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This course aims to provide students with an in-depth understanding of the fundamental concepts and techniques in computer vision, including image formation, image feature extraction, 3D reconstruction, image segmentation, object recognition, deep learning, object detection, object tracking, and face recognition. Students will also gain proficiency in implementing and applying algorithms, models, and frameworks related to computer vision, equipping them to solve computer vision problems in various fields such as autonomous driving, smart homes, and medical image analysis. Through this course, students will understand the limitations and challenges of existing computer vision applications and explore future development directions. By the end of the course, students will have developed the following abilities: 1. Students will possess basic knowledge and technical skills in computer vision and image processing. 2. Students will have sensitivity to emerging technologies and trends, along with strong analytical abilities. 3. Students will be capable of conducting independent research and development work, with competencies in teamwork and project management. 4. Students will have innovative thinking and problem-solving abilities, applying what they have learned to promote technological innovation and social progress.

Linear Algebra, Calculus, Probability and Statistics, Python

(1) Lectures on Theories and Principles: Instructional sessions covering the fundamental theories and principles of computer vision. (2) Practical Sessions and Case Analyses: Hands-on activities and analysis of real-world cases to apply theoretical knowledge. (3) Student Presentations and Discussions: Opportunities for students to present their work and engage in discussions to enhance understanding and critical thinking.

(1) Assignments (40%): Including programming assignments, literature review reports, etc. (2) 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. (3) Final Project (30%): 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. (4) Attendance/Class Participation (10%): Graded based on students' attendance, level of participation in class, and interaction performance.

1. Programming Computer Vision with Python, Jan Erik Solem, O'REILLY Media, June 2012. (ISBN: 9781449316549). 2. Computer Vision: Algorithms and Applications, Richard Szeliski, ebook (https://szeliski.org/Book).

地點

Room 374, Engineering Building 6.

時間

Mon. 11:00~12:00.

聯絡方式

yfyangd@nycu.edu.com