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| JDZL7401 | Photogrammetric Computer Vision | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Doç. Dr. Mustafa DİHKAN | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Understanding the basic concepts of photogrammetry and computer vision, understanding camera calibration models, Understanding the concepts of homography, epipolar geometry, Essential and Fundamental matrix in 3D reconstruction process, understanding of various feature detection algorithms (Harris, SIFT, SURF, etc.), conjugate point determination with RANSAC and self-calibration techniques, Understanding of automatic bundle adjustment and 3D point cloud generation with image sequences, Various applications in Matlab and Phyton environments.
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| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Learn basic concepts of photogrammetry and computer vision integration | 1 | 1 | | PO - 2 : | Learns commonly used photogrammetric computer vision algorithms | 8 | 1 | | PO - 3 : | Make applications with photogrammetric computer vision algorithms | 1 | 1,4 | | PO - 4 : | Various applications in Matlab and Phyton environments | 8 | 1,6 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Basic concepts of photogrammetry and computer vision, explanation of the concepts of digital image formation and projective geometry, explanation of camera calibration models, explanation of homography, epipolar geometry, Essential and Fundamental matrix concepts in 3D reconstruction process, analysis of various feature detection algorithms (Harris, SIFT, SURF, etc.), conjugate point determination with RANSAC and self-calibration techniques, implementing various applications on digital images acquired by photogrammetry/remote sensing techniques on Matlab and Python platforms.
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| Course Syllabus | | Week | Subject | Related Notes / Files | | 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 and application of various feature detection algorithms (Harris, SIFT, SURF, etc.) | | | Week 8 | Explanation and application of various feature detection algorithms (Harris, SIFT, SURF, etc.) | | | Week 9 | Mid-term exam | | | 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 in Matlab and Python environment | | | Week 14 | Implementing various applications in Matlab and Python environment | | | Week 15 | Implementing various applications in Matlab and Python environment | | | Week 16 | Final Exam | | | |
| 1 | Förstner, Wolfgang, and Bernhard P. Wrobel. Photogrammetric computer vision. Springer International Publishing Switzerland, 2016.
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| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1 | 50 | | End-of-term exam | 16 | | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 3.5 | 14 | 49 | | Arasınav için hazırlık | 8 | 1 | 8 | | Arasınav | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 15 | 1 | 15 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 116 |
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