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