缘检测算子
4.3.2 基于二阶微分的边缘检测算子
4.3.3 高斯一拉普拉斯算子(LOG算子)
4.3.4 Canny算子
4.3.5 Hough变换
4.4 基于模板匹配的格网点提取
4.5 基于模板匹配的直线段提取

第5章 基于平面控制场的数码相机标定
5.1 数码相机标定的基本流程
5.2 利用同形矩阵分解数码相机参数初值
5.2.1 同形矩阵的计算
5.2.2 内方位元素的求解
5.2.3 外方位元素的求解
5.2.4 外方位元素的变换
5.3 利用二维DLT分解数码相机参数初值
5.3.1 二维DLT及内方位元素初值的求解
5.3.2 外方位元素初值的求解
5.4 光束法平差的数学模型
5.5 数码相机标定的几个相关问题
5.5.1 格网纯绕z轴旋转时主点无法计算的证明
5.5.2 利用基本矩阵剔除粗差格网点
5.5.3 光束法平差的快速算法
5.6 数码相机标定试验与结果分析
5.6.1 同形矩阵数码相机标定算法试验
5.6.2 二维DLT数码相机标定算法试验

第6章 点线混合摄影测量与视觉检测
6.1 点、线混合摄影测量基本流程
6.2 附加刚体变换的点摄影测量模型
6.2.1 物体坐标系与世界坐标系间的旋转平移变换
6.2.2 系统参数较大时的数学模型
6.2.3 系统参数较小时的数学模型
6.3 附加刚体变换的直线摄影测量模型
6.3.1 误差方程式的基本形式
6.3.2 基于距离的误差方程式改化形式
6.3.3 直线摄影测量按间接平差的数学模型
6.4 点、线混合摄影测量的相关问题
6.4.1 外方位元素初值的传递
6.4.2 利用三视张量剔除粗差直线段
6.4.3 方差分量估计
6.4.4 系统参数的统计检验
6.4.5 病态矩阵的无偏解算方法
6.4.6 图形条件较差像片的自动剔除

第7章 复杂形状的三维重建
7.1 基于物方直接解的圆匹配与重建
7.1.1 旋转角初值的解算
7.1.2 物方直接解的数学模型
7.2 基于广义点摄影测量的复杂形状三维重建
7.2.1 广义点摄影测量的数学模型
7.2.2 直线段的三维重建
7.2.3 圆(圆弧)的三维重建
7.2.4 圆角矩形的三维重建
7.2.5 数学曲线的三维重建

第8章 工业钣金件视觉检测系统试验
8.1 视觉检测系统的基本功能
8.2 数码相机标定试验
8.3 线框模型的视觉检测试验
8.4 复杂形状的视觉检测试验
参考文献
Book Abstract

第5章 基于单面控制场的数码相机标定
检查和校准相机(或摄像机)的内方位元素和光学畸变参数的过程称为标定,对于广泛使用的非量测数码相机(或摄像机)来说,标定是从二维图像获取三维信息必不可少的步骤。计算机视觉界提出的基于主动视觉的相机自标定算法,在无法布设控制场的情况下有独特的作用,不过大多需要进行比较耗时的非线性最优化或者需要部分已知相机的运动参数,而且少数情况下,自标定技术得到的解不唯一。自标定技术的另一个缺点是其精度水平无法与传统的相机标定方法尤其是光束法平差相比。
本章主要讨论两种基于平面控制场的非量测数码相机(包括数字摄像机)标定方法及其数据试验结果。5.1节给出数码相机标定的基本流程。5.2节讨论基于同形矩阵的相机标定模型的改进,以适应取用不同内方位元素作为未知数时相机的高精度标定。5.3节将重点讨潞利用二维DLT参数分解相机内外方位元素初值的实用算法。5,4节将讨论利用自检校光束法串差进行相机标定的数学模型,此时相机参数的初值可以直接使用5.2节或5.3节分解的相机参数。5.5节将论述几个与相机标定相关的实际问题,包括纯绕Z轴旋转时(临界运动序列)主点无法计算的证明;粗差点观测值的剔除及光束法平差的快速算法等。5.6节将给出基于同形矩阵和光束法平差的标定方法应用实际数据的标定结果,并与基于二维DLT和光束法平差的标定结果进行比较,同时将利用模拟数据和实际数据对基于二维DLT的相机标定方法进行大量试验,以期验证算法的正确性并评价其精度水平。
Introduction

序列图像一般是指利用一台传感器采集被测物的一组随时间(空间)变化的图像,相应的摄站外方位元素一般是时间(空间)的函数。序列图像具有重叠度高、同名特征的冗余观测值多等突出优点,在工业自动化、飞行目标跟踪、生物医学影像分析及数字摄影测量处理等方面具有广泛的应用前景。随着制造加工工艺的不断进步,现代制造业对质量检测手段的要求也越来越高,质量检测的自动化程度、检测速度和精度是决定产品质量和生产效率的重要因素。利用序列图像进行高精度三维重建及视觉检测是当前计算机视觉和数字摄影测量等领域的重要研究方向,可以在零件识别与定位、三维量测、产品质量控制等领域发挥重要作用。基于序列图像的工业零件视觉检测主要涉及传感器标定、影像特征的高精度提取、三维几何模型的精确重建及自动化误差检测与质量控制等关键技术,国内外众多学者都在从事相关的研究工作,并取得了丰硕的成果。
CAD技术的迅猛发展为建立物体的几何模型提供了基础,充分利用工业零件的CAD设计数据作为参考来评价产品的几何精度是提高质量检测自动化程度的可行途径。本书充分利用序列图像的优点,结合工业零件的CAD设计数据,提出采用平面控制场进行非量测数码相机的高精度标定;并利用图像中提取的点、线观测值进行混合区域网平差,对工业钣金件进行高精度三维重建以检测其尺寸制造误差或使用中所产生的尺寸变形。

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