Machine learning for computer vision [43083]
Vanrell i Martorell, Maria Isabel (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Serrat, Joan
Vázquez, David
López, Antonio
Vilariño, Fernando
Marin, Javier
Pujol, Oriol
Masip, David
Camps, Gustavo
Álvarez, Jose Manuel
Universitat Autònoma de Barcelona. Escola d'Enginyeria

Date: 2015-16
Abstract: Machine learning deals with the automatic analisys of large scale data. Nowadays it conforms the basics of many computer vision methods, specially those related to visual pattern recognition or classification, where 'patterns' encompasses images of world objects, scenes and video sequences of human actions, to name a few. This module presents the foundations and most important techniques for classification of visual patterns, focusing on supervised methods. Also, related topics like image descriptors and dimensionality reduction are addressed. As much as possible, all these techniques are tried and assessed on a practical project concerning traffic sign detection and recognition, toghether with the standard metrics and procedures for performance evaluation like precision-recall curves and k-fold cross-validation. The learning outcomes are: (a) Distinguish the main types of ML techniques for computer vision: supervised vs. unsupervised, generative vs. discriminative, original feature space vs. feature vector kernelization. (b) Know the strong and weak points of the different methods, in part learned while solving a real pattern classification problem. (3) Being able to use existing method implementations and build them from scratch.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Language: Anglès.
Study plan: Visió per Computador / Computer Vision [1172]



Anglès
3 p, 138.4 KB

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 Record created 2016-02-09, last modified 2018-12-22



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