2019 Computational Imaging

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Academic unit or major
Graduate major in Systems and Control Engineering
Instructor(s)
Tanaka Masayuki 
Course component(s)
Lecture
Mode of instruction
 
Day/Period(Room No.)
Mon3-4(S516)  
Group
-
Course number
SCE.I501
Credits
1
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/3/18
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

Computational imaging systems have variety of applications include consumer cameras, cell phone cameras, vehicle camera systems, surveillance, medical imaging, remote sensing, and human computer interaction. Topics of computational imaging have a wide range of technologies in computer vision and image processing. Recently, the network-based image processing become hot topic. This course focuses on the network-based image processing. In this course, students develop and train the network by themselves.

Student learning outcomes

Students are expected to
(i) gain an ability to build and learn deep neural networks,
(ii) gain an ability to use numerical computing environments using MATLAB to solve engineering problems,
(iii) gain practical skill to apply the deep learning techniques such as momentum, data arugumentation and filter setting, after taking this course.

Keywords

Computational imaging, Image processing, Convolutional neural network (CNN), Deep learning, matlab

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

This class is a kind of active learning. Instructor will give some information, but students are required to develop their own matlab code.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction of this course and of grader system. Logical operation by neural network
Class 2 Two-layer logical network & simple image processing Two-layer logical network & simple image processing
Class 3 Introduction of train1000 project: train the network with 1000 samples. Train 1000 project
Class 4 Introduction of BlockScramble challenge. Type-I: Supervised learning Type-II: Unspervised learning Develop and train own network.
Class 5 Key techniques of CNN Develop and train own network.
Class 6 Evaluation data submission Evaluation data submission
Class 7 Presentation1 Presentation
Class 8 Presentation2 Presentation

Textbook(s)

None

Reference books, course materials, etc.

None

Assessment criteria and methods

Presentation, and report.

Related courses

  • SCE.I531 : Computer Vision

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

Other

Students will code by themselves.

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