Master's Degree in Computer Vision

The study plan consists of 15 subjects, including external internships and the master's thesis (TFM). The result is an academic offer of 105 ECTS (30 ECTS for TFM, 3 ECTS for external internships, 48 ​​ECTS for compulsory subjects and 24 for electives). To obtain the Master in Computer Vision, the student must pass 90 ECTS.

The Master is organized into 6 modules, three of them aimed at acquiring skills in transversal computer vision technologies and therefore applicable to a large number of domains; Two other modules focused on the specific technologies and methodologies of two large groups of applications: industrial and engineering applications and biomedical imaging applications; and the TFM module.

Teaching will generally be developed by combining face-to-face and distance learning (mostly), through master classes with both theoretical and practical components (hands-on), in which students will use computer tools to consolidate the learning of concepts and techniques. The development of teaching will be complemented with integrated teaching methodologies in which cooperative and project-based learning activities will be developed.

In remote education it is important to combine the use of synchronous media (videoconferencing) with asynchronous media (virtual classrooms). The course material will be available sufficiently in advance so that students can know in advance the activities to be carried out, the initial content on which they are based, the recommended readings, the associated calendar of activities and the monitoring and evaluation procedure.

For academic tutoring, the same mechanisms can be used through general-purpose video conferencing tools, combined with email and telephone. Work outside the classroom will include self-study activities, supervised work, problem solving, and participation in discussion forums on the virtual platform.

Its multidisciplinary nature is based on the fact that (i) many of its results are inspired by and feed back on results in Neuroscience, (ii) the complexity of the problems from both a geometric and a statistical and probabilistic point of view demand good training in Mathematics, (iii) ) the photometric dimension of images, the resolution of poorly conditioned problems, multispectral analysis, or the sources of noise in images, are a field for Physics, (iv) the technologies for cameras, communications, and hardware come from various Engineering, ( v) and the computational models that are required for processing and learning from large amounts of data, allow the development of new paradigms within Computing.

On the other hand, its high technological potential is evident from the fact that it is a discipline that allows rapid applicability of all its theoretical results, which makes it a transversal engineering that can be integrated into multiple systems of diverse applications.

Thus, we are faced with a technological sector that requires a high degree of training of its professionals and whose scientific interest advances at great speed. The interest at an academic level occurs on two fronts, on the one hand there are students who have just completed their degree studies and seek greater specialization before entering the labor market. On the other hand, there are multiple research groups dedicated to computer vision that require a master's degree in this field that allows them to train students who intend to write a doctoral thesis.

The recommended income profile is:

• Mathematical training equivalent to at least a degree in Engineering.
• Knowledge of programming in languages such as C/C++ or Java, or prototyping such as Matlab or Python.
• Knowledge of English for understanding, writing and speaking, at least equivalent to level B2 of the European framework of reference for languages of the Council of Europe.

This master's degree, with an academic profile, with a practical and applied approach (enhanced with a TFM of 30 ECTS, a minimum requirement in accordance with Portuguese regulations), provides skills and experience that allow knowledge to be applied immediately to generate both highly trained professionals, with abilities to generate immediate benefit for the industry, as professionals with entrepreneurial capacity, or researchers who intend to begin doctoral studies in a growing scientific field. Upon completion of the training, students are expected to be competent in:

• Reading and understanding current research publications on computer vision techniques.
• Use of fundamental tools commonly used to develop computer vision applications.
• Implementation of computer vision applications based on state-of-the-art algorithms.
• Conduct experimental analyzes and tests consistent with current practice in computer vision, including standard metrics and reference data sets.
• Application of mathematical and machine learning tools, such as geometry, optimization, and statistics to computer vision applications.