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Author
Xavier Otazu; Oriol Pujol
Title
Wavelet based approach to cluster analysis. Application on low dimensional data sets
Type
Journal Article
Year
2006
Publication
Pattern Recognition Letters
Abbreviated Journal
PRL
Volume
27
Issue
14
Pages
1590–1605
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Notes
MILAB; CIC; HuPBA
Approved
no
Call Number
BCNPCL @ bcnpcl @ OtP2006
Serial
658
Permanent link to this record
Author
Cristina Cañero; Petia Radeva
Title
Vesselness enhancement diffusion
Type
Journal Article
Year
2003
Publication
Pattern Recognition Letters
Abbreviated Journal
PRL
Volume
24
Issue
16
Pages
3141–3151
Keywords
Abstract
IF: 0.809
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Notes
MILAB
Approved
no
Call Number
BCNPCL @ bcnpcl @ CaR2003
Serial
371
Permanent link to this record
Author
Oriol Rodriguez-Leor; J. Mauri; Eduard Fernandez-Nofrerias; Antonio Tovar; Vicente del Valle; Aura Hernandez-Sabate; Debora Gil; Petia Radeva
Title
Utilizacion de la estructura de los campos vectoriales para la deteccion de la Adventicia en imagenes de Ecografia Intracoronaria
Type
Journal
Year
2004
Publication
Revista Española de Cardiología
Abbreviated Journal
REC
Volume
57
Issue
2
Pages
100
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
SEC
Notes
MILAB;IAM
Approved
no
Call Number
BCNPCL @ bcnpcl @ RMF2004
Serial
566
Permanent link to this record
Author
Oriol Rodriguez-Leor; J. Mauri; Eduard Fernandez-Nofrerias; Antonio Tovar; Vicente del Valle; Aura Hernandez-Sabate; Debora Gil; Petia Radeva
Title
Utilización de la Estructura de los Campos Vectoriales para la Detección de la Adventicia en Imágenes de Ecografía Intracoronaria
Type
Journal Article
Year
2004
Publication
Revista Internacional de Enfermedades Cardiovasculares Revista Española de Cardiología
Abbreviated Journal
Volume
57
Issue
2
Pages
100
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
SEC
Notes
IAM;MILAB
Approved
no
Call Number
IAM @ iam @ RMF2004
Serial
1642
Permanent link to this record
Author
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls
Title
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification
Type
Journal Article
Year
2016
Publication
IEEE Transaction on Geoscience and Remote Sensing
Abbreviated Journal
TGRS
Volume
54
Issue
3
Pages
1349 - 1362
Keywords
Abstract
This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0196-2892
ISBN
Medium
Area
Expedition
Conference
Notes
LAMP; 600.079;MILAB
Approved
no
Call Number
Admin @ si @ RGC2016
Serial
2723
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