Course Overview and Objectives:

This course aims to provide students with a deep understanding of the fundamental concepts and techniques of computer vision, including image formation, feature extraction, 3D reconstruction, image segmentation, object recognition, deep learning, object detection, object tracking, and facial recognition. It also aims to equip students with the implementation and application of algorithms, models, and frameworks related to computer vision, enabling them to address computer vision problems across various domains such as autonomous driving, smart homes, medical image analysis, and more. Through this course, students will comprehend the limitations and challenges of current computer vision applications and explore future development directions. By undertaking this course, students will acquire the following abilities:

• Fundamental knowledge and skills in computer vision and image processing.
• Sensitivity and analytical skills toward emerging technologies and trends.
• Capability to conduct independent research and development, possessing teamwork and project management abilities.
• Innovative thinking and problem-solving skills, with the capacity to apply learned knowledge to promote technological innovation and societal progress.

Prerequisites

• Proficiency in Python
  • All class assignments will be in Python. If you have a lot of programming experience but in a different language (e.g. C++/Matlab/Javascript) you will probably be fine.
• College Calculus, Linear Algebra
  • You should be comfortable taking derivatives and understanding matrix vector operations and notation.
• Basic Probability and Statistics
  • You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.

Grading

• Assignments (40%): Including programming assignments, literature review reports, etc.
• Midterm Report (20%): Students are required to select a computer vision-related paper from the past three years and write a research report. Additional points will be awarded if the report includes a demo and technical implementation.
• Final Project (40%): Students must choose a computer vision-related topic and produce both a research report and a practical implementation project. The grading criteria include a clear understanding of the problem, the innovation and practicality of the solution, and the completeness and effectiveness of the technical implementation.
• Class Participation (10%): -1 each absent.

Office Hours

• Monday 11:00-12:00 am
• Room: Engineering Building-6 (374)

Progress

Resource

• Computer Vision
• Deep Learning

Textbook

• Programming Computer Vision with Python
• Computer Vision: Algorithms and Applications