Course title
L00250003
Computer Vision

 IJIRI Takashi
Middle-level Diploma Policy (mDP)
Program / Major mDP Goals Courses
Fundamental Mechanical Engineering F 産業界や社会の要請を把握して解決するべき課題を設定し、さまざまな工学分野の知識を関連付けながら設計生産技術を活用することで、立案した構想に従って研究を進め課題を解決することができる。 Sub
Advanced Mechanical Engineering F 産業界や社会の要請を把握して解決するべき課題を設定し、機械工学の学理を応用して異分野を含む融合分野で革新的な機能を創成することができる。 Sub
Environment and Materials Engineering B 地球環境や地域社会との調和を見据えて、さまざまな工学分野に関わる問題を解決することができる。 Sub
Chemistry and Biotechnology B 地球環境や地域社会との調和を見据えて、さまざまな工学分野に関わる問題を解決することができる。 Sub
Electrical Engineering and Robotics D 電気工学や関連する工学の技術分野を課題に適用し、社会の要求を解決するために応用することができる。 Sub
Advanced Electronic Engineering E 専門的デザイン課題について解決する能力を身に付けることができる。 Sub
Information and Communications Engineering F 社会のニーズに対して技術課題を主体的に発見し、工学分野における分野横断的な知識も活用しつつ、計画的・継続的に取り組んで課題を達成することができる。 Sub
Computer Science and Engineering B-2 コンピュータサイエンスの各分野の基礎知識とその応用能力を身に付けることができる。 Main
Urban Infrastructure and Environment G ⼟⽊⼯学における現実の問題について、⼯学・専⾨基礎知識を⽤いて理解・解決することができる。 Sub
Purpose of class
To learn various methods concerning image processing and image recognition.
To obtain deeper knowledge of the methods presented in the class and programming skills through Python programming exercises.
Course description
Image processing is crucial in advancing various fields, including industry, natural sciences, and entertainment. In the Digital Media Processing class for second-year undergraduate students, the basics of image processing, such as image data structure, image capturing methods, various filters, resizing, interpolation, and more, were introduced.

Building upon the Digital Media Processing class, this class will present methods for computers to recognize images. Specifically, this class introduces:
(1) Segmentation, which is a method to cut out regions of interest from an image
(2) Feature extraction, which is a method to convert a part of an image into a format that a computer can recognize
(3) Pattern recognition, which is a method to identify images (or image contents) using the extracted features.
In addition, we briefly introduce image processing using deep learning.

In the last half of this class, programming exercises using Python will be conducted for a deeper comprehension of the methods presented in the class.
Goals and objectives
  1. To be able to explain and implement representative algorithms for image feature extraction
  2. To be able to explain and implement representative algorithms for image segmentation
  3. To be able to explain and implement representative algorithms for pattern recognition
  4. To be able to implement various CV algorithms with python
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 mini exam exam programming Total.
1. 6% 15% 21%
2. 3% 10% 13%
3. 6% 20% 26%
4. 40% 40%
Total. 15% 45% 40% -
Evaluation method and criteria
By mini exams (15%), exam (45%) and assignments (40%).
-- The mini exam and exam will contain basic questions (30~40%), developmental questions (20~30%), and calculation (30~40%) with respect to image processing.
-- Programming assignments contain basic assignments (60%) and developmental assignments (40%) with respect to image processing and image recognition.

If solving all basic questions and programming assignments, it will be evaluated as 60%.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Introduction
# mini exam 		Preparation and review the lecture by using online lecture notes. 200minutes
2. Feature extraction 1 : template matching, corner detection, edge detection
# mini exam 		Preparation and review the lecture by using online lecture notes. 200minutes
3. Feature extraction 2 :Difference of Gaussian. SIFT, Hough Transform.
# mini exam 		Preparation and review the lecture by using online lecture notes. 200minutes
4. Segmentation 1:
Introduction to image segmentation,
thresholding,
region growing,
active contours
# mini exam 		Preparation and review the lecture by using online lecture notes. 200minutes
5. Segmentation 2:
graph cut segmentation,
morphological operation, m
arching cubes

Optical flow

# mini exam 		Preparation and review the lecture by using online lecture notes. 200minutes
6. Pattern recognition 1: Introduction to pattern recognition, support vector machine, random forest, k-nearest neighbors

# mini exam 		Preparation and review the lecture by using online lecture notes. 200minutes
7. Pattern recognition 2: Neural network, deep learning
# mini exam 		Preparation and review the lecture by using online lecture notes. 200minutes
8. Pattern recognition 3: PCA, auto encoder
# mini exam 		Preparation and review the lecture by using online lecture notes. 200minutes
9. survey and examination preparation for the examination 200minutes
10. Programming exercise (PC room): solve assignments. 200minutes
11. Programming exercise (PC room): solve assignments. 200minutes
12. Programming exercise (PC room): solve assignments. 200minutes
13. Programming exercise (PC room): solve assignments. 200minutes
14. Programming exercise (PC room): solve assignments. 200minutes
Total. - - 2800minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback outside of the class (ScombZ, mail, etc.)
Textbooks and reference materials
reference material : 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』
Prerequisites
Recommend to make a study plan based on this syllabus and online lecture notes (takashiijiri.com/classes/).
Office hours and How to contact professors for questions
  • Friday 09:00-10:40
  • contact me with email or slack
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
  • Course that cultivates a basic self-management skills
Active-learning course
About half of the classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 4.QUALITY EDUCATION
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Sat Mar 14 14:20:23 JST 2026