|
Lecturer(s)
|
-
Bartoněk Luděk, doc. Ing. Ph.D.
|
|
Course content
|
1.The principles of imaging theory and design of imaging systems (parametric trajectory decomposition of the primary field); 2.Scan and preprocessing image information measurement systems (modification of electrical signals from the CCD camera in real time); 3.Measurement coordinates, distances and angles, geometric transformations, 4.Spectral evaluation of image information (FFT reduction of time, frequency) 5.Evaluation optical measurement (analysis of geometrical and physical shape of an object, study of deformation); 6.Feature Recognition methods; 7.Structural Pattern Recognition (choice of primitives, a description of formal languages??, grammars, automata, parsing); 8.Neural networks; 9.Use visual programming products NIS, DIPS, NI IMAQ Vision Advanced cases the application moiré and speckle holography.
|
|
Learning activities and teaching methods
|
|
Monologic Lecture(Interpretation, Training)
|
|
Learning outcomes
|
General principles of theory of imaging and construction of imaging systems.
Evaluation Evaluate the particular methods and principles, explain the aspects and results concerning the given issue, integrate the knowledge, predict the solutions, evaluate the results and outcomes.
|
|
Prerequisites
|
unspecified
|
|
Assessment methods and criteria
|
Mark
<ul> <li> Knowledge within the scope of the course topics (examination) </ul>
|
|
Recommended literature
|
-
Duda R. O. et al. (2001). Pattern Classification, (2nd ed.). John Wiley, New York.
-
Gonzales R. C. et al. (2004). Digital Image Processing using MATLAB. Prentice Hall.
-
Gonzales, R. C., Woods, R. E. (2002). Digital Image Processing (2nd ed.). Prentice Hall.
-
Pratt, K. W. (2001). Digital image processing (3rd ed.). John Wiley, New York.
-
Šonka, Hlaváč, Boyle. (1998). Image Processing, Analysis and Machine Vision. PWS.
-
Zitová B., Flusser J. (2003). Image and Vision Computing, 21, pp. 977-1000.
|