INTRODUCTION to COMPUTER VISION

AIM

To understand the basic topics in computer vision and to apply and evaluate various computer vision techniques.

CONTENT

This course contains; Optical image formation,Imaging pipeline,Image filtering,Edge detection and Hough transform,Morphological operations,Image enhancement,Keypoint detection (basic ideas),Keypoint detection (scale invariant methods),Image interpolation,Geometric transformations,Motion estimation,Camera calibration,3D vision,Color space.

1. 1

   Understand and apply basic image processing techniques

   Taught by: Problem Solving Method, Self Study Method, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

2. 2

   Understand and apply image formation and modeling concepts

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

3. 3

   Understand and apply mid-level computer vision techniques, including feature extraction and optical flow

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

4. 4

   Design and evaluate solutions to computer vision problems

   Taught by: Problem Solving Method, Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Traditional Written Exam, Homework

WEEKLY PLAN

1. WEEK 1

   Optical image formation

2. WEEK 2

   Imaging pipeline

3. WEEK 3

   Image filtering

4. WEEK 4

   Edge detection and Hough transform

5. WEEK 5

   Morphological operations

6. WEEK 6

   Image enhancement

7. WEEK 7

   Keypoint detection (basic ideas)

8. WEEK 8

   Keypoint detection (scale invariant methods)

9. WEEK 9

   Image interpolation

10. WEEK 10

    Geometric transformations

11. WEEK 11

    Motion estimation

12. WEEK 12

    Camera calibration

13. WEEK 13

    3D vision

14. WEEK 14

    Color space

ASSESSMENT

• Rate of Midterm Exam to Success30%
• Rate of Final Exam to Success70%

WORKLOAD

READING

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

TEACHING STAFF

• Assist.Prof. İbrahim KARLIAĞACOORDINATOR
• Assist.Prof. İbrahim KARLIAĞA