Course title
Introduction to Multimedia technology


Course description
For years, multimedia has been touching most aspects of Computer Science and Engineering fields and forms an important component of image processing, computer vision, computer network, real-time systems, retrieval, and so on. Therefore, comprehensive knowledge in multimedia is rapidly becoming a necessity, emphasizing the importance of this course. There are major parts covered in this class: image processing (in spatial & frequency domain), computer vision (e.g., recognition, 3D reconstruction, etc). As the outcomes of this course, students will be facilitated a strong knowledge foundation supporting them in doing research in numerous hot topics of multimedia technologies (e.g., image and video processing, Image rendering, etc.)

We will conduct in-person (face-to-face) class in Fall 2023. Those who are oversea and wish to take this class should contact the lecturer via email.
If the number of students is large enough, they will be divided into different group. Particular assignment is given to each group.
Purpose of class
The students are expected to learn fundamental knowledge of multimedia in conjunction with real applications
Goals and objectives

Goals and objectives Course Outcomes
1. The students can understand the fundamentals of image processing & computer vision
2. The students can understand the concepts in image processing & computer vision through various applications. They are also expected to develop applications as the assignments.
3. The students themselves can develop good skills in programming, teamworks, critical thinking, etc. They can also develop good attitude during studying, e.g., proactively discuss with classmates and lecturer, attend the class in time, submit the assignment in time, no cheating during the exam.
Class schedule

Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Introduction to image processing & computer vision Read section 1 and 2 in the textbook 1 380分
2. Spatial image processing (part 1) Read section 1 and 2 in the textbook 1 380分
3. Spatial image processing (part 2) Read section 3 in the textbook 1 380分
4. Frequency domain processing & compression Read section 4 in the textbook 1 380分
5. Photometry & color processing Read section 5 in the textbook 1 380分
6. Deep learning for computer vision Read section 6 in the textbook 1 380分
7. Mid-term examination Read section 7 and 8 in the textbook 1 380分
8. Introduction to features: features detection & matching Preparation for mid-term exam 380分
9. Matching and stitching Read section 10 in textbook 1 380分
10. Recognition Read section 11 in textbook 1 140分
11. Motion estimation Reading section 2 in textbook 2 380分
Investigate for external materials for camera calibration, stereo vision
12. Stereo & camera models Read the paper:
Adelson, E.H. and Bergen, J.R., 1991. The plenoptic function and the elements of early vision (Vol. 2). Vision and Modeling Group, Media Laboratory, Massachusetts Institute of Technology.
Self-investigation on 360 video and light field imaging, virtual reality, augmented reality
13. Depth estimation & 3D reconstruction Self-investigation on machine learning 120分
Read section12 in textbook 1 240分
14. Final exam and discussions on the solutions Preparation for final exam 380分
Total. - - 5300分
Relationship between 'Goals and Objectives' and 'Course Outcomes'

Mid-term Final Attitude during class Total.
1. 15% 20% 0% 35%
2. 15% 30% 0% 45%
3. 0% 0% 20% 20%
Total. 30% 50% 20% -
Evaluation method and criteria
Midterm exam (30%), Final Exam (50%) and Attitude During Class are the criteria of the grade. More than 60% of the total score is needed for getting the course credit.
Students are marked absent from the class if they are late regardless of the delay time.
If students are absent from more than one third of the total number of classes, the credit of this course cannot be given to them.
Even though students are absent from the class whatever the reason, e.g. sickness, delay of public transportation systems, forgetting to bring the student ID card, it is counted as absence.
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
The Others Feedbacks on exams, assignments, etc can be done either in class, via scomb or email.
Textbooks and reference materials
1. Digital Image processing 4e, Gonzalez Rafael 2017
2. Computer Vision: Algorithms and Applications 2nd Edition, Richard Szeliski, 2021
3. Multiple view geometry in Computer vision, 2nd edition, Richard Hartley, 2004
Linear Algebra (strictly required)
Programming skill (Python or C++ or Matlab)
Office hours and How to contact professors for questions
  • Weekdays: From 10:00 - 16:30 by email or face-to-face discussion at 11C05-b New Building, Toyosu Campus. Making an appointment beforehand is highly recommended.
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
Most classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
Education related SDGs:the Sustainable Development Goals
    Last modified : Sat Sep 09 07:16:43 JST 2023