Graduate study programme

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Image Processing and Computer Vision DRd1-05

ECTS 6 | P 45 | A 0 | L 30 | K 0 | ISVU 149786 190683 | Academic year: 2019./2020.

Course groups

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Course lecturers



Introduce students to basic methods used in image processing and computer vision, from basic image transformation, image enhancement, feature extraction to basic computer vision algorithms. Through programme tasks, students are introduced to the ways in which image processing algorithms and computer vision work.

Conditions for enrollment

Requirements met for enrolling in the study programme

Course description

Definitions, image types, discretisation, degradations in digital images. Image transformations: continuous Fourier transform, discrete Fourier transform, image pyramids, discrete wavelet transform. Colour perception and colour spaces. Image compression. Image interpolation. Image enhancement: point operations, linear filters, wavelet shrinkage, median filters, m-smoothers, morphological filters, nonlinear diffusion filtering, Discrete Variational Methods, Continuous Variational Methods, Fourier methods and deconvolution. Feature extraction: edges, edges in multichannel images and corners, contour representations and Hough transform. Texture analysis. Segmentation: classical methods, optimisation methods. Image sequence analysis: local methods, variational methods. 3-D reconstruction: camera geometry, stereo, shape-from-shading. Object recognition: invariants, eigenspace methods.

Student requirements

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Monitoring of students

Defined by the Student evaluation criteria of the Faculty of Electrical Engineering, Computer Science and Information Technology Osijek and paragraph 1.9

Obligatory literature

1. 1 Gonzalez, R.C.G.; Woods, R. E. Digital Image Processing New Jersey: Pearson Education, 2008.

Pretraži literaturu na:

Recommended additional literature

1. 1 E. Trucco, A. Verri Introductory Techniques for 3-D Computer Vision Prentice Hall, New Jersey, 1998.

2. 2 J. Bigun Vision with Direction Springer, Berlin, 2006.

Course assessment

Conducting university questionnaires on teachers (student-teacher relationship, transparency of assessment criteria, motivation for teaching, teaching clarity, etc.). Conducting Faculty surveys on courses (upon passing the exam, student self-assessment of the adopted learning outcomes and student workload in relation to the number of ECTS credits allocated to activities and courses as a whole).

Overview of course assesment

Learning outcomes
Upon successful completion of the course, students will be able to:

1. define and describe the concepts of image processing and computer vision

2. describe the methods of image processing and computer vision

3. apply the basics of image processing and computer vision and evaluate results

4. analyse a practical problem of digital image processing

5. use and customise the basic image processing and computer vision algorithms and interpret results

6. interconnect acquired knowledge and apply methods for processing image and computer vision in open source applications and interpret results

Aktivnosti studenta: Vidi tablicu aktivnosti