Course title
L0366000
Digital Media Processing 2

 ijiri takashi Click to show questionnaire result at 2018
Course description
Image processing is indispensable for various fields, including industry, natural science, entertainment, and so on. This lecture, named digital media processing 2, introduces various image processing techniques such as filtering, segmentation, feature extraction, and object recognition. For each technique, the lecture provides interactive demonstration with source code (python) to support your deep comprehension.
Purpose of class
To learn various developmental algorithms for image processing.
Goals and objectives
  1. Feature extraction – Become to be able to explain basic image features used for object recognition.
  2. Object recognition – Become to be able to explain basic algorithms for object recognition.
  3. Image segmentation – Become to be able to explain various methods for image segmentation
  4. Image processing exercises – Become to be able to write basic image processing programs on Python
Language
Japanese
Class schedule

 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
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 exam programming Total.
1. 20% 15% 35%
2. 15% 20% 35%
3. 15% 15% 30%
Total. 50% 50% -
Evaluation method and criteria
By examinations and assignments
Textbooks and reference materials
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.
Prerequisites
Make a study plan based on this syllabus and lecture notes available online (takashiijiri.com).
Office hours and How to contact professors for questions
  • 10:40-12:30 Friday
Relation to the environment
Non-environment-related course
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates a basic problem-solving skills
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
About half of the classes are interactive
Last modified : Wed Oct 17 06:50:04 JST 2018