**Image processing**

**Lecture
Curricula:**

Computer vision and applications. Vision system structure and
functions. Image acquisition systems.

Image formation and sensing. Camera model

Binary image processing: Simple Geometric Properties.

Binary image processing: Labeling, Contour Tracing, Polygonal
Approximation

Binary image processing: Mathematical Morphology

Grayscale image processing: Mathematical methods for grayscale
image processing, Statistic features of the grayscale images, Histogram
processing, Point Processing

Grayscale image processing: Convolution and Fourier Transform

Grayscale image processing: Noise in images

Grayscale image processing: Digital Filtering

Grayscale image segmentation: Edge based segmentation (first order
differential methods).

Grayscale image segmentation: Edge based segmentation (second
order differential methods, edge linking, contour closing,).

Stereo-vision
basics. Epipolar geometry. Depth computation.

Color images: Color models. Color based segmentation.

**Textbooks and
references:**

1.
Rafael C. Gonzalez and Richard E.
Woods, “Digital Image Processing”, 4th Edition, *Pearson,* March 30, 2017.

2.
Rafael C. Gonzalez, Richard E. Woods
and Steven L. Eddins , “Digital Image Processing Using MATLAB”, 2nd ed., Mc Graw Hill, 2010.

3.
Richard Szeliski,
Computer Vision: Algorithms and Applications, Springer, 2011

4.
David. A. Forsyth, Jean Ponce,
Computer Vision: A Modern Approach, Pearson, 2011

5.
E. Trucco,
A. Verri, “Introductory Techniques for 3-D Computer
Vision”, Prentice Hall, 1998.

6.
S. Nedevschi, R. Danescu,
F. Oniga, T. Marita, Tehnici de viziune
artificiala in conducerea
automata a autovehiculelor, Editura
UT Press, 2012

7.
S. Nedevschi, T. Marita, R. Danescu, F. Oniga, R. Brehar, I. Giosan, S. Bota, A. Ciurte, A. Vatavu, Image
Processing - Laboratory Guide, UT Press, 2016