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.
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.
The students are expected to learn fundamental knowledge of multimedia in conjunction with real applications
| Goals and objectives | Course Outcomes | |
|---|---|---|
| 1. | The students can understand the fundamentals of image processing & computer vision |
A-1
|
| 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. |
A-1
|
| 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. |
A-1
|
| 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 |
| 240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. |
380分 |
| 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 |
| 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% | - |
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.
<Note>
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.
<Note>
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.
| ways of feedback | specific contents about "Other" |
|---|---|
| The Others | Feedbacks on exams, assignments, etc can be done either in class, via scomb or email. |
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
2. Computer Vision: Algorithms and Applications 2nd Edition, Richard Szeliski, 2021
3. Multiple view geometry in Computer vision, 2nd edition, Richard Hartley, 2004
- 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.
- Course that cultivates an ability for utilizing knowledge
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| N/A | N/A |
Last modified : Sat Sep 09 07:16:43 JST 2023