Subject

Digital image processing

Course Title

Digital image processing
Digital image processing

Code

F23L2S095

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

4 / Summer

Number of ECTS credits

Teacher

Andrea Kulakov, Ivica Dimitrovski

Prerequisites for enrolling in the course

Discrete Mathematics or Discrete Structures 2 or Mathematics 2 or Selected Topics in Mathematics

10.

Objectives of the course programme (competences)

Upon completion of the course, the student is expected to master and use the basic tools and methods for image processing.

11.

Course content

Lectures:
1. Historical overview; Types of images and image creation devices; Image display; Digital images and pixels; Color components; Overview of applications and examples of digital image processing; Image digitization: 2-D sampling and reconstruction; quantization; digitization; Introduction to OpenCV and Python; Basic image operations
2. Pixel-level transformations in images; Image histogram; Image contrast and gamma; Histogram equalization; Adaptive histogram equalization; Color space.
3. Linear filters and convolution; Image smoothing and sharpening.
4. Edge detection: gradient-based methods, Laplacian-based methods; edge merging; Image segmentation: threshold-based segmentation; region-based segmentation.
5. Morphological operations on an image: dilation/erosion; opening/closing; boundary extraction; region filling; connected component extraction; thinning; thickening; skeletonization.
6. 2-D Fourier transform and properties. Wavelets and multiresolution processing. Image templates and pyramids.
7. Image compression.
8. Detection of shapes in an image and contour analysis.
9. Extraction of visual features from images. Keypoint detection. A lexicon of visual words. Panoramic images.
10. Application of image processing algorithms.
11. Application of image processing algorithms. .

Practical Classes:
1. Installation and configuration of OpenCV and Python; Basic operations in OpenCV and Python
2. Practical examples of transforming pixel values in an image, calculating histograms, and manipulating image contrast.
3. Practical examples in OpenCV and Python.
4. Practical examples in OpenCV and Python.
5. Practical examples in OpenCV and Python.
6. Practical examples in OpenCV and Python.
7. Practical examples in OpenCV and Python.
8. Practical examples in OpenCV and Python.
9. Practical examples in OpenCV and Python.
10. Practical examples in OpenCV and Python.
11. Practical examples in OpenCV and Python.

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment and seminar paper, learning in an electronic environment (forums, consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	0 points
17.4.	Final exam	70 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

completed activities 15.1 and 15.2

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. Rafael C. Gonzalez, Richard E. Woods \| Digital Image Processing (4th Edition) \| Pearson \| 2017 2. Prateek Joshi \| OpenCV with Python By Example \| Packt Publishing \| 2015
22.2.	Additional literature No. Author Title Publisher Year