2021 Fundamentals of Computer Vision

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Academic unit or major
Graduate major in Artificial Intelligence
Kawakami Rei  Sato Ikuro 
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Tue7-8()  Fri7-8()  
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Syllabus updated
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Course description and aims

Computer vision is a field of research that uses computers to extract information of interest from data acquired by visual sensor. This course introduces the fundamentals of computer vision, including image processing, image features extraction, 3D structure reconstruction, segmentation, and camera calibration. This course aims to develop bases to study advanced computer vision topics such as image recognition and image generation.

Student learning outcomes

- To be able to explain and implement basic image processing, filtering, feature extraction, and 3D reconstruction methods.
- To be able to explain about image segmentation and camera calibration.

Course taught by instructors with work experience

Applicable How instructors' work experience benefits the course
The instructors have been using methods such as feature extraction, 3D reconstruction, and calibration in the automotive industry.


Image Processing, Image Filtering, Image Features, Optical Flow, Epipolar Geometry, Stereo Matching, Image Segmentation, Camera Calibration

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 Introduction To understand areas of computer vision
Class 2 Basics of Image Processing To understand basics of digital image processing
Class 3 Programming: Image Processing Implementation of basic image processing
Class 4 Filtering To understand 2D filters (e.g. smoothing, edge extraction)
Class 5 Programming: Filtering Implementation of basic image filtering
Class 6 Image Features To understand key point detectors and local descriptors
Class 7 Programming: Image Features Implementation of a representative key point detector or local descriptor.
Class 8 Optical Flow To understand basics of optical flow (e.g. Lukas-Kanade method)
Class 9 Epipolar Geometry To understand epipolar geometry, essential matrix, and motion parameter estimation
Class 10 Stereo Matching To understand 3D reconstruction from stereo camera
Class 11 Programming: 3D Reconstruction Implementation of 3D reconstruction method from multiple images
Class 12 Segmentation To understand segmentation method (e.g. Level Set, Graph Cut)
Class 13 Camera Calibration To understand extrinsic and intrinsic parameters
Class 14 Discussion: Related Work To discuss related computer vision papers

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

Students are encouraged to review the materials taught in each class for about 60 minutes.


None required.

Reference books, course materials, etc.

Lecture slides are used during the class.
Richard Szeliski, Computer Vision: Algorithms and Applications, 2nd ed., Springer, 2011.
Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, 2nd ed., Cambridge University Press, 2004.

Assessment criteria and methods

Final Report (40%) and 4 short reports (60%)

Related courses

  • ART.T552 : Advanced Topics in Computer Vision
  • ART.T551 : Image and Video Recognition
  • ART.T463 : Computer Graphics
  • XCO.T489 : Fundamentals of artificial intelligence
  • ART.T465 : Sparse Signal Processing and Optimization
  • ART.T547 : Multimedia Information Processing

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

Students are required to have undergraduate-level knowledges on computer science, linear algebra, calculus, probability, and statistics.

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

isato[at]c.titech.ac.jp; reikawa[at]c.titech.ac.jp

Office hours

11:30-12:00 on Wednesdays


Students are required to set up an environment to use MATLAB and Python.

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