Image processing is indispensable for various fields, including industry, natural science, entertainment, and so on. This
class, named digital media processing 2, introduces various image processing techniques such as filtering, segmentation, feature
extraction, object recognition, and image recognition. This class include python programming exercise to achieve both deep
comprehension and to promote your programming skills.
To learn various developmental algorithms for image processing.
To promote your python progamming skills.
To promote your python progamming skills.
- Feature extraction – Goal is to be able to explain basic image features used for object recognition.
- Object recognition – Goal is to be able to explain basic algorithms for object recognition.
- Image segmentation – Goal is to be able to explain various methods for image segmentation
- Image processing exercises – Goal is to be able to write basic image processing programs on Python
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Introduction | Review the lecture note. | 100minutes |
| Prepare for the next lecture by checking online lecture note. | 100minutes | ||
| 2. | Feature detection 1: template matching, corner detection, edge detection | Review the lecture note. | 100minutes |
| Prepare for the next lecture by checking online lecture note. | 100minutes | ||
| 3. | Feature detection 2: DoG, SIFT, Hough transform | Review the lecture note. | 100minutes |
| Prepare for the next lecture by checking online lecture note. | 100minutes | ||
| 4. | Image segmentation 1: thresholding, region growing, active contours | Review the lecture note. | 100minutes |
| Prepare for the next lecture by checking online lecture note. | 100minutes | ||
| 5. | Image segmentation 2: graph cut, morphological operations, marching cubes | Review the lecture note. | 100minutes |
| Prepare for the next lecture by checking online lecture note. | 100minutes | ||
| 6. | Pattern recognition 1 : introduction to pattern recognition, KNN, SVM, Decision tree | Review the lecture note. | 100minutes |
| Prepare for the next lecture by checking online lecture note. | 100minutes | ||
| 7. | Pattern recognition 2 : NN, DNN | Review the lecture note. | 100minutes |
| Prepare for the next lecture by checking online lecture note. | 100minutes | ||
| 8. | Pattern recognition 3 : PCA, auto encoder | Review the lecture note. | 100minutes |
| Prepare for the next lecture by checking online lecture note. | 100minutes | ||
| 9. | Examination | Prepare for the examination | 200minutes |
| 10. | Programming exercise 1 | solve assignments | 200minutes |
| 11. | Programming exercise 2 | solve assignments | 200minutes |
| 12. | Programming exercise 3 | solve assignments | 200minutes |
| 13. | Programming exercise 4 | solve assignments | 200minutes |
| 14. | Programming exercise 5 | solve assignments | 200minutes |
| Total. | - | - | 2800minutes |
| exam | programming | Total. | |
|---|---|---|---|
| 1. | 20% | 10% | 30% |
| 2. | 20% | 10% | 30% |
| 3. | 10% | 10% | 20% |
| 4. | 20% | 20% | |
| Total. | 50% | 50% | - |
CG-Arts協会（画像情報教育進行委員会）『ディジタル画像処理[改訂新版] 大型本』 (in Japanese).
All the lecture notes will be uploaded at takashiijiri.com/classes/ about one week before the lecture. I recommend you to check them in advance.
All the lecture notes will be uploaded at takashiijiri.com/classes/ about one week before the lecture. I recommend you to check them in advance.
Make a study plan based on this syllabus and lecture notes available online (takashiijiri.com).
- Course that cultivates a basic problem-solving skills
- Course that cultivates an ability for utilizing knowledge
| Work experience | Work experience and relevance to the course content if applicatable |
|---|---|
| N/A | N/A |
Last modified : Thu Mar 21 14:45:58 JST 2019