| Program / Major | mDP | Goals |
|---|---|---|
| 先進国際課程 | A-1 | A-1 Students shall obtain basic and advanced knowledge and skills in mathematics, natural and computer sciences as well as presentation skills to communicate on their knowledge with scholars from various fields. |
| (改組前)先進国際課程 | A-1 | A-1 Students shall obtain basic and advanced knowledge and skills in mathematics, natural and computer sciences as well as presentation skills to communicate on their knowledge with scholars from various fields. |
- Students are expected to develop a fundamental understanding of computer vision and image processing, along with their practical
applications in areas such as object recognition, 3D reconstruction, medical imaging, and real-time visual analysis.
- Students are also expected to enhance their ability to design, implement, and evaluate computer vision systems by applying advanced techniques and methodologies. They will develop problem-solving skills to tackle real-world challenges, engage in research and innovation, and effectively communicate their findings to both technical and non-technical audiences.
- Students are also expected to enhance their ability to design, implement, and evaluate computer vision systems by applying advanced techniques and methodologies. They will develop problem-solving skills to tackle real-world challenges, engage in research and innovation, and effectively communicate their findings to both technical and non-technical audiences.
For years, computer vision and image processing have played a pivotal role in various aspects of Computer Science and Engineering,
driving advancements in areas such as object detection, recognition, 3D reconstruction, medical imaging, autonomous systems,
and augmented reality. Given the growing importance of these fields, acquiring a comprehensive understanding of image processing
and computer vision has become essential.
This course provides an in-depth exploration of key concepts and techniques, including image processing in both spatial and frequency domains, as well as computer vision methodologies such as feature extraction, detection, classification, tracking, and 3D scene reconstruction. Through hands-on exercises and theoretical insights, students will develop a strong foundation that prepares them for cutting-edge research in image and video analysis, computational photography, deep learning-based vision, and real-time image rendering.
This course provides an in-depth exploration of key concepts and techniques, including image processing in both spatial and frequency domains, as well as computer vision methodologies such as feature extraction, detection, classification, tracking, and 3D scene reconstruction. Through hands-on exercises and theoretical insights, students will develop a strong foundation that prepares them for cutting-edge research in image and video analysis, computational photography, deep learning-based vision, and real-time image rendering.
- The students can explain the fundamentals of image processing ＆ computer vision
- Students can explain concepts in image processing and computer vision through various applications, enabling both the development of assignments and the practical solving of real-life problems.
- Students can develop programming skills, teamwork, and critical thinking while also cultivating a positive attitude by proactively discussing with classmates and the lecturer, attending class on time, submitting assignments punctually, and maintaining academic integrity during exams.
| Mid-term | Final | Assignment | Total. | |
|---|---|---|---|---|
| 1. | 10% | 20% | 10% | 40% |
| 2. | 10% | 20% | 5% | 35% |
| 3. | 10% | 10% | 5% | 25% |
| Total. | 30% | 50% | 20% | - |
Grading Criteria:
Mid-exam (30%), final-exam (50%), assignment (20%)
60% of the total score is needed for earning the credit
Cheating during exams results in “F” score
＜Note＞:
- Students will be marked absent if they arrive more than 10 minutes late to class.
- If a student is absent for more than 4 classes, they cannot obtain sufficient knowledge , as the result, they cannot be eligible for earning credits.
- Using smartphone and PC during the exams will not be allowed.
Mid-exam (30%), final-exam (50%), assignment (20%)
60% of the total score is needed for earning the credit
Cheating during exams results in “F” score
＜Note＞:
- Students will be marked absent if they arrive more than 10 minutes late to class.
- If a student is absent for more than 4 classes, they cannot obtain sufficient knowledge , as the result, they cannot be eligible for earning credits.
- Using smartphone and PC during the exams will not be allowed.
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Introduction to image processing ＆ computer vision | Review what you learnt and do the assignments | 380minutes |
| 2. | Image Formation-Human vision system- Color | Review what you learnt and do the assignments | 380minutes |
| 3. | Spatial Domain Image Processing | In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 4. | Frequency Domain Image Processing | In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 5. | Image Feature I | In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 6. | Image Feature II | In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 7. | Mid-term examination and discussions on the solutions | Review all previous lessons as the preparation for a writing test (with 10 questions for 120 minutes) | 380minutes |
| 8. | Image Feature III Image Stitching, Registration and mosaicking |
In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 9. | Object Detection and Tracking | In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 10. | Image Segmentation | In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 11. | 3D Reconstruction I | In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 12. | 3D Reconstruction II | In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 13. | A review on advanced topics in Computer Vision | In second half of class, student will practice with programming-based implementation so they are required to check the content of the class beforehand. | 380minutes |
| 14. | Final exam and discussions on the solutions | Review all learnt lessons and practice for the final exam test (with 10 questions for 120 minutes) | 380minutes |
| Total. | - | - | 5320minutes |
| ways of feedback | specific contents about "Other" |
|---|---|
| Feedback outside of the class (ScombZ, mail, etc.) | 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
Contact Professor Phan Xuan Tan for the appointments via email: tanpx@shibaura-it.ac.jp
- 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 Mar 14 13:40:42 JST 2026