The goal of the course is to provide the student with an understanding of the fundamental Digital Image Processing concepts, some ability to use the basic tools to process static images and video sequences, and a general view of the main image processing applications. At the end of the course, the student will know (PO a): ¿ The mathematical and statistical foundations of Digital Image Processing, emphasizing linear processing. ¿ The main sources of image degradation, and the possibilities of image restoration techniques. ¿ The basic components of image analysis and image understanding systems. ¿ Elementary strategies for image segmentation, feature extraction, morphological processing and object recognition. ¿ The basic tools for processing image sequences. In relation to specific abilities, at the end of the course the student will be able to: ¿ Use a specific software environment to implement image processing algorithms. (PO b) ¿ Implement and use basic processing tools: linear and non-linear local processing, image transform, morphological operators, basic methods for feature extraction and segmentation. (PO b) ¿ Solve complex image processing problems through the combination of basic processing blocks. (PO e) ¿ Designing strategies and image processing algorithms for solving specific problems. (PO e) Finally, the student will achieve (new) or enhance (existing) general capabilities to ¿ Analize problems and synthesize solutions. ¿ Apply knowledge to engineering practice. ¿ Decompose complex problems and tasks in a structured collection of simpler ones. ¿ Integrate multidisciplinary knowledge. ¿ Work autonomously and cooperatively. ¿ Make decision designs. ¿ Make oral presentations

0. Introduction. Applications of Digital Image Processing 1. The digital image. 2. Basic Image Processing 2.1 Pointwise transformations. 2.2 Geometric transformations. 2.3 Filtering 2.4 Image Processing in the Frequency Domain 3. Edge detection 4. Image restoration 5. Segmentation 6. Morphological image processing 7. Image Descriptors 8. Classification methods 9. Introduction to CNNs and their applications in image processing

The course will be organized around two types of class sessions: theoretical and laboratory. THEORY The theoretical classes will consist of conventional oral sessions by the teacher, with the aim to present and discuss the fundamental concepts and tools for digital image processing, providing the students with the opportunity to ask and resolve whatever questions arise during learning. Slides, blackboard and software demonstrations will be used to support the session. LAB SESSIONS At least one lab session per week will take place, with the following objectives (1) to get some skills in the use of an Image Processing Software (in particular, Matlab + Image Processing Toolbox), (2) use the image processing tools to visualize the eficacy of the methods discussed during the theoretical sessions, (3) solve simple image processing problems, and (4) complete a final lab project oriented to solve a complex problem.