Information Package
baskent.edu.tr

Program of Artificial Intelligence Engineering (English)

AI309

SIGNAL PROCESSING AND COMPUTER VISION

Course Code
AI309
Semester
Fifth Term (Fall)
Theory+Practice
3 + 1
ECTS
4
Course TypeCompulsory Course
Course LevelBachelor's Degree
Year of Study3
SemesterFifth Term (Fall)
ECTS4
Name of Lecturer(s) -
Learning Outcomes

At the end of this course, the students;

  1. Know the low- and mid-level image processing techniques such as filtering, edge detection, segmentation and clustering.
  2. Know about the object and scene recognition.
  3. Know the techniques of motion detection from video data.
  4. Know about the object and people tracking.
  5. Comprehend the human activity recognition and inference.
Mode of DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme Components
Course DescriptionThis course integrates fundamental principles of continuous and discrete-time signal processing with applications in computer vision. Topics include convolution, system responses, and Fourier transforms, applied to visual data such as images and video. Students will gain the ability to analyze and interpret visual information using signal-based techniques.
Course Contents
WEEK TOPICS
1st Week An introduction and basic concepts
2nd Week Low and mid level image processing:filtering
3rd Week Low and mid level image processing:filtering
4th Week Edge detection
5th Week Segmentation and clustering
6th Week Object and scene recognition
7th Week Object and scene recognition
8th Week Mid-term
9th Week Motion detection from video data
10th Week Motion detection from video data
11th Week Object and people tracking
12th Week Object and people tracking
13th Week Human activity recognition and inference
14th Week Human activity recognition and inference
Recommended or Required Reading* Oppenheim, A. V., Willsky, A. S., (1997) "Signals and Systems", Prentice-Hall, 2nd Ed.
* Buck-Daniel-Singer, (2002) "Signals and Systems using MATLAB", Prentice-Hall, 2nd Ed.
* Forsyth, D.A. & Ponce, J., "Computer Vision: A Modern Approach", 2nd edition, Prentice Hall, (2011).
* Shapiro, L.G. & Stockman, G.C., "Computer Vision", Prentice Hall, (2001).
* Parker, J.R., "Algorithms for Image Processing and Computer Vision", Wiley, (2010).
Planned Learning Activities and Teaching Methods
  • Lecture
  • Questions/Answers
  • Practice
  • Problem Solving
  • Project
  • Report Preparation
  • Presentation
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 30
Project 1 30
Total(%) 60
Contribution of In-term Studies to Overall Grade(%) 60
Contribution of Final Examination to Overall Grade(%) 40
Total(%) 100
Language of InstructionTurkish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

LO1 LO2 LO3 LO4 LO5
K1
K2
K3
K4
K5
K6
K7
K8
K9
K10
K11
K12