计算机视觉介绍 Introduction to Computer Vision

1. 计算机视觉介绍Introduction to Computer Vision ----系列讲座之1 计算机视觉的背景及几何基础简介 邹丰美 联系：fmzou@xmu.edu.cn, 13459246202 资料下载: http://teach.xmu.edu.cn 2006-2-13

2. ５次讲座的题目/时间 １．计算机视觉的背景及几何基础 (2/13,第1周) ２．摄像机的几何标定 (3/6,第4周) ３．刚体运动姿态估计问题 (3/27,第7周) ４．姿态估计问题 (II)(或对应问题) (4/17,第10周) ５．应用 (5/8,第13周)

3. 要求 • 听5 次讲座并积极提问,共同讨论(每次有约15-20分钟的提问及讨论时间) • 至少完成3个实验中的一个(程序+报告) • (上机地点头两周内定，到时候我通知) • 完成一篇(与实验相关的) “学术”论文 • 最终成绩计算: • 本科生: 60%(实验) + 40%(文章) • 研究生: 40%(实验) + 60%(文章)

4. 纲要 • 什么是CV? • 什么是CV? 它是从什么时候发展起来的? • 它有哪些研究内容? • 它与哪些学科/领域相关? • CV的若干问题及应用展望 • 几何基础／概率基础 • 一些相关资源

5. Definitions of CV (1) • “Today, the study of extracting 3-D information from video images and building a 3-D model of the scene, called computer vision or image understanding, is one of the research areas that attract the most attention all over the world.” – from K. Kanatani, “Statistical Optimization for Geometric Computation: Theory and Practics”, 1996.

6. CV的定义 (2) • “视觉，不仅指对光信号的感受，它还包括了对视觉信息的获取、传输、处理、存储与理解的全过程．信号处理理论与计算机出现以后，人们试图用摄像机获取环境图像并将其转换成数字信号，用计算机实现对视觉信息处理的全过程，这样，就形成了一门新兴的学科－－计算机视觉”．“计算机视觉的研究目标是使计算机具有通过二维图像认知三维环境信息的能力．” －“计算机视觉－计算理论与算法基础”, 马颂德, 张正友, 1998. • “计算机视觉是当前计算机科学研究的一个非常活跃的领域，该学科旨在为计算机和机器人开发出具有与人类水平相当的视觉能力。各国学者对于计算机视觉的研究始于20世纪60年代初，但相关基础研究的大部分重要进展则是在80年代以后取得的。”–“http://research.microsoft.com/asia/news/displayArticle.aspx?id=1332”

7. 研究的内容 • 早期:低层(low-level)图像处理,如 image transformation, image restoration, image enhancement, thresholding, region labelling, and shape characterization. • “Tried to identify and classify objects in images by techniques of Pattern Recognition (模式识别), which had been developed for the purpose of recognizing 2-D characters and symbols by feature extraction and statistical decision making by learning”.

8. “Many pattern recognition researchers believed that the paradigm of pattern recognition would also lead to intelligent vision systems that could understand 3-D scenes”. • “However, they soon realized the crucial fact that 3-D objects look very different from viewpoint to viewpoint beyond the capability of 2-D feature-based learning; 3-D meanings of 2-D images cannot be understood unless some a prior knowledge about the scene is given. Thus, ‘Knowledge’ came to play an essential role”.

9. “This type of knowledge-based ‘high-level’ reasoning is called the top-down (自上而下)(or goal-driven (目标驱动)) approach.” • “In a sense, this approach corresponds to the psychological view toward human perception(感知) that humans understand the environment by unconsciously ‘matching’ the vast amount of knowledge accumulated from experience in the process of growth.” • “This view can be compared to what is known as the Gestalt psychology, which regards human perception as integration of the environment and experience. ”

10. Thus, the problem of how to represent and organize such knowledge became a major concern, and many symbolic schemes were derived. Establishing such symbolic representations is one of the central themes of artificial intelligence (人工智能), and machine vision was regarded as problem solving by artificial intelligence.

11. “However, the inherent difficulty of this approach was soon realized: the amount of necessary knowledge, most of which has the form of “if … then … else …”, is limitless, heavily depending on the domain of each application (“office scene”, “outdoor scene”, etc) and constantly changing (e.g., today, many telephones are no longer black and do not have dials). However large the amount of knowledge is, exceptions are bound to appear, and computation time blows up exponentially as the amount of knowledge increases.”

12. Many combinatorial techniques were proposed so as to find plausible interpretation efficiently without doing exhaustive search. Such techniques include various types of heuristic (启发式的) search as well as special techniques such as constraint propagation (约束繁殖) and probabilistic relaxation (概率松弛).

13. “Realizing that such computational problems are inevitable as long as knowledge is directly matched with features extracted from raw images, researchers began to pay attention to “physical/optical laws” governing 3-D scenes. In analyzing 2-D images, such laws can provide clues to the 3-D shapes and positions of objects. ”

14. “For example, the surface gradients of objects can be estimated by analyzing shading intensities (shape from shading). The orientation of a surface in the scene can also be estimated by analyzing the perspective distortion of a texture on it (shape from texture). If objects are moving in the scene (or the camera is moving relative to the objects), the 3-D shapes of the objects and their 3-D motions (or the camera motion) can be computed (shape from motion or structure from motion).”

15. “Although such analyses require appropriate assumptions about surface reflectance, illumination, perspective distortion, and rigid motion, they do not depend on specific application domains; they are called constraints in contrast to ‘knowledge’ for the top-down approach. • This approach is in line with the psychological view toward human vision that human perception occurs automatically when visual signals trigger ‘computation’ in the brain and that this computational functionality is innate, acquired in the process of evolution.”

16. This view was asserted by J. J. Gibson, who had a great influence on not only psychologists but also machine vision researchers. • Thus, a new paradigm (范例) was established. First, primitive features are extracted from raw images by edge detection and image segmentation, resulting in primal sketches; next, approximate shapes and surface orientations are estimated by applying available constraints (shading, texture, motion, stereo, etc.), resulting in 2.5-D sketches;

17. then, appropriate 3-D models (e.g., generalized cylinders) are fitted to such data, resulting in a numerical and symbolic representation of the scene; finally, high-level inference is made from such representations. This is called the bottom-up (自下向上) (or data-driven (数据驱动)) approach, which is also known as the Marr paradigm after David Marr, who strongly endorsed this approach.

18. Marr的计算视觉理论框架 • Marr从信息处理系统的角度出发，认为视觉系统的研究应分为三个层次，即计算理论层次、表达(representation)与算法层次、硬件实现层次． • 计算理论层次要回答系统各部分的计算目的与计算策略，亦即各部分的输入输出是什么，之间的关系是什么变换或什么约束． • 表达与算法层次应给出各部分的输入输出和内部的信息表达，以及实现计算理论所规定的目标的算法. • 硬件实现层次要回答“如何用硬件实现以上算法”．

19. A major drawback of this approach is its susceptibility to noise. Computation solely based on physical/optical constraints is likely to produce meaningless interpretations in the presence of noise. This is because 3-D reconstruction from 2-D data is a typical inverse problem(逆问题), for which solutions are known to be generally unstable with respect to noise.

20. “In order to cope with this inherent ill-posedness, many optimization techniques were devised so as to force the solution to have required properties. Such techniques are generally called regularization. Other types of optimization include a stochastic relaxation technique called simulated annealing(模拟退火), which was constructed by analogy with statistical mechanics, and the use of neural networks, which gave rise to a new view toward human cognition called connectionism. ”

21. “Today, many attempts are being made to enhance the reliability of image data. One approach is to actively control the motion of the camera so that the resulting 3-D interpretation becomes stable (active vision). Another approach is using multiple sensors (stereo, range sensing, etc.) and fusing the data (sensor fusion).”

22. “In order to fuse data, the reliability of individual data must be evaluated in quantitative terms so that reliable data contribute more than unreliable data.” • “Some researchers are attempting to use only minimum information that is enough to achieve a specific goal such as object avoidance (qualitative vision, purposive vision, etc.). ” • --- for detailed information, read “intro_KKanatani.doc”

23. 相关领域 • 数学，物理学 • 脑科学(或神经生理学) • 心理学，认知科学, AI, … • “计算机视觉发展得益于神经生理学、心理学与认知科学对动物视觉系统的研究，但计算机视觉已发展起一套独立的计算理论与算法，它并不刻意去‘仿真’生物视觉系统”．

24. 相关学科与相关课程的联系 计算机图形学 重叠量反应相关程度 集合论 计算几何 机器视觉 线性代数 计算机视觉 高等代数 数字图象处理 最优化方法 模式识别 。。。。。。。。。。。。 计算机视觉专题(如图象与视觉计算) 高级语言程序设计 数据结构 信号与系统 基础知识 先后顺序

25. Overview (1) • 计算机视觉的几何学基础 • 摄像机模型 • 单摄像机(pinhole model/perspective transformation) • 双摄像机 (epipolar geometry: fundamental/essential matrix) • 三摄像机及更多(multi-view geometry) • 运动估计 • 对应点问题（correspondence problem） • 光流计算方法 • 刚体运动参数估计（minimal projective reconstruction） • 2-view, 7 points in correspondence; (Faugeras) • 3-view, 6 points in correspondence; (Quan Long) • 3-view, 8 points with one missing in one of the three view. (Quan Long) • 几何重构（Geometry reconstruction） • 立体视觉(stereo vision) • Shape from X (shading/motion/texture/contour/focus/de-focus/….)

26. Overview (2) • 计算机视觉的物理学基础 • 摄像机及其成像过程 • 视点、光源、空间中光线、表面处的光线…. • 明暗 (shading)、阴影 (shadow) • 光学/色彩 (light/color) • 辐射学(radiometry)，辐照率, …, • 物体表面特性 • 漫反射表面（各向同性）Lambertian surface • BDRF (bi-directional reflectance distribution function)

27. Overview (3) • 计算机视觉的图像模型基础 • 摄像机模型及其校准 • 内参数、外参数 • 图像特征 • 边缘、角点、轮廓、纹理、形状… • 图像序列特征 (运动) • 对应点、光流

28. Overview (4) 计算机视觉的信号处理层次 • 低层视觉处理 • 单图像：滤波/边缘检测/纹理 • 多图像：几何/立体/从运动恢复仿射或透视结构 (affine/perspective structure from motion) • 中层视觉处理 • 聚类分割/拟合线条、曲线、轮廓 clustering for segmentation, fitting line… • 基于概率方法的聚类分割/拟合 • 跟踪 tracking • 高层视觉处理 • 匹配 • 模式分类/关联模型识别 pattern classification/aspect graph recognition • 应用 • 距离数据（range data）/图像数据检索/基于图像的绘制

29. Overview (5) 计算机视觉的数学基础 • 射影／仿射几何、微分几何 • 概率统计与随机过程 • 数值计算与优化方法 • 机器学习 计算机视觉的基本的分析工具和数学模型 • Signal processing approach: FFT, filtering, wavelets, … • Subspace approach:PCA, LDA, ICA, … • Bayesian inference approach: EM,Condensation/sequential importance sampling (SIS) …, Markov chain Monte Carlo (MCMC) , …. • Machine learning approach: SVM/Kernel machine, Boosting/Adaboost, k-NN/Regression, … • HMM, BN/DBN (Dynamic Bayesian Network), … • Gibbs, MRF, … • …

30. Overview (6) 计算机视觉问题的特点 • 高维数据的本质维数很低，使得模型化成为可能。 High dimensional image/video data lie in a very low dimensional manifold. • 解的不唯一性 缺少约束的逆问题 • 优化问题

31. CV的若干问题及应用展望 • 基本视觉系统如下： 特征检测 Shape from X 识别 物体描述 图像 低层特征 位置与形状 • 涉及模块与系统的研究存在的问题与出现的一些新 • 思路，如“视觉信息处理系统的‘任务’”， “关于模块化 • 问题” ， “局部特征与全局特征” ， “物体建模” ，等等． • 三维计算机视觉将会有极广泛的应用前景, 如: • 计算机人－机交互；多媒体技术，数据库与图像通信； • 生产自动化；医学；自动导航；三维场景建模与可视化．

32. 纲要 • 什么是CV? • 什么是CV? 它是从什么时候发展起来的? • 它有哪些研究内容? • 它与哪些学科/领域相关? • CV的若干问题及应用展望 • 几何基础／概率基础 • 一些相关资源

33. 射影几何知识简介 • 欧氏几何：旋转和平移都是欧氏变换．研究在欧氏变换下保持不变的性质（欧氏性质）的几何是欧氏几何．如平行性，长度，角度等都是欧氏性质． • 射影几何：照相机的成像过程是一个射影（透视或中心射影）的过程．它不保持欧氏性质，如平行线不再平行．研究射影空间中在射影变换下保持不变的性质（射影性质）的几何学是射影几何．

36. 无穷远元素 • 平行线交于一个无穷远点； • 平行平面交于一条无穷远直线； • 在一条直线上只有唯一一个无穷远点； • 所有的一组平行线共有一个无穷远点． • 在一个平面上，所有的无穷远点组成一条直线，称为这个平面的无穷远直线．

38. ３维空间中所有的无穷远点组成一个平面， 称为这个空间的无穷远平面．

39. 射影空间 • 对n维欧氏空间加入无穷远元素，并对有限元素和无穷远元素不加区分，则它们共同构成了n维射影空间. • 1维射影空间是一条射影直线，它由欧氏直线和它的无穷远点组成； • 2维射影空间是一个射影平面，它由欧氏平面和它的无穷远直线组成； • 3维射影空间是由3维欧氏空间加上无穷远平面组成．

40. 齐次坐标 • 在欧氏空间中建立坐标系以后，点与坐标有了一一对应，但当引入无穷远点以后，无穷远点没有坐标，为了刻划无穷远点的坐标，可以引入齐次坐标． • 在n维欧氏空间中，建立直角坐标以后，每个点的坐标为(m1, …, mn)，对任意n+1个数x1, …, xn, x0，如果满足 x00, xi/x0 = mi, (i = 1…n) 则称(x1, …, xn, x0)为该点的齐次坐标．而(m1, …, mn)被称为非齐次坐标．

41. 不全为0的数x1, …, xn组成的坐标 (x1, …, xn, 0) 被称为无穷远点的齐次坐标． • 例 设在欧氏直线上的普通点的坐标为x，则适合x1/ x0 = x 的任意两个数组成的坐标(x1, x0)为该点的齐次坐标，而x为该点的非齐次坐标．对任意x10，则(x1, 0)是无穷远点的齐次坐标．

44. 射影参数

45. 交比

46. 射影变换

49. 射影平面中的对偶 • “点”与“直线”叫做射影平面上的对偶元素． • “过一点作一直线”与“在直线上取一点”叫做对偶作图． • 在射影平面设有点，直线及其相互结合和顺序关系所组成的一个命题，将此命题中的各元素改为它的对偶元，各作图改为它的对偶作图，其结果形成另一个命题，这两个命题称为平面对偶命题． • 对偶原则：在射影平面中，若一个命题成立，则其对偶命题也成立．