Information Package

Ph.D. on Defence Technologies and Systems

STS613

IMAGE SENSORS & PROCESSING

Course Code
STS613
Semester
-
Theory+Practice
3 + 0
ECTS
10
Course TypeElective Course
Course LevelDoctorate Of Science
Year of Study-
Semester-
ECTS10
Name of Lecturer(s)
Learning Outcomes

At the end of this course, the students;

  1. Know the history and application areas of image processing.
  2. Learn digital image models.
  3. Learn spatial and gray level solutions.
  4. Learns and applies arithmetic and logic operations on images.
  5. Learn and apply image enhancement and filtering methods.
Mode of DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme Componentsnone
Course DescriptionIntroduction to computer vision, image formation,Image model, image acquisition schemes,Lower-level vision problems: smoothing, edge detection, edge linking, multiscale approaches,Moderate vision problems: surface creation, drawing from tones, motion and stereo images,High-level vision problems: model-based vision, semantic networks, generalized cylinders,Hough transformation
Course Contents
WEEK TOPICS
1st Week Introduction to computer vision, image formation
2nd Week Introduction to computer vision, image formation
3rd Week Image model, image acquisition schemes
4th Week Image model, image acquisition schemes
5th Week Lower-level vision problems: smoothing, edge detection, edge linking, multiscale approaches
6th Week Lower-level vision problems: smoothing, edge detection, edge linking, multiscale approaches
7th Week Moderate vision problems: surface creation, drawing from tones, motion and stereo images
8th Week Midterm
9th Week Moderate vision problems: surface creation, drawing from tones, motion and stereo images
10th Week High-level vision problems: model-based vision, semantic networks, generalized cylinders
11th Week High-level vision problems: model-based vision, semantic networks, generalized cylinders
12th Week High-level vision problems: model-based vision, semantic networks, generalized cylinders
13th Week Hough transformation
14th Week Hough transformation
Recommended or Required ReadingDigital Image Processing, Rafael C. Gonzales and Richard E. Woods, Printice Hall, 2002.
Planned Learning Activities and Teaching Methods
  • Presentation
  • Lecture
  • Report Preparation
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 25
Assignment 1 25
Quiz 2 15
Attendance 1 5
Total(%) 70
Contribution of In-term Studies to Overall Grade(%) 70
Contribution of Final Examination to Overall Grade(%) 30
Total(%) 100
ECTS Allocated Based on Student Workload
Activities Quantity Duration (Hour) Total Workload (Hour)
Midterm exam 1 2 2
Preparation for Quiz 1 34 34
Individual or group work 14 8 112
Preparation for Final exam 1 60 60
Course hours 14 3 42
Preparation for Midterm exam 1 25 25
Laboratory (including preparation)
Final exam 1 2 2
Homework 2 10 20
Quiz 2 2 4
Total Work Load301
Total Work Load / 30 (h)10.03
ECTS Credit of the Course10
Language of InstructionTurkish
Work Placement(s)No
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