COMPUTER VISION

AIM

The aim of this course is to know, apply and evaluate computer vision techniques.

CONTENT

This course contains; Image formation (radiometric),Image formation (geometric) ,3D vision,Segmentation,Shape representation,Feature extraction,Texture representation and analysis,Image understanding,Object recognition,Optical flow estimation,Panoramic imaging,Object tracking,Color,High dynamic range imaging.

1. 1

   Describe radiometric and geometric image formation process.

   Taught by: Lecture Method · Assessed by: Traditional Written Exam, Homework

2. 2

   Apply various computer vision techniques

   Taught by: Lecture Method · Assessed by: Traditional Written Exam, Homework

3. 3

   Develop new computer vision algorithms

   Taught by: Lecture Method · Assessed by: Traditional Written Exam, Homework

4. 4

   Compare various computer vision techniques.

   Taught by: Lecture Method · Assessed by: Traditional Written Exam, Homework

WEEKLY PLAN

1. WEEK 1

   Image formation (radiometric)

2. WEEK 2

   Image formation (geometric)

3. WEEK 3

   3D vision

4. WEEK 4

   Segmentation

5. WEEK 5

   Shape representation

6. WEEK 6

   Feature extraction

7. WEEK 7

   Texture representation and analysis

8. WEEK 8

   Image understanding

9. WEEK 9

   Object recognition

10. WEEK 10

    Optical flow estimation

11. WEEK 11

    Panoramic imaging

12. WEEK 12

    Object tracking

13. WEEK 13

    Color

14. WEEK 14

    High dynamic range imaging

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Sonka, Hlavac, and Boyle. “Image Processing, Analysis, and Machine Vision.” Cengage Learning, 4th edition.

TEACHING STAFF

• Prof.Dr. Bahadır Kürşat GÜNTÜRKCOORDINATOR
• Assist.Prof. İbrahim KARLIAĞA