toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
  Records Links
Author Enrique Cabello; Cristina Conde; Angel Serrano; Licesio Rodriguez; David Vazquez edit   pdf
openurl 
  Title (up) Empleo de sistemas biométricos para el reconocimiento de personas en aeropuertos Type Journal Article
  Year 2006 Publication Instituto Universitario de Investigación sobre Seguridad Interior (IUSI 2006) Abbreviated Journal  
  Volume Issue Pages  
  Keywords Surveillance; Face detection; Face recognition  
  Abstract El presente proyecto se desarrolló a lo largo del año 2005, probando un prototipo de un sistema de verificación facial con imágenes extraídas de las cámaras de video vigilancia del aeropuerto de Barajas. Se diseñaron varios experimentos, agrupados en dos clases. En el primer tipo, el sistema es entrenado con imágenes obtenidas en condiciones de laboratorio y luego probado con imágenes extraídas de las cámaras de video vigilancia del aeropuerto de Barajas. En el segundo caso, tanto las imágenes de entrenamiento como las de prueba corresponden a imágenes extraídas de Barajas. Se ha desarrollado un sistema completo, que incluye adquisición y digitalización de las imágenes, localización y recorte de las caras en escena, verificación de sujetos y obtención de resultados. Los resultados muestran, que, en general, un sistema de verificación facial basado en imágenes puede ser una ayuda a un operario que deba estar vigilando amplias zonas.  
  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 invisible;ADAS Approved no  
  Call Number ADAS @ adas @ CCS2006a Serial 1672  
Permanent link to this record
 

 
Author Fadi Dornaika; Angel Sappa edit  openurl
  Title (up) Evaluation of an Appearance-based 3D Face Tracker using Dense 3D Data Type Journal
  Year 2008 Publication Machine Vision and Applications Abbreviated Journal  
  Volume 19 Issue 5-6 Pages 427–441  
  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 ADAS Approved no  
  Call Number ADAS @ adas @ DoS2008b Serial 1018  
Permanent link to this record
 

 
Author Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title (up) Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot Type Journal Article
  Year 2012 Publication Journal of Intelligent and Robotic Systems Abbreviated Journal JIRC  
  Volume 68 Issue 2 Pages 185-208  
  Keywords  
  Abstract This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Netherlands Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0921-0296 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RAV2012 Serial 2150  
Permanent link to this record
 

 
Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri edit   pdf
doi  openurl
  Title (up) Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction Type Journal Article
  Year 2018 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 60 Issue 4 Pages 512-524  
  Keywords  
  Abstract This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model.
 
  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 DAG; ADAS; 600.086; 600.130; 600.121; 600.118 Approved no  
  Call Number Admin @ si @ DMH2018a Serial 3062  
Permanent link to this record
 

 
Author Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate edit  doi
openurl 
  Title (up) Feature Extraction by Using Dual-Generalized Discriminative Common Vectors Type Journal Article
  Year 2019 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 61 Issue 3 Pages 331-351  
  Keywords Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning  
  Abstract In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.  
  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 DAG; ADAS; 600.084 Approved no  
  Call Number Admin @ si @ DRR2019 Serial 3172  
Permanent link to this record
 

 
Author P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris Vintimilla; Angel Sappa edit  doi
openurl 
  Title (up) Feature Point Descriptors: Infrared and Visible Spectra Type Journal Article
  Year 2014 Publication Sensors Abbreviated Journal SENS  
  Volume 14 Issue 2 Pages 3690-3701  
  Keywords  
  Abstract This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.  
  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 ADAS;600.055; 600.076 Approved no  
  Call Number Admin @ si @ RCA2014a Serial 2474  
Permanent link to this record
 

 
Author Angel Sappa; M.A. Garcia edit  openurl
  Title (up) Generating compact representations of static scenes by means of 3D object hierarchies Type Journal
  Year 2007 Publication The Visual Computer, 23(2): 143–154 Abbreviated Journal  
  Volume Issue Pages  
  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 ADAS Approved no  
  Call Number ADAS @ adas @ SaG2007a Serial 798  
Permanent link to this record
 

 
Author Cesar de Souza; Adrien Gaidon; Yohann Cabon; Naila Murray; Antonio Lopez edit  doi
openurl 
  Title (up) Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models Type Journal Article
  Year 2019 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume Issue Pages  
  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 ADAS Approved no  
  Call Number Admin @ si @ SGC2019 Serial 3303  
Permanent link to this record
 

 
Author Xavier Boix; Josep M. Gonfaus; Joost van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez edit   pdf
url  doi
openurl 
  Title (up) Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation Type Journal Article
  Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 96 Issue 1 Pages 83-102  
  Keywords  
  Abstract The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.
 
  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 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes CAT;ISE;CIC;ADAS Approved no  
  Call Number Admin @ si @ BGW2012 Serial 1718  
Permanent link to this record
 

 
Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title (up) Hierarchical Adaptive Structural SVM for Domain Adaptation Type Journal Article
  Year 2016 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 119 Issue 2 Pages 159-178  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition.
 
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number Admin @ si @ XRV2016 Serial 2669  
Permanent link to this record
Select All    Deselect All
 |   | 
Details

Save Citations:
Export Records: