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| HRT3022 | Photogrammetric Computer Vision | 2+0+0 | ECTS:4 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 2 hours of lectures per week | | Lecturer | Doç. Dr. Mustafa DİHKAN | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Objectives of this course are to explain the basic fundamentals of Photogrammetric Computer Vision, digital image features, image orientation techniques, orthophoto creation and 3D point cloud estimation from digital image data
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| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Learn basic concepts of photogrammetry and computer vision integration | 1,2,6 | 1,3 | | LO - 2 : | Learns commonly used photogrammetric computer vision algorithms | 1,2,6 | 1,3 | | LO - 3 : | Make applications with photogrammetric computer vision algorithms | 1,2,6 | | | LO - 4 : | Various applications in Matlab environments | | | | 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), LO : Learning Outcome | | |
| Dijital image features, Relative orientation (RO) of two cameras, Direct and iterative RO methods, Triangulation, Bundle Adjustment, Aerial Triangulation, Orthophotos
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction | | | Week 2 | Digital image properties | | | Week 3 | Relative Orientation and the Fundamental Matrix
| | | Week 4 | Epipolar Geometry and the Essential Matrix
| | | Week 5 | Direct Solutions for Computing Fundamental and Essential | | | Week 6 | Iterative Solution for the Relative Orientation | | | Week 7 | Triangulation and Absolute Orientation | | | Week 8 | mid-term exam | | | Week 9 | Multi-View Reconstruction (Bundle Adjustment) | | | Week 10 | Multi-View Reconstruction (Bundle Adjustment) | | | Week 11 | Orthophotos | | | Week 12 | Finding Corresponding Points (SIFT Features & RANSAC) | | | Week 13 | Matlab exercises | | | Week 14 | Matlab exercises | | | Week 15 | Homework presentation | | | Week 16 | Final Exam | | | |
| 1 | Förstner & Wrobel: Photogrammetric Computer Vision, 2015 | | | |
| 1 | Szeliski: Computer Vision: Algorithms and Applications. Springer, 2010 | | | 2 | Hartley & Zisserman: Multiple View Geometry in Computer Vision, 2004 | | | 3 | Linder: Digital photogrammetry: theory and applications. Springer Science & Business Media, 2013. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | | 1 | 30 | | Homework/Assignment/Term-paper | 8
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12 | | 6 | 20 | | 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 | 2 | 14 | 28 | | Sınıf dışı çalışma | 4 | 14 | 56 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 8 | 1 | 8 | | Arasınav | 1 | 1 | 1 | | Uygulama | 0 | 0 | 0 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 8 | 1 | 8 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 8 | 1 | 8 | | Dönem sonu sınavı | 1 | 1 | 1 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 110 |
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