Mathematical Methods in Image Processing


Lecturer: Asst. Prof. Sven Loncaric

Summary

Digital image processing and analysis studies methods and algorithms for processing and interpretation of multidimensional images by means of a digital computer. Complexity of this area is evident from the fact that image analysis requires intelligence. Applications of image processing and analysis include automated visual inspection, medical image processing and analysis, motion detection and analysis, intelligent traffic systems and autonomous vehicles, face recognition, person authentication, remote sensing, computer and robot vision. The aim of the course is to present mathematical methods for image processing and analysis.

Course outline

Foundations of signal theory and linear system theory. Image representation. Image transformation. Image enhancement. Image restoration. Image reconstruction from projections. Image Segmentation. Shape analysis. Mathematical morphology. Image coding. Detection and estimation theory. Pattern recognition. Energy minimization techniques. Physics-based deformable models. Texture analysis. Motion analysis. Image registration. Stereo vision. Evolutionary algorithms. Neural networks.

Literature

  1. A. K. Jain: Fundamentals of Digital Image Processing, Prentice Hall, Englewood Cliffs, 1989
  2. R. M. Haralick, L.G.Shapiro: Computer and Robot Vision, vol. 1, 2, Addison-Wesley, 1992
  3. M. Sonka, V. Hlavac, R. Boyle: Image Processing, Analysis, and Machine Vision, 2nd Ed., Brooks/Cole, 1999

Electronic publications (in Croatian language):

  1. S. Loncaric, Mathematical Methods in Image Processing: Lecture notes, HTML document, Zagreb, 2000.


Dr. Sven Loncaric