All students must validate 30 ECTS per semester.We offer two speciality tracks:

Track 1: Vision and Applications
The first track focuses on various applications of computer vision: biomedical applications, people detection, object tracking, computational photography.

Track 2: Vision and devices
The second track focuses on devices to capture images (intelligent sensors, medical imaging systems) and to visualise and interact with them (augmented reality)




Course name: Basic Image Processing Algorithms Credits: 5

  • Class type: lecture/practical/lab
  • Hours per week: 2/1/1
  • Type of the exam: oral exam
  • Prerequisites (if exist): Stochastic Signals and Systems; Java Programming; Comprehensive Exam in Mathematics


The aim of the course is to give an introduction to the basic algorithms used in digital image processing.
Introduction to human vision, 
Digital image representations, sampling, color spaces, interpolation methods
Image properties: contrast, sharpness, histogram
Linear convolution, edge images, noise filtering, image enhancement
Fourier transformation and its applications
Image segmentation, morphological operations
Video processing: segmentation, object detection, object tracking, optical flow
Image compression methods
Shape and feature point descriptors

Required reading

W. K. Pratt: Digital Image Processing, Wiley, 2001.
Richard Szeliski, “Computer Vision. Algorithms and Applications.” Springer, London, 2011
MSeul, L. O’Gorman and M. J. Sammon, “Practical Algorithms for Image Analysis”, Cambridge University Press, Cambridge, 2012

Recommended reading:

  • Lecturer (name, position, degree): Dr. D├íniel Szolgay, assistant professor, PhD

The European Credit Transfer and Accumulation System (ECTS) is a student-centred system based on the student workload required to achieve the objectives of a programme of study. Its aim is to facilitate the recognition of study periods undertaken abroad by mobile students through the transfer credits. The ECTS is based on the principle that 60 credits are equivalent to the workload of full-time student during one academic year.