The goals and outcomes of this unit include learn how to extract information from images so that it is possible to estimate 3D world and object structure, object motion (including velocities and displacements), object recognition, shapes and activities. The techniques that the student has to learn are based on learning and classification.
Introduction to probability, Fitting probability models, Learning and inference in vision, Regression and Classification models, Graphical Models, Models for chains, trees and grids, Models for shape, style and identity, Models for visual words
Algebra, Differential Calculus, Probability
Generic skills to reach
. Competence in analysis and synthesis; . Knowledge of a foreign language; . Competence to solve problems; . Critical thinking; . Creativity; . Competence in organization and planning; . Competence for working in group; . Adaptability to new situations; . Quality concerns; . Research skills; (by decreasing order of importance)
Teaching hours per semester
total of teaching hours
assessment implementation in 20172018 Assessment Evaluation is either performed based on Matlab computational projects (100%) or Matlab computational projects (50%) and a final test (50%): 100.0%
Bibliography of reference
Computer Vision: Models, learning and inference, Simon Prince
Cognitive Vision Systems: Sampling the Spectrum of Approaches (Lecture Notes in Computer Science), Henrik I. Christensen and Hans-Hellmut Nagel .
The Cognitive Neuroscience of Vision (Fundamentals of Cognitive Neuroscience), Martha J. Farah
Active Vision: The Psychology of Looking and Seeing (Oxford Psychology Series), John M. Findlay and Iain D. Gilchrist.
Pattern Recognition and Machine Learning, Christopher M. Bishop
Learning with Kernels, Bernhard Schlkopf and Alexander J. Smola
Teaching methods include lectures by the professor, presentations of specific topics by the students and also tutorial supervision.