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.
(*)In 2021, the lecture video will be provided via Microsoft stream before the lecture (on-demand style). On the day of the lecture, an online live lecture will be held with zoom. The last five classes where you will work on the python programming exercise will be conducted in a combination of offline (PC room) and online (zoom breakout room).
(*)Lecture style may change depending on the prevalence of COVID19 and social conditions.
(*)Lecture notes are available from Ijiri's web page (takashiijiri.com) and lecture videos will be available from Microsoft stream.
(*)In 2021, the lecture video will be provided via Microsoft stream before the lecture (on-demand style). On the day of the lecture, an online live lecture will be held with zoom. The last five classes where you will work on the python programming exercise will be conducted in a combination of offline (PC room) and online (zoom breakout room).
(*)Lecture style may change depending on the prevalence of COVID19 and social conditions.
(*)Lecture notes are available from Ijiri's web page (takashiijiri.com) and lecture videos will be available from Microsoft stream.
To learn various developmental algorithms for image processing and image recognition.
To promote your python programming skills.
Lecture notes are available at takashiijiri.com.
Lecture videos are also available on Microsoft Stream.
To promote your python programming skills.
Lecture notes are available at takashiijiri.com.
Lecture videos are also available on Microsoft Stream.
- Image segmentation – Goal is that students understand various algorithms of image segmentation and can explain them.
- Feature extraction – Goal is that students can explain image features used for image recognitions and algorithms for extracting feature vectors.
- Object recognition – Goal is that students can explain basic algorithms for object recognition.
- Image processing exercises – Goal is that students can write basic image processing programs on Python.
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Introduction | Preparation : watch the lecture video and submit minutes paper. | 120minutes |
| Review lecture note and lecture video. | 80minutes | ||
| 2. | Feature detection 1: template matching, corner detection, edge detection | Preparation : watch the lecture video and submit minutes paper. | 120minutes |
| Review lecture note and lecture video. | 80minutes | ||
| 3. | Feature detection 2: DoG, SIFT, Hough transform | Preparation : watch the lecture video and submit minutes paper. | 120minutes |
| Review lecture note and lecture video. | 80minutes | ||
| 4. | Image segmentation 1: thresholding, region growing, active contours | Preparation : watch the lecture video and submit minutes paper. | 120minutes |
| Review lecture note and lecture video. | 80minutes | ||
| 5. | Image segmentation 2: graph cut, morphological operations, marching cubes | Preparation : watch the lecture video and submit minutes paper. | 120minutes |
| Review lecture note and lecture video. | 80minutes | ||
| 6. | Pattern recognition 1 : introduction to pattern recognition, KNN, SVM, Decision tree | Preparation : watch the lecture video and submit minutes paper. | 120minutes |
| Review lecture note and lecture video. | 80minutes | ||
| 7. | Pattern recognition 2 : NN, DNN | Preparation : watch the lecture video and submit minutes paper. | 120minutes |
| Review lecture note and lecture video. | 80minutes | ||
| 8. | Pattern recognition 3 : PCA, auto encoder | Preparation : watch the lecture video and submit minutes paper. | 120minutes |
| Review lecture note and lecture video. | 80minutes | ||
| 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 | minute paper | Total. | |
|---|---|---|---|---|
| 1. | 7% | 10% | 10% | 27% |
| 2. | 7% | 10% | 10% | 27% |
| 3. | 6% | 10% | 10% | 26% |
| 4. | 20% | 20% | ||
| Total. | 20% | 50% | 30% | - |
By minute papers (30%), examinations (20%), and assignments (50%).
-- You should watch the lecture video and submit a minute paper before the lecture day.
-- The examination will contain basic questions (30~40%), developmental questions (20~30%), and calculation (30~40%) with respect to image recognition.
-- Programming assignments contain
+ basic assignments (70%)
+ developmental assignments (30%)
with respect to image processing and image recognition.
-- You should watch the lecture video and submit a minute paper before the lecture day.
-- The examination will contain basic questions (30~40%), developmental questions (20~30%), and calculation (30~40%) with respect to image recognition.
-- Programming assignments contain
+ basic assignments (70%)
+ developmental assignments (30%)
with respect to image processing and image recognition.
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 applicable |
|---|---|
| N/A | N/A |
- 4.QUALITY EDUCATION
- 17.PARTNERSHIPS FOR THE GOALS
Last modified : Thu Apr 01 04:04:08 JST 2021