TEACHING ARCHIVESTIEI
Course Description (for 2014)
Course Description
Tianjin International Engineering Institute
Course Name(Chinese):机器视觉
(English):Computer Vision
Course Name: Computer Vision
Course Code:
Semester: 4
Credit:
Programme: Electronic
Course Module: Specialized Subjects
Responsible: Liu li
E-mail: liuli@tju.edu.cn
Wang Jian
wangjian@tju.edu.cn
Department:School of Electronic Information Enginnering
Time Layout (1 credit hour = 45 minutes)
Practice
Lecture
Lab-study
4
12
16
Project
Internship(days)
Personal Work
20
Course Resume
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Image formation: camera model, camera calibration, radiometry, color, shading
Early vision: Stereo, structure from motion, illumination, reflectance,
Mid-level vision: feature detection and extraction
High-level vision: object detection, object recognition, visual tracking
Pre-requirements
Signal Processing; Machine Learning
Course Objectives
The aim of this course is to compute properties of the three-dimensional world from digital
images. Problems in this field include identifying the 3D shape of an environment, determining
how things are moving, and recognizing familiar people and objects, all through analysis of
images and video. This course provides an introduction to computer vision, including such topics
as image filtering, feature detection, object detection and object tracking.
After learning this course, students will be able to earn knowledge and understanding about
theories, concepts and application of computer vision, and skills of Matlab software by
demonstration, and practical views of current and future application domains.
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TEACHING ARCHIVESTIEI
Course Description (for 2014)
Course Syllabus
1. Introduction to computer vision
2 .Image Formation and Filtering
2.1 Cameras and optics
2.2 Light and color
2.3 Image filtering
3 .Feature Detection and Matching
3.1 Interest points and corners
3.2 Local image features
3.3 Feature matching and hough transform
3.4 Model fitting and RANSAC
4 .Stereo and Motion
4.1 Stereo
4.2 structure from motion,
4.3 Feature Tracking
4.4 Optical Flow
5 .Object recognition
5.1 Face detection
5.2 Face recognition
Text Book & References
Required: E.R Davies, Computer and Machine Vision - Theory, Algorithm and Practicalities (4th
Edition)
We may also use readings from a few reference books:
David. A. Forsyth, and Jean Ponce, Computer Vision A Modern Approach (2ed Edition)
Richard Szeliski, Computer Vision: Algorithms and Applications
Capability Tasks
CT1
CT2
CT3
CT4
CT10
Achievements
To be able to use Matlabor OpenCVto do image processing and video processing - Level: M
To be able to do feature detection, object detection and object tracking - Level: M
Students: Electronic year 4
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