• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Learn basic concepts of photogrammetry and computer vision integration

   Contribution to Program Outcomes

   1. Obtain basic knowledge of Geomatics Engineering
   2. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields

   Method of assessment

   1. Written exam
   2. Term paper
2. Learn commonly used photogrammetric computer vision algorithms

   Contribution to Program Outcomes

   1. Obtain basic knowledge of Geomatics Engineering
   2. Design and develop hardware and/or software-based systems, components or processes in order to solve the defined problems,
   3. Ability to work independently and take responsibility

   Method of assessment

   1. Written exam
   2. Term paper
3. Apply applications with photogrammetric computer vision algorithms

   Contribution to Program Outcomes

   1. Obtain basic knowledge of Geomatics Engineering
   2. Design and develop hardware and/or software-based systems, components or processes in order to solve the defined problems,
   3. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
   4. Ability to work independently and take responsibility

   Method of assessment

   1. Term paper

Contents
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*:	-
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.
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	8	30
Other in-term studies:		0
Project:	13	20
Homework:	0	0
Quiz:		0
Final exam:	16	50
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	4	14
Practice, Recitation:	0	0
Homework:	0	0
Term project:	5	2
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	5	1
Mid-term:	1	1
Personal studies for final exam:	5	2
Final exam:	1	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)