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Contents
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Week 1: |
Explaining the basic concepts of photogrammetry and computer vision |
Week 2: |
Explanation of image formation, projective geometry and homogeneous coordinate concepts |
Week 3: |
Explanation of camera geometry and calibration techniques |
Week 4: |
Explanation of Homography and Epipolar Geometry |
Week 5: |
Explanation of Essential and Fundamental matrix concepts |
Week 6: |
Basic stereo matching algorithm, dense stereo matching and depth detection |
Week 7: |
Explanation of various feature detection algorithms (Harris, SIFT, SURF, etc.) |
Week 8: |
Application of various feature detection algorithms (Harris, SIFT, SURF, etc.) - Midterm exam |
Week 9: |
Explanation and application of various feature detection algorithms (Harris, SIFT, SURF, etc.) |
Week 10: |
Description of conjugate point and self-calibration techniques with RANSAC |
Week 11: |
Bundle adjustment and 3D dense cloud generation techniques using image sequences |
Week 12: |
Explanation of Meshing and texturing techniques with multi-view images and sample applications |
Week 13: |
Implementing various applications - 1 - Term Project |
Week 14: |
Implementing various applications - 2 |
Week 15*: |
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Week 16*: |
FINAL EXAM |
Textbooks and materials: |
Förstner & Wrobel: Photogrammetric Computer Vision, 2015 |
Recommended readings: |
Szeliski: Computer Vision: Algorithms and Applications. Springer, 2010 Hartley & Zisserman: Multiple View Geometry in Computer Vision, 2004
Linder: Digital photogrammetry: theory and applications. Springer Science & Business Media, 2013.
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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