460-2070/02 – Fundamentals of Image Processing (ZAO)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorIng. Radovan Fusek, Ph.D.Subject version guarantorIng. Radovan Fusek, Ph.D.
Study levelundergraduate or graduateRequirementOptional
Year3Semestersummer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesBachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
FUS032 Ing. Radovan Fusek, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Part-time Credit and Examination 10+0

Subject aims expressed by acquired skills and competences

The course acquaints with the topics of image analysis, which accompany the people in everyday life. These topics are a natural part of development of the society with the transition towards Industry 4.0. In case of completing the course, students gain an overview of modern methods of image analysis. In the case of their deeper interest, the students can attend the master study courses that are focused on digital processing and image analysis in which the students will obtain deeper information.

Teaching methods

Lectures
Tutorials

Summary

The following topics will be discussed: Image analysis in self-driving cars, image analysis in Industry 4.0, detection and recognition of 2D and 3D objects, detection and recognition of people and objects in the security and spy industry.

Compulsory literature:

1. Gary Bradski, Adrian Kaehler: Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O'Reilly Media, 2017 2. Petrou, M., Petrou, C.: Image Processing: The Fundamentals, Wiley, ISBN-10: 047074586X, ISBN-13: 978-0470745861, 2010

Recommended literature:

1. Michael Beyeler: Machine Learning for OpenCV, Packt Publishing, ISBN-13: 978-1783980284, 2017

Way of continuous check of knowledge in the course of semester

Conditions for granting the credit: The tasks that form the program of exercises must be worked out. Exam - written test.

E-learning

Other requirements

Without additional requirements.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Introduction to actual topics in image analysis 2. Image-based driver behavior recognition 3. Analysis of objects in vehicle surroundings, the detection of vehicles, pedestrians, traffic signs, traffic lights, etc. 4. Analysis of data gained from the self-driving car sensors 5. Detection and recognition of 3D objects and its application in augmented reality 6. Depth data gathering and analysis 7. Object detection in aerial and satellite images, buildings detection, parking lot analysis, smart cities 8. Image-based people identification, biometry 9. Image and video editing with a goal to fake reality 10. Actual and future trends in artificial intelligence in image processing Exercises: 1. Introduction to the image processing libraries 2. Practice the methods for image-based driver behavior recognition 3. Experiments with the methods for analysis of objects in vehicle surroundings 4. Introduction to the processing of car sensor data 5. Practice the methods for 3D object recognition 6. Experiments with the depth data 7. Experiments with the methods for the parking lot and satellite image analysis 8. Study of recognition techniques for image-based people identification 9. Experiments with image and video editing with a goal to fake reality 10. Introduction to the new trends in artificial intelligence in image processing

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 45  20
        Examination Examination 55  6
Mandatory attendence parzicipation: Credit: 20-45 (the tasks that form the program of exercises must be carry out) Exam: 6-55 (written and oral)

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (B0613A140010) Computer Science P English Ostrava 3 Optional study plan
2020/2021 (B0541A170009) Computational and Applied Mathematics P English Ostrava 3 Optional study plan
2019/2020 (B0613A140010) Computer Science P English Ostrava 3 Optional study plan
2019/2020 (B0541A170009) Computational and Applied Mathematics P English Ostrava 3 Optional study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner