Digital Image Processing and Analysis

Course Description

The course provides knowledge in theory and applications of digital image processing and analysis. Elements of human visual system. Two-dimensional (2-D) sequences. Linear 2-D system. 2-D convolution. Sampling and quantization. 2-D transforms. Image enhancement in spatial domain. Image histogram operations. Histogram equalization and specification. Homomorfic filtering. Median filter. Image enhanement in the frequency domain. Image restoration. Inverse and pseudoinverse filtering. Wiener filter. Geometric image transformations. Color image representation and processing. Image feature extraction. Principal component analysis. Edge detection. Gradient and compass operators. Object boundary detection. Image segmentation. Hough transform. Clustering methods. Texture segmentation. Applications in biomedicine, industrial visual quality control, and communications.

General Competencies

Students will gain knowledge required to design methods for processing of images for the purpose of filtering, enhancement, restoration, or geometric transformation. They will be able to develop methods for image analysis including feature extraction and segmentation. Students will be able to develop software applications for image processing and analysis in biomedicine, industrial visual quality control, biometric security, and other areas.

Learning Outcomes

  1. define and describe concepts of image and video processing and analysis
  2. list examples of digital image and video processing applications
  3. explain methods for image processing and analysis
  4. analyze a practical image processing and analysis problem
  5. combine acquired knowledge and propose a solution to the given problem
  6. evaluate a practical solution to an image processing and analysis problem

Forms of Teaching

Lectures

Lectures are delivered according to the published plan and on the basis of the materials that are made available to the students.

Exams

Student exams are in the form of a mid-term exam and a final exam that are graded together with other student activities. Students who do not pass through continuing evaluation can take exam at a final examination term.

Laboratory Work

Laboratory excercises are completed according to the published plan and based on the instructions available to students. Laboratory involves software implementation of various methods for image processing and analysis.

Consultations

Oral consultations are available on student request.

Other Forms of Group and Self Study

Student teams consisting of 3-4 students work on team projects during the whole semestar. At the end of semester the team prepares a written report about the project and presents the project to other students and the professor, followed by a discussion.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Laboratory Exercises 50 % 20 % 50 % 0 %
Seminar/Project 50 % 20 % 50 % 0 %
Mid Term Exam: Written 50 % 30 % 0 %
Final Exam: Written 50 % 30 %
Exam: Written 50 % 50 %
Exam: Oral 50 %

Week by Week Schedule

  1. Introduction to digital image processing and analysis. Overview of applications.
  2. Two-dimensional systems and signals. Overview and discussion of team projects.
  3. Image transforms.
  4. Image enhancement.
  5. Image restoration - part one.
  6. Image restoration - part two.
  7. Examples of practical image and video processing projects (biomedicine, biometrics, automotive industry, visual inspection) that are conducted at FER.
  8. Midterm exam
  9. Image feature extraction. Edge detection.
  10. Image segmentation.
  11. Shape analysis.
  12. Motion analysis from image sequences.
  13. Presentations of team projects and discussion of results.
  14. Presentations of team projects and discussion of results.
  15. Final exam

Study Programmes

University graduate
Computer Science (profile)
Specialization Course (2. semester)
Control Engineering and Automation (profile)
Specialization Course (2. semester)
Electronic and Computer Engineering (profile)
Specialization Course (2. semester)
Information Processing (profile)
Specialization Course (2. semester)

Literature

S. Lončarić (2011.), Lectures Notes in Digital Image Processing, FER
R. C. Gonzalez, R. E. Woods (2007.), Digital Image Processing, Prentice Hall
M. Sonka, V. Hlavac, R. Boyle (2007.), Image Processing, Analysis, and Machine Vision, 3rd Ed., Brooks/Cole

Associate Lecturers

Laboratory exercises

General

ID 127250
  Summer semester
4 ECTS
L2 English Level
L1 e-Learning
30 Lectures
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

89 Excellent
79 Very Good
70 Good
61 Acceptable