Master in Image Processing and Computer

The Master's program is built upon existing curricula in information technology, engineering, applied mathematics, and computer science. These four fields have been gathered together to propose an excellent and well-designed curriculum. Each semester has its objective:

• The 1st semester at PPKE Budapest provides all students with fundamental leveraging knowledge in visual processing, numerical analysis, machine learning, high-performance hardware computing architectures, and programming methodologies.
• The 2nd semester at UAM Madrid deepens my knowledge in visual signal processing and machine/deep learning, both in the area of DL-based IPCV, and high-performance green computing.
• The 3rd semester at UBx Bordeaux provides courses on further advances in visual signal processing and machine/deep learning and focuses on the development of computer vision applications in different specific areas.
• The 4th semester is dedicated to an internship at a company or research laboratory that can be selected among our network of private- and public-sector partners. It ends with the Master thesis defence, and a final oral exam.

Courses

First semester: PPKE, Budapest

• Basic Image Processing Algorithms
• Biomedical Signal Processing
• Data Mining and Machine Learning
• Design Patterns
• High-level Synthesis Methods on FPGA-s
• Intelligent Sensors
• Multimodal Sensor Fusion and Navigation
• Numerical Analysis
• Parallel Computing Architectures
• Signal Processing
• Sports

TOTAL (offered/coursed) : 39/30

Second semester: UAM, Madrid

• 3D Vision for Multiple or Moving Cameras
• Deep Learning for Visual Signal Processing I
• Deep Learning for Visual Signal Processing II
• High Performance Computing for Deep Learning
• Initiation to Research
• Tutored Research & Development Project I

TOTAL (offered/coursed) : 30/30

Third semester: UB, Bordeaux

• Acquisition and Reconstruction
• Augmented and Virtual Reality
• Career Development Week
• Deep Learning in Computer Vision
• Inversion for Augmented Reality
• Management of IT Projects
• Reconstruction and Inverse Problems
• Tutored Research and Development II
• Variational Methods and PDEs and Optimisation for Image Processing

TOTAL (offered/coursed) : 30/30

Fourth semester

Internship at a company or research laboratory

TOTAL (offered/coursed) : 30/30

All students must validate 30 ECTS per semester.

The European Credit Transfer and Accumulation System (ECTS) is a student-centered system based on the student workload required to achieve the objectives of a program of study. It aims to facilitate the recognition of study periods undertaken abroad by mobile students through transfer credits. The ECTS is based on the principle that 60 credits are equivalent to the workload of a full-time student during one academic year.

The IPCVai Master programme trains interdisciplinary specialists in image processing and computer vision. Our graduates can adapt to the various application domains of our field, which can be as diverse as large-scale multimedia indexing, event detection on wearable cameras, image editing, medical imaging, augmented reality, and robotics. They can propose innovative solutions in these domains, using AI techniques in a sustainable and controlled manner.

Scientific learning outcomes

• Understanding of mathematical formulations of an image processing problem;
• Analysis of imaging problems, knowledge of the relevant state-of-the-art;
• Detailed understanding of the results of AI methods in computer vision;
• Analysis of the environmental impact of computer vision algorithms;
• Good programming skills in Python, C++, Matlab, and key open-source libraries and programming environments;
• Applications: ability to design, implement, and deploy on different targets including low-resource computer architectures;

Interdisciplinary competences

• Ability to work in the whole processing chain: acquisition, analysis, and visualization, within different application areas (e.g. medicine, privacy preservation, movie post-production, augmented reality, robotics, embedded systems for digital agriculture);
• Implementing a variety of classical and state-of-the-art image processing and computer vision algorithms;
• Awareness of the difficulties that may arise when facing heterogeneous technical problems;
• Awareness of the challenges of green computing and trustworthy AI;
• Proposing innovative algorithms to solve new problems arising as visual technologies evolve.

Transferable skills

• Project management, including strategic planning, coordination, and technical development;
• Foundations of entrepreneurship;
• Understanding of tech transfer and patenting;
• Writing and Presentation skills;
• Technical decision-making: ability to formulate correct opinions and reviews;
• Scientific ethics, intellectual property;
• Intercultural awareness, gained through projects where students work in pairs, the “local buddy” system, and language courses.