2024 | Visual and Knowledge Information Processing

2024　Visual and Knowledge Information Processing

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Academic unit or major: Graduate major in Systems and Control Engineering

Instructor(s): Kawakami Rei

Class Format: Lecture

Media-enhanced courses

Day/Period(Room No.): -

Group: -

Course number: SCE.I435

Credits: 1

Academic year: 2024

Offered quarter: 4Q

Syllabus updated: 2024/4/5

Lecture notes updated: -

Language used: English

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Syllabus

Course description and aims

This course is about information processing of knowledge, especially about computer vision. The classical methods of information acquisition, which are now often estimated by neural networks based on learning, will be studied, leading to a deeper understanding of events related to cameras, images, geometry, and reflective properties. Topics include image feature extraction, 3D shape reconstruction, object tracking, generative modeling, and reinforcement learning.

Student learning outcomes

To be able to explain and implement basic topics about computer vision and reinforcement learning.

Keywords

Computer vision, reinforcement learning, image features, object recognition, image segmentation, epipolar geometry, 3D restoration, optical flow, object tracking, image-based rendering, generative modeling
　

Competencies that will be developed

✔ Specialist skills

Intercultural skills

Communication skills

✔ Critical thinking skills

✔ Practical and/or problem-solving skills

Class flow

Lectures and programming exercises.

Course schedule/Required learning

	Course schedule	Required learning
Class 1	Neural networks	Topics include multi-layer perceptron, error backpropagation, convolutional neural networks, transformer, and network optimization.
Class 2	Image features	Topics include Harris corner detector, scale selection, SIFT, HOG, ORB, and object detection.
Class 3	Object recognition, metric learning, image segmentation	Topics include face detection, identify recognition, pedestrian detection, gestalt principle, K-means/Mean-shift clustering, graphcut, superpixels.
Class 4	Epipolar geometry, 3D reconstruction	Topics include epipolar geometry, essential matrix, fundamental matrix, robust estimation, Structure from Motion (SfM).
Class 5	Optical flow, object tracking	Topics include optical flow, aperture problem, tracking, particile filter、data association.
Class 6	Special imaging devices, generative models	Topics include plenoptic function, light field, high-dynamic range imaging, neural rendering, generative models.
Class 7	Reinforcement learning	Topics include Markov decision process, optimal action value function, Q learning.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.

Textbook(s)

None.

Reference books, course materials, etc.

Computer Vision: Algorithms and Applications

Assessment criteria and methods

The level of understanding and ability to apply the lecture content will be evaluated by submitting reports on the assignments.

Related courses

ART.T551 ： Image and Video Recognition
ART.T463 ： Computer Graphics
XCO.T489 ： Fundamentals of Artificial Intelligence
ART.T547 ： Multimedia Information Processing
SCE.I352 ： Fundamentals of Machine Learning
SCE.I501 ： Image Recognition

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

No course requirements.

Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).

reikawa[at]sc.e.titech.ac.jp

TOKYO INSTITUTE OF TECHNOLOGY