Expected Learning Outcomes:

General educational objectives of teaching in terms of expected learning outcomes:

Knowledge and understanding: the objective of the course is for students to acquire knowledge that enables them to understand basic concepts about digital images and videos, as well as their processing for accurate analysis of low-level discriminative features useful for solving common tasks (face recognition; face detection; etc.)
Ability to apply knowledge and understanding (applying knowledge and understanding): the student will acquire the necessary skills for processing and analyzing multimedia content. In this regard, part of the course will consist of laboratory lectures, with practical examples of programs written in Python language and use of ad-hoc libraries (mainly OpenCV)
Autonomy of judgment (making judgements): The student will be able to independently develop solutions in writing algorithmic solutions for the analysis of mutlimedia content.
Communication skills (communication skills): the student will acquire the main skills in the use of technical language in the general area of digital images and video and the basic concepts of computer vision.
Learning skills: the course aims, as an objective, to provide the student with the necessary theoretical and practical methodologies to be able to address and solve independently new problems that may arise during a work activity. To this end, various topics will be covered in class involving the student in the search for possible solutions to real problems.

Detailed Course Content:

The first part of the course is about digital images:

Introduction to digital images + Lab. Session
Interpolation operations: replication, bilinear and bicubic + Lab. Session
Space domain and Frequency domain + Lab. Session
Fourier and DCT Transform + Lab. Session
The convolution and convolution theorem
Lossy and lossless compression
The JPEG standard
Mathematical morphology applied to digital images
Mathematical morphology applied to gray-scale images + Lab. Session
Image restoration and Noise models + Lab. Session
Filters: arithmetic, geometric, harmonic and counter-harmonic mean + Lab. Session
Median, minimum, maximum, midpoint, + Lab. Session
Adaptive filters + Lab. Session
Periodic noise. Noise removal in the frequency domain + Lab. Session
Filtering in the spatial domain. Edge detector. Canny's algorithm + Lab. Session
Steganography and Steganalysis + Lab. Session

The second part of the course is about digital video:

Introduction to digital video and main definition: aspect ratio, resolution, video file format.
Lab. Session: OpenCV and Digital Video
Video formats (MPEG-1, MPEG-2, MPEG-4, H.264).

The third part of the course covers Low-Level Vision:

Filters and Features: Edges, Textures, Laplacian Pyramid, Corner Detection (Harris, ...), SIFT.
Computer Vision applications: face detection and recognition, etc..

The fourth part of the course concerns modeling and processing of digital data:

Data modeling (features extracted from multimedia contents) and classification tasks

Laboratory Session: