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Syllabus ( GEO 409 )


   Basic information
Course title: Photogrammetric Computer Vision
Course code: GEO 409
Lecturer: Assoc. Prof. Dr. Bahadır ERGÜN
ECTS credits: 5
GTU credits: 3 ()
Year, Semester: 2020, Fall and Spring
Level of course: First Cycle (Undergraduate)
Type of course: Elective
Language of instruction: Turkish
Mode of delivery: Face to face
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: The aim of this course is to teach 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 platform.
   Learning outcomes Up

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 Up
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 Up
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 Up
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)
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