南京理工大学
《Computer Vision》课程内容简介
课程编码 L106C007 课程类别 必修课
课程名称 Computer Vision
英文名称 Computer Vision
开课院系 计算机科学与工程学院
开课季节 秋学期 授课方式 面授讲课
考核方式 考试 课件地址
考试方式 闭卷 成绩计算方法 期末100%
课程总学时 32 课程学分 2
实验学时 适用对象
课程类型 理论课 课程属性 必修
任 课 教 师
教师姓名性别所属院系职称年龄
刘亚洲 计算机科学与工程学院 副教授 42

教学目标:
Introduce the student to the concepts of computer vision: what is meant, how it is done, and its current limitations. In-depth exercises will help the student to become familiar with the algorithms that are being used.

课程内容:
1) Camera Basics
a. cameras: still, motion, stereo
b. lenses: pinhole, simple, compound, panoramic
c. sensors: film, CCD, CMOS, image intensifiers
d. exposure control: mechanical & electronic shutters
e. analog-to-digital conversion, communication, computer video cards
2) Camera Geometry
a. homogeneous coordinates
b. transformations: translation, rotation, rigid, affine, and projective
c. intrinsic parameters and extrinsic parameters
d. calibration
3) Feature extraction
a. simple features, simple filters: edges, neighborhoods
b. many variations on the theme of edge detection
c. complex features: man-made (e.g., corners), nature-made (e.g., faces)
d. decomposition, Gabor filters, wavelets, scale-independence
e. transformations, compression, lossy-compression
4) Motion
a. motion tracking, optical flow
b. structure from motion
c. affine structure from motion
d. projective structure from motion
e. video compression
5) Binocular and multi-view stereo
a. recovery of scene depth from a stereo pair
b. the corresponding point problem
c. occlusions, windows, periodicities, other ambiguities
d. stereo from motion, holography, motion from stereo
e. stereo for people (teleoperation) vs. stereo for computers
6) Image understanding
a. segmentation of objects and regions
b. foreground-background, sky-ground, belonging-anomalous
c. people, vehicles, threats, targets
d. vision-based driving and navigation
e. scene enhancement for inspection and teleoperation

适用学生:
全日制硕士    非全日制硕士    留学硕士    进修硕士    硕博连读    本科直博    全日制博士    留学博士    进修博士    在职专硕    其他    

预修课程:
Preparatory Courses(预修课程):
Probability theory, mathematical statistics, linear algebra, matrix analysis, digital image processing

参考书目:
[1] Computer Vision: A Modern Approach, Forsyth & Ponce, ISBN: 0130851981
[2] Image Processing: Analysis and Machine Vision, Sonka, Hlavac, & Boyle, ISBN: 0-534-9539
[3] Computer Vision, Shapiro & Stockman, ISBN: 0130307963

备注: