课程名称 (Course Name) ： Machine Vision Based Measurement

课程代码 (Course Code）： IN26010

学分/学时 (Credits/Credit Hours)： 3/48

开课时间 (Course Term )：Autumn

开课学院（School Providing the Course）: SEIEE

任课教师（Teacher）:  Manhua Liu, Wei Tao

课程讨论时数（Course Discussion Hours）:  6

课程实验数（Lab Hours）:   4

课程内容简介(Course Introduction):

This course is a specialized course for Instrument Science and Technology major. It can also be used in other majors. The aim of this course is to help students understand the basic concepts, basic principles, and the commonly used technologies and methods on machine vision based measurement, so that the students can apply these technologies to solve practical problems. In addition, this course will also introduce the latest development and progresses on machine vision and lay the foundations for future work. By this course, students can master the basic concepts and methods from the low-level visual information sensing and processing, such as image acquisition, image registration and matching, image processing and segmentation, image description, pattern recognition and machine vision related, to understand the latest development and trend in the field of machine vision. It promotes students understand and solve the machine vision problem, and improve students' ability to find problems, analyze problem, and solve the problem. The objective of this course is to teach students to understand and grasp the principles and applications of machine vision based measurement technologies.

教学大纲(Course Teaching Outline):

Chapter 1: Introduction (2 hours)

  Motivation i.e., why is Machine vision important and Difficult? The system of machine vision based measurement, its main contents, and various applications and research progresses etc..

Chapter 2: Image acquisition (6 hours)

  Imaging sensors, optical system, image formation, image digitalization and sampling etc..

Chapter 3: The calibration of vision inspection system and stereo imaging (4 hours)

  The calibration methods of vision inspection system and coordinates transformation, the concepts of stereo imaging

Chapter 4: Image calibration and registration (4 hours)

image translation and rotation, image alignment, image attitude conversion, etc..

Chapter 5: Image Preprocessing (4 hours)

  Image denoising, filtering, enhancement and restoration etc., including linear filtering and nonlinear filtering.

Chapter 6: Image Segmentation (4 hours)

  Image gradients, various edge detection algorithms, Log methods, region based methods, etc..

Chapter 7: Feature Extraction and Detection (6 hours)

  Point detection, line detection, color feature extraction, invariant feature extraction and texture feature detection etc.

Chapter 8: Image Matching and Object Recognition (4 hours)

  Image similarity, image correlation, image classification such as SVM classifier, and object recognition.

Chapter 9: Applications (4 hours)

  Present several practical machine vision applications such as fingerprint recognition, face detection and recognition etc.

课程进度计划(Course Schedule):

Week 1: Introduction & image formation

Week 2: Image acquisition

Week 3: The calibration of vision inspection system and stereo imaging

Week 4: Image calibration and registration

Week 5: Image Preprocessing

Week 6: Image Segmentation

Week 7: Feature Extraction and Detection

Week 8: Feature Extraction and Detection, Image Matching and Object Recognition

Week 9: Image Matching and Object Recognition

Week 10: Applications & Discussions

Week 11: Discussions

课程考核要求(Course Assessment Requirements)：

Assignments: reading some papers and materials, and doing the assigned experiments.

The evaluation of this courses is based on the performances of attendance, assignments and final project reports, which are 20% attendance, 30% assignments, and 50% final project reports, respectively.

参考文献(Course References)：

1. Richard Szeliski，Computer Vision: Algorithms and Applications，2010，Springer， ISBN 978-1-84882-935-0

2. Milan Sonka, Vaclav Hiavac, Roger Boyle; Image Processing, Analysis, and Machine Vision; Thomson Asia Pte Led, United States of America;

3. R.C. Gonzalez and R.E. Woods, Digital Image Processing, 2^nd  Edition, Prentice-Hall, 2007.

4.    R. O. Duda, P. E. Hart and D. Stork, Pattern Classification (2nd. Edition) Wiley 2002. 

预修课程（Prerequisite Course）

Digital Signal Processing, Linear algebra, Vector calculus, A good working knowledge of C, C++, MATLAB programming