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CS484: Introduction to Computer Vision
Fall 2024
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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, N24 Bldg, Rm. 1102 |
TA office hours |
Tuesday and Thursday 3:00PM—6:00PM, E3-1, Rm. 2401 |
Teaching Assistants |
Inchul Kim (Head TA, ex. 7864, )
Donggun Kim (ex. 7864, )
Hyeongjoon Cho (ex. 7864, )
Seeha Lee (ex. 7864,
)
Yeonwoo Lim (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]
Xiang Gao, Tao Zhang (2011) Introduction to Visual SLAM: From Theory to Practice, Splinger [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. Basic knowledge of Python and LaTeX is fundamentally required to fulfill homework tasks. |
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 |
Remarks |
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0 |
09/03 |
No lecture |
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1 |
09/05 |
Introduction, light, human visual system |
KLMS |
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2 |
09/10 |
Color camera |
KLMS |
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3 |
09/12 |
Color transformation |
KLMS |
hw1 |
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09/17 |
Chuseok Holiday |
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4 |
09/19 |
Image filter |
KLMS |
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5 |
09/24 |
Fourier transform |
KLMS |
hw2 |
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6 |
09/26 |
Image formation model |
KLMS |
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10/01 |
Armed Forces Day |
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10/03 |
National Foundation Day |
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8 |
10/08 |
Homography, calibration, thin-lens optics |
KLMS |
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9 |
10/10 |
Epipolar geometry |
KLMS |
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10 |
10/15 |
Stereo matching |
KLMS |
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11 |
10/17 |
Multiview geometry |
KLMS |
hw3 |
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10/22 |
Mid-term exam |
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12 |
10/29 |
Multiview geometry |
KLMS |
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13 |
10/31 |
3D scanning workflow |
KLMS |
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14 |
11/05 |
Feature detection |
KLMS |
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15 |
11/07 |
Feature matching |
KLMS |
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16 |
11/12 |
Feature descriptor |
KLMS |
hw4 |
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17 |
11/14 |
Machine learning for computer vision |
KLMS |
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18 |
11/19 |
Classification |
KLMS |
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19 |
11/21 |
Clustering |
KLMS |
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20 |
11/26 |
Recognition (Bag-of-words) |
KLMS |
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11/28 |
KAIST admission interview exam |
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12/03, 05 |
SIGGRAPH Asia 2024 Conference |
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hw5 |
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21 |
12/10 |
Linear regression and denoising |
KLMS |
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22 |
12/12 |
RANSAC, generalization error, dimension reduction |
KLMS |
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12/17 |
Final exam |
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Grading |
Attendance (10%), mid-term exam (30%), final exam (30%), homework assignments (30%) |
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Hosted by Visual Computing Laboratory, School of Computing, KAIST.
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