Checking date: 28/04/2020

Course: 2019/2020

Computer Vision
Study: Master in Information Health Engineering (359)

Coordinating teacher: GONZALEZ DIAZ, IVAN

Department assigned to the subject: Department of Signal and Communications Theory

Type: Electives
ECTS Credits: 6.0 ECTS


Students are expected to have completed
Machine Learning Statistical Signal Processing Biomedical Image Processing Deep learning
Competences and skills that will be acquired and learning results.
Basic competences CB6 Having and understanding the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context CB7 Students know how to apply their acquired knowledge and problem-solving skills in new or unfamiliar settings within broader (or multidisciplinary) contexts related to their field of study. CB8 Students are able to integrate knowledge and to face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments. CB9 Students know how to communicate their conclusions and the knowledge and ultimate reasons behind them to specialised and non-specialised audiences in a clear and unambiguous way. CB10 Students have the learning skills that will enable them to continue studying in a way that will be largely self-directed or autonomous. General competences CG2 Ability to apply the knowledge of skills and research methods related to engineering. CG3 Ability to apply the knowledge of research skills and methods related to Life Sciences. CG4 Ability to contribute to the widening of the frontiers of knowledge through an original research, part of which merits publication referenced at an international level. CG5 Ability to perform a critical analysis and an evaluation and synthesis of new and complex ideas. CG6 Ability to communicate with the academic and scientific community and with society in general about their fields of knowledge in the modes and languages commonly used in their international scientific community. Specific competences CE6 Ability to understand the basis of the main technologies involved in biomedical imaging systems. CE7 Ability to solve a biomedical problem from an engineering perspective based on the acquisition and processing of biomedical images
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 Topic 8: Surface Estimation Block 4: High-level Vision Topic 9: Object Recognition & Image Classification with Convolutional Neural Networks Topic 10: 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
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 and the academic weekly planning may change due academic events or other reasons.