Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Licenţă
Domeniul: Informatică
Programul de studii: Informatică - în limba engleză
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MII0017	Optimisations in Image Processing	

Semester	Hours: C+S+L	Category	Status	
6	2+0+2	speciality	optional	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
LUPŞA Radu	Ph.D.	Lect.	Computer Systems	*			

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
Learning the main methods in image representation and processing.	

IV. Course contents
1.	Physical quantities representing in an image and physical phenomenons involved in the aquisition process.
2.	Colors: physical fundamentals and representation systems (SPD, XYZ, Yxy, L*a*b*).
3.	Image representations: pixel maps, geometrical representations, transforms (Fourier, wavelets). Issues regarding discrete domain (aliasing problems) and discrete valued images. Multiresolution representations.
4.	Simple linear operations: translation, rotation, scaling; convolution: low-pas, high-pass, mid-pass filters.
5.	Non-linear operations: contrast changes; morphological operations: erosion, dilatation, opening, closure, median filter, hit-or-miss, skeleton.
6.	Image restoration: degradation model, Wiener filter.
7.	Image analysis: edge and feature detection, image segmentation.
8. Compression methods.	

V. Bibliography
1.	Aurel Vlaicu, Prelucrarea digitală a imaginilor, Ed. Albastră, 1997
2.	2. Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Addison-Wesley, 1992.	

VI. Thematic of didactic activities per weeks
Lectures:
1.	(week 1) Physical quantities and phenomenons involved in image aquisition.
2.	Colors: physical fundamentals and representations: SPD, XYZ, L*a*b*.
3.	(weeks 2-3) Grayscale images: continuous or discrete domain, continuous or discrete values. Fourier series and Fourier transform, sampling, aliasing, Shannon-Nyquist sampling theorem. 2D Fourier transform, Fourier series, DFT.
4.	(weeks 3-4) Linear operations - space and frequency domain perspectives: translation, rotation, scaling, convolution. Smoothing (low-pass) and sharpening (high- or mid-pass).
5.	(weeks 5-7) Morphological and other non-linear operations: Hystogram equalization, erosion, dilatation, opening, closure, median filter, hit-or-miss, medial axis transform (skeleton). Monotone morphological operations and operations that are invariant to contrast change.
6.	(weeks 7-8) Image restoration: inverse filter, Wiener filter.
7.	(weeks 8-9) Wavelets transforms and multiresolution analysis.
8.	(weeks 10-12) Image analysis techniques: edge detection (laplacian 0-crossing, Canny edge detector), contour detection (active contours), other feature detectors; image segmentation.
9.	(weeks 12-14) Image compression techniques.

Lab works:
•	L1. Accustomizing with an image processing library (ImageMagick++ or other, at student's choice).
•	L2-L3. Naïve rotation or scaling, for noticing aliasing effects. DFT analysis of images.
•	L4-L5. Linear operations.
•	L6-L7. Morphological operations.
•	L8-L9. Image restoration.
•	L10-L11. Wavelet transforms.
•	L12-L13. Image compression or image analysis.
•	L14. Project presentation.

VII. Didactic methods used

VIII. Assessment
25% for the lab woks throughout the semester.
75% for the project, at the final presentation
See also: http://www.cs.ubbcluj.ro/~rlupsa/edu/2008-imgproc/requirements.html.	

IX. Additional references
1.	http://www.cs.ubbcluj.ro/~rlupsa/edu/2008-imgproc/	

	Dean,							Chair head,

	Date,							Course responsible,
								Lect. LUPSA Radu, Ph.D.