D1. Knowledge and comprehension skills: At the end of the course, the student must demonstrate that he or she knows the basic principles of the physical nature, formation, and structure of digital images. Must know the basic methods of analysis and processing digital images with particular regard to filtering, pattern recognition, and compression. D2. Ability to apply knowledge and understanding: At the end of the course, the student must be able to apply the knowledge acquired at point D1 to solve digital image analysis and processing exercises, using the NV5 IDL (Interactive Data Language) processing environment, by developing the relevant codes with a structured programming approach. D3. Autonomy of judgment: At the end of the course, the student will be able to judge the basic methods for the analysis and processing of digital images. It must be able to operate independently using the classical basic methods. He/she must propose ideas and solutions for choosing the most appropriate method to pursue a given objective. D4. Communicative skills: At the end of the course, the student must be able to clearly explain the concepts acquired in point D1, know how to document the codes developed, and present the result clearly. It must also be able to intervene in a critical discussion on course topics by giving valid suggestions. D5. Learning ability: At the end of the course the student must be able to deepen the topics covered, and he/she must also be able to transfer the knowledge acquired into subsequent courses, to design and propose basic processes of analysis and processing of digital images and develop the corresponding code in IDL language according to the rules of structured programming.

Basic concepts of programming. Basic Math for Physics.

Formation and structure of digital images. Visualization, processing and morphological analysis. Practicums in the computer lab.

The course materiai is distributed in PDF or Powerpoint format. A reference textbook is: "Digital lmage Processing", R.C. Gonzalez and R.E. Woods, Prentice Hall (2002) [Available in the Library of the Department of Physics].

Theoretical lessons
1. Two-dimensional images, matrices of numerical data, pixels, coordinate systems. Data structure, indexed images, intensity, binary, RGB, series of pictures. Conversion of image type. Import and export images.
2. Visualization, transfer function in intensity, false color, true color, RGB and HSV color space. Graphical display of images, isophotes, axonometric. Animation of series of images.
3. Space operations on images, the transformation of the coordinate system, the transformation of size, scale transformation, rotation, projections.
4. Photometric operations on images, photometric transformations on images, transformations of the transfer function in intensity, false color and true color, quantization of intensity, color quantization, equalization of 'histogram of intensity.
5. lmage compression without degradation, run-length encoding and LZW (notes). lmage compression with degradation by discrete Fourier transform, discrete cosine transform, discrete wavelet transform. lmage compression using fractal coding (notes).
6. lmage processing, linear and nonlinear operators, extraction of contours, localization and two-dimensional correlation of morphological details. Processing of binary images, Radon transform and extraction of dominant directions, estimates of perimeter and area, operators of dilation and erosion.
7. Processing the Signal / Noise ratio of images, two-dimensional Fourier transform, Fourier analysis of an image, two-dimensional convolution, local operators and process block, linear filters, median operator, non­linear filters.
8. Deconvolution linear and non-linear image.
9. lmage analysis using non-linear fit to the form known (notes).
10. Morphological classification of images.
11. Morphological classification techniques based on Deep learning.

Tutorials
1. lntroduction to the software IDL (lnteractive Data Language) of Exelis.
2. The array image in IDL
3. Visualization and characterization of images
4. Binarization using threshold level
5. Type conversion
6. Analysis of RGB colors in an image True Color
7. Viewing the palette of an indexed image
8. Viewing a pseudo-2-dimensional image
9. Viewing a pseudo-3-dimensional image
10. Determination of the Physical Scale an image
11. Transformations and Spatial lnterpolation Bilinear
12. Edit Histogram
13. Fourier analysis of an image
14. Filtering in the Fourier domain (Ideai and Butterworth)
15. Noise filtering of the Fourier Domain (Low Pass Filter)
16. Noise filtering in the Fourier domain (Band Pass Filter)
17. Compression with Low-Pass Filter in the Doma in Fourier Transform (DFT and DCT)
18. Pattern Recognition with Template Matching
19. Filtering by Convolution Operators with Linear
20. Morphological Analysis
21. Orientation identification with Radon Transform
22. Linear deconvolution (Fourier-Wiener) and Non-Linear (Richardson­Lucy)
23. lntroduction to Object Programming in IDL

Lectures and practicums in the computer lab.

"Poropat" Computer Lab (Building F, II floor);
Mon., Tue. 14-17
(Temporary)

Oral exam on the course topics and presentation and discussion of a code for image processing.