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| JDZ7400 | Advanced Segmentation in Photogrammetry | 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 | | | Contact Hours | 14 weeks - 3 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: | | To understand of the concept of the segmentation, to learn widely used segmentation algorithms, to application of these algorithms on photogrametrically or remotely sensed multi-spectral images in Matlab and Python environment. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Understand the importance of the segmentation concept in Photogrammetry | 1,3,5 | 1, | | PO - 2 : | Learn widely used segmentation algorithms | 1,3,5 | 1, | | PO - 3 : | Apply advanced segmentation methods on photogrametric images | 1,3,5 | 1, | | PO - 4 : | Make applications by programming some segmentation algorithms in Matlab and Python | 1,3,5 | 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 | | |
| Definition of segmentation, image segmentation techniques, thresholding based segmentation techniques, edge based segmentation techniques, region based segmentation techniques, selected advanced segmentation techniques, mean-shift algorithm, gradient vector flow algorithm, deformable segmentation algorithm, graph search based segmentation algorithm, optimal single and multiple surface detection algorithms, segmentation applications on photogrammetric aerial images. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | The concept of segmentation and related issues | | | Week 2 | Image segmentation techniques | | | Week 3 | Thresholding based segmentation algorithms | | | Week 4 | Edge based segmentation techniques | | | Week 5 | Region based segmentation techniques | | | Week 6 | Selected advanced segmentation techniques | | | Week 7 | Mean-Shift segmentation algorithm | | | Week 8 | Mid-term exam | | | Week 9 | Gradient vector flow segmentation algorithm | | | Week 10 | Deformable segmentation algorithm | | | Week 11 | Explaining the Segmentation Algorithm Based on Graph Search | | | Week 12 | Optimal single and multiple surface detection algorithms | | | Week 13 | Segmentation applications on Matlab, Python environment | | | Week 14 | Segmentation applications on Matlab, Python environment | | | Week 15 | Segmentation applications on Matlab, Python environment | | | Week 16 | Final exam | | | |
| 1 | Sonka, M., Hlavac, V., & Boyle, R. (2014). Image processing, analysis, and machine vision. C engage Learning. | | | |
| 1 | Gonzales, R., Woods, R., & Eddins, S. (2004). Digital Image Processing Using Matlab. | | | 2 | Russ, J. C., & Woods, R. P. (1995). The image processing handbook. Journal of Computer Assisted Tomography, 19(6), 979-981. | | | 3 | Ho, P. (2011). Image segmentation, Edited by Pei-Gee Peter Ho. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | | | | | | | |
| 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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