J
Jordi Vitrià
Researcher at University of Barcelona
Publications - 170
Citations - 3086
Jordi Vitrià is an academic researcher from University of Barcelona. The author has contributed to research in topics: Facial recognition system & Feature extraction. The author has an hindex of 28, co-authored 170 publications receiving 2877 citations. Previous affiliations of Jordi Vitrià include Autonomous University of Barcelona & Given Imaging Ltd..
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Journal ArticleDOI
Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes
TL;DR: A heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion is presented, validated using the UCI database and applied to a real problem, the classification of traffic sign images.
Journal ArticleDOI
Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification
TL;DR: A novel approach for the detection and classification of traffic signs that offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.
Journal ArticleDOI
Introducing a weighted non-negative matrix factorization for image classification
TL;DR: A comparison between NMF, WNMF and the well-known principal component analysis (PCA) in the context of image patch classification has been carried out and it is claimed that all three techniques can be combined in a common and unique classifier.
Book ChapterDOI
Non-negative Matrix Factorization for Face Recognition
David Guillamet,Jordi Vitrià +1 more
TL;DR: Non-negative Matrix Factorization (NMF) technique is introduced in the context of face classification and a direct comparison with Principal Component Analysis (PCA) is also analyzed.
Journal ArticleDOI
Nonparametric discriminant analysis and nearest neighbor classification
M. Bressan,Jordi Vitrià +1 more
TL;DR: It is observed that when the authors seek a linear representation adapted to improve NN performance, what they obtain not surprisingly is quite close to NDA.