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1、计算机视觉介绍Introduction to Computer Vision,邹丰美 联系：, 13459246202 资料下载: 2006-2-13,-系列讲座之1 计算机视觉的背景及几何基础简介,次讲座的题目/时间,计算机视觉的背景及几何基础 (2/13,第1周) 摄像机的几何标定 (3/6,第4周) 刚体运动姿态估计问题 (3/27,第7周) 姿态估计问题 (II)(或对应问题) (4/17,第10周) 应用 (5/8,第13周),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)
2、 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),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,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
3、 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.,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
4、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。” “,研究的内容,早期:低层(low-level)图像处理,如 image transformation, image restoration
5、, 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 dec
6、ision making by learning”.,“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 vie
7、wpoint 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”.,“This type of knowledge-based high-level reasoning is called the top-down (自上而下) (or go
8、al-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
9、what is known as the Gestalt psychology, which regards human perception as integration of the environment and experience. ”,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
10、one of the central themes of artificial intelligence (人工智能), and machine vision was regarded as problem solving by artificial intelligence.,“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 limit
11、less, 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 e
12、xponentially as the amount of knowledge increases.”,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 propag
13、ation (约束繁殖) and probabilistic relaxation (概率松弛).,“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,
14、such laws can provide clues to the 3-D shapes and positions of objects. ”,“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
15、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).”,“Although such analyses require approp
16、riate 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. ”,
