陈涛
Image Processing and Machine Vision
(2021,Spring)
Introduction 丨Schedule 丨Extended
Notice: Welcome to the course of Image Processing and Machine Vision in the spring of 2021
Lecture by: Tao Chen
Office: C5029, Interdisciplinary Building Two, Jiangwan Campus, Fudan University
Tel: 021-65643036
Mail: eetchen@fudan.edu.cn
Teaching Assistant: Qi Fu，20210720095@fudan.edu.cn
Information: 4 credits/64 class hours，1-16 weeks
Classroom：H2210
- Monday 3–4（9:55–11:35）
- Thursday 6–7 (13:30–15:10)
Teaching objectives:
This course is to make the students understand the fundamentals of image processing and machine vision, and train them how to solve real-world problem using image processing tehniques. Further, students are expected to be able to implement basic image processing modules, including image transform, compression and feature extraction, etc. In the learning process, students will develop interests in image and vision research field, and be prepared for pursuing a higher degree.
Courswork：
•Attendence(10%)
•Assignments(30%)
•Quiz(Open-Book)(60%)
Notes：
1.Attendance and class performance: Students are assessed based on whether they are late for class, leave early, absent without reason, whether they prepare for class carefully, and whether they make loud noises in class. Students who are absent without reason for three times or who violate class rules will get 0 points.
2.Presentation of course assignments：
The assessment is mainly based on the students’ completion of major assignments and course reports. Specifically, the comprehensive assessment can be made according to the following situations:
The completion of the assignments；
Overall performance of course presentations；
Writing of course report .
3.NO COPY
Prerequisites：
• Basic knowledge of Digital Signal Processing
• C/Python/Matlab Basic Programming Skills
Reference books：
1.Gonzales,Woods,Digital image processing(The fourth edition),Electronic Industry Press,2020.
2. Carsten Steger,Machine Vision Algorithms and Applications,Tsinghua University Press,2019.
Image Processing and Maching Vision
(2021,Spring)
Introduction 丨Schedule 丨Extended
There are some significant lessons or resources that can help our students who want to learn more about computer vision or deep learning. If you are interested in image processing especially computer vision, you can have a great learning experience.
Machine Learning and having it Deep and Structured-Hongyi Li
CS231n：Convolutional Neural Networks for Visual Recognition-Stanford
复旦大学图像处理与机器视觉课程
（2021年春季）
课次 学时 主题 主要内容
课程ppt 作业 1 4 数字图像的采集方式 课程课程准备，数字图像的采集方式以及特性讲解 安装Matlab等准备课程工作 2 4 图像预处理 图像预处理方法如灰度化，去噪声 3 4 图像增强 包括基于直方图的增强，彩色图像增强等 4 4 图像变换 包括空域变换（几何变换）和频域变换（DFT、FFT、DCT） 作业1 5 4 图像压缩 包括有损压缩（哈夫曼编码）与无损压缩（预测编码）等 作业2 6 4 图像分割 包括阈值分割法与区域分割法等 作业3 7 4 图像分割 包括阈值分割法与区域分割法等 作业4 8 4 图像的特征提取 课程大作业part1 9 4 用深度学习提取图像特征 作业5 10 4 图像分类 作业6 11 4 语义分割 作业7 12 4 实例分割 作业8 13 4 课程大作业part2 14 4 课程大作业汇报 15 4 课程大作业汇报 16 4 答疑
Tips：
1.参考教材链接：数字图像处理（第三版）
2.所有课程ppt提取码为fdu0