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CS484: Introduction to Computer Vision
Fall 2022
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Instructor |
Min Hyuk Kim, [Room] 2403, [email] |
Course description
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This course provides a comprehensive introduction to low-level computer vision, including the foundations of camera image formation, geometric optics, feature detection, stereo matching, motion estimation, image recognition, scene understanding, etc. This course will help students develop intuitions and mathematics of various computer vision applications. |
Lecture time and place |
Tuesday and Thursday 1:00PM—2:30PM, Creative Learning Bldg., Rm. 311 |
TA office hours |
Tuesday and Thursday 3:00PM—6:00PM, E3-1, Rm. 2401 |
Teaching Assistants |
Shinyoung Yi (Head TA, ex. 7864, )
Donggun Kim (ex. 7864, )
Inseung Hwang (ex. 7864, )
Jaemin Cho (ex. 7864, )
Sanghyeon Lee (ex. 7864, )
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Reference books |
Richard Szeliski (2010) Computer Vision: Algorithms and Applications, Springer [site]
Richard Hartley and Andrew Zisserman (2011) Multiple View Geometry in Computer Vision, Cambridge Press [site]
Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer [site]
Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016) Deep Learning, MIT Press [site]
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Prerequisites |
There are no official course prerequisites. |
Course goal |
Student will establish theoretical and practical foundations of computer vision and be familiar with various computer vision applications. |
Tentative schedule |
(Note that this curriculum will be revised adaptively.) |
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Index |
Date |
Lecture |
Slides |
HW |
Due |
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1 |
08/30 |
Introduction to computer vision |
slide01 |
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2 |
09/01 |
Light |
slide02 |
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3 |
09/06 |
Human visual system |
slide02 |
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4 |
09/08 |
Color camera, photography |
slide03 |
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5 |
09/13 |
Digital imaging |
slide04 |
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6 |
09/15 |
Image filter |
slide05 |
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7 |
09/20 |
Fourier Series & Transform |
slide06v5 |
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8 |
09/22 |
Image formation of camera |
slide07 |
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9 |
09/27 |
Epipolar geometry |
slide08v2 |
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10 |
09/29 |
Homography, calibration, thin-lens optics |
slide09 |
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10/04 |
No lecture for all Tuesday classes at KAIST |
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11 |
10/06 |
Stereo matching |
slide10 |
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12 |
10/11 |
Multiview geometry |
slide11 |
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13 |
10/13 |
3D scanning workflow |
slide12v2 |
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10/18 |
Mid-term exam |
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14 |
10/25 |
Feature detection (Harris corner detector) |
slide13 |
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15 |
10/27 |
Feature matching (blob dectection) |
slide14 |
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16 |
11/01 |
Feature descriptor (SIFT) |
slide15 |
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17 |
11/03 |
Optical flow and tracking |
slide16 |
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18 |
11/08 |
Machine learning for computer vision |
slide17 |
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19 |
11/10 |
Linear regression and denoising |
slide18 |
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20 |
11/15 |
RANSAC, generalization error |
slide19 |
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21 |
11/17 |
Classification |
slide20 |
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22 |
11/22 |
Clustering, demension reduction |
slide21 |
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23 |
11/24 |
Recognition (Bag-of-words) |
slide22 |
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24 |
11/29 |
Introduction to neural network |
slide23 |
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12/01 |
No lecture (KAIST entrance exam) |
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12/06 |
No lecture (SIGGRAPH Asia 22) |
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12/13 |
Final exam |
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Grading |
Attendance (10%), mid-term exam (25%), final exam (25%), homework assignments (40%) |
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Hosted by Visual Computing Laboratory, School of Computing, KAIST.
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