Checking date: 31/03/2023

Course: 2023/2024

Computer Vision
Master in Machine Learning for Health (Plan: 480 - Estudio: 359)

Coordinating teacher: GONZALEZ DIAZ, IVAN

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Machine Learning Statistical Signal Processing Biomedical Image Processing Deep learning
Students are expected to achieve the following goals: - Learn how images are formed both in the human visual system and in digital cameras, attending both to photometric and geometric dimensions. - Know well-known algorithms that implement processes of human vision: extraction of visual characteristics, estimation of movement, stereopsis, image registration, object tracking, visual recognition. - Apply the knowledge acquired in previous related subjects (e.g. machine learning, deep learning) to the field of computer vision. - Solve practical problems related to computer vision - Design and develop a scientific-technical project that involves the use of computer vision techniques.
Skills and learning outcomes
Description of contents: programme
Block 1: image Formation Topic 1: Light, shading and color. Topic 2: Geometric Camera Models and Camera Calibration Block 2: Early Vision Topic 3: Local Invariant Features Topic 4: Motion Estimation and Optical Flow Topic 5: Stereopsis and Structure from Motion Block 3: Mid-level Vision Topic 6: Object Tracking Topic 7: Image Registration: rigid and deformable Block 4: High-level Vision Topic 8: Object Recognition & Image Classification with Convolutional Neural Networks Topic 9: Other applications of Deep Learning in images: object detection, segmentation, image matching, etc.
Learning activities and methodology
AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams Activity code total hours number presencial hours number % Student Presence AF3 134 134 100% AF4 42 42 100% AF5 24 0 0% AF6 120 0 0% AF7 248 0 0% AF8 16 16 100% SUBJECT TOTAL 600 184 30,66%
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Forsyth, Ponce. Computer Vision: A Modern Approach. Pearson. 2012
  • Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. The MIT Press, Cambrigde, Massachussetts, London, England. 2016
  • Richard Hartley & Andrew Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press. 2003
  • Richard Szeliski. Computer Vision: Algorithms and Applications. Springer-Verlag. 2011

The course syllabus may change due academic events or other reasons.