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Author Joan M. Nuñez; Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
Title (down) Blood Vessel Characterization in Colonoscopy Images to Improve Polyp Localization Type Conference Article
Year 2013 Publication Proceedings of the International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume 1 Issue Pages 162-171
Keywords Colonoscopy; Blood vessel; Linear features; Valley detection
Abstract This paper presents an approach to mitigate the contribution of blood vessels to the energy image used at different tasks of automatic colonoscopy image analysis. This goal is achieved by introducing a characterization of endoluminal scene objects which allows us to differentiate between the trace of 2-dimensional visual objects,such as vessels, and shades from 3-dimensional visual objects, such as folds. The proposed characterization is based on the influence that the object shape has in the resulting visual feature, and it leads to the development of a blood vessel attenuation algorithm. A database consisting of manually labelled masks was built in order to test the performance of our method, which shows an encouraging success in blood vessel mitigation while keeping other structures intact. Moreover, by extending our method to the only available polyp localization
algorithm tested on a public database, blood vessel mitigation proved to have a positive influence on the overall performance.
Address Barcelona; February 2013
Corporate Author Thesis
Publisher SciTePress 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 800 Expedition Conference VISIGRAPP
Notes MV; 600.054; 600.057;SIAI Approved no
Call Number IAM @ iam @ NBS2013 Serial 2198
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Author David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri
Title (down) Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion Type Conference Article
Year 2007 Publication Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA Abbreviated Journal
Volume 163 Issue Pages 197–204
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 978-1-58603-798-7 Medium
Area Expedition Conference CCIA’07
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ RRF2007a Serial 831
Permanent link to this record
 
 
Author David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri
Title (down) Blood Detection in IVUS Images for 3D Volume of Lumen Changes Measurement Due to Different Drugs Administration Type Conference Article
Year 2007 Publication Computer Analysis of Images and Patterns, 12th International Conference Abbreviated Journal
Volume 4673 Issue Pages 285–292
Keywords
Abstract
Address Vienna (Austria)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-540-74271-5 Medium
Area Expedition Conference CAIP
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ RRF2007b Serial 832
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Author Sonia Baeza; Debora Gil; Carles Sanchez; Guillermo Torres; Ignasi Garcia Olive; Ignasi Guasch; Samuel Garcia Reina; Felipe Andreo; Jose Luis Mate; Jose Luis Vercher; Antonio Rosell
Title (down) Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung Type Conference Article
Year 2023 Publication SEPAR Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Pòster
Address Granada; Spain; June 2023
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 SEPAR
Notes IAM Approved no
Call Number Admin @ si @ BGS2023 Serial 3951
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Author Anton Cervantes; Gemma Sanchez; Josep Llados; Agnes Borras; A. Rodriguez
Title (down) Biometric Recognition Based on Line Shape Descriptors Type Conference Article
Year 2005 Publication Sixth IAPR International Workshop on Graphics Recognition (GREC 2005) Abbreviated Journal
Volume Issue Pages 335–344
Keywords
Abstract
Address Hong Kong (China)
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 Approved no
Call Number DAG @ dag @ CSL2005 Serial 596
Permanent link to this record
 
 
Author Anton Cervantes; Gemma Sanchez; Josep Llados; Agnes Borras; Ana Rodriguez
Title (down) Biometric Recognition Based on Line Shape Descriptors Type Book Chapter
Year 2006 Publication Lecture Notes in Computer Science Abbreviated Journal
Volume 3926 Issue Pages 346–357,
Keywords
Abstract Abstract. In this paper we propose biometric descriptors inspired by shape signatures traditionally used in graphics recognition approaches. In particular several methods based on line shape descriptors used to iden- tify newborns from the biometric information of the ears are developed. The process steps are the following: image acquisition, ear segmentation, ear normalization, feature extraction and identification. Several shape signatures are defined from contour images. These are formulated in terms of zoning and contour crossings descriptors. Experimental results are presented to demonstrate the effectiveness of the used techniques.
Address
Corporate Author Thesis
Publisher Springer Link 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 Approved no
Call Number DAG @ dag @ CSL2006 Serial 685
Permanent link to this record
 
 
Author Anton Cervantes
Title (down) Biometric Newborn Identification Type Report
Year 2005 Publication CVC Technical Report #87 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address CVC (UAB)
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 Approved no
Call Number Admin @ si @ Cer2005 Serial 574
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Author Arash Akbarinia; C. Alejandro Parraga
Title (down) Biologically Plausible Colour Naming Model Type Conference Article
Year 2015 Publication European Conference on Visual Perception ECVP2015 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Poster
Address Liverpool; UK; August 2015
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 ECVP
Notes NEUROBIT; 600.068 Approved no
Call Number Admin @ si @ AkP2015 Serial 2660
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Author Arash Akbarinia; C. Alejandro Parraga
Title (down) Biologically plausible boundary detection Type Conference Article
Year 2016 Publication 27th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.
Address York; UK; September 2016
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 BMVC
Notes NEUROBIT; 600.068; 600.072 Approved no
Call Number Admin @ si @ AkP2016a Serial 2867
Permanent link to this record
 
 
Author Xavier Perez Sala; Cecilio Angulo; Sergio Escalera
Title (down) Biologically Inspired Turn Control in Robot Navigation Type Conference Article
Year 2011 Publication 14th Congrès Català en Intel·ligencia Artificial Abbreviated Journal
Volume Issue Pages 187-196
Keywords
Abstract An exportable and robust system for turn control using only camera images is proposed for path execution in robot navigation. Robot motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames in the image sequence. This information is used to compute the instantaneous rotation angle. Finally, control loop is closed correcting robot displacements when it is requested for a turn command. The proposed system has been successfully tested on the four-legged Sony Aibo robot.
Address Lleida
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 978-1-60750-841-0 Medium
Area Expedition Conference CCIA
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ PAE2011a Serial 1753
Permanent link to this record
 
 
Author Xavier Perez Sala; Cecilio Angulo; Sergio Escalera
Title (down) Biologically Inspired Path Execution Using SURF Flow in Robot Navigation Type Conference Article
Year 2011 Publication 11th International Work Conference on Artificial Neural Networks Abbreviated Journal
Volume II Issue Pages 581--588
Keywords
Abstract An exportable and robust system using only camera images is proposed for path execution in robot navigation. Motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames, so the method is called SURF flow. This information is used to correct robot displacement when a straight forward path command is sent to the robot, but it is not really executed due to several robot and environmental concerns. The proposed system has been successfully tested on the legged robot Aibo.
Address Malaga
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21497-4 Medium
Area Expedition Conference IWANN
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ PAE2011b Serial 1773
Permanent link to this record
 
 
Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen
Title (down) Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification Type Journal Article
Year 2018 Publication ISPRS Journal of Photogrammetry and Remote Sensing Abbreviated Journal ISPRS J
Volume 138 Issue Pages 74-85
Keywords Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis
Abstract Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene
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 LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ RKW2018 Serial 3158
Permanent link to this record
 
 
Author Carlo Gatta; Petia Radeva
Title (down) Bilateral Enhancers Type Conference Article
Year 2009 Publication 16th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages 3161-3165
Keywords
Abstract Ten years ago the concept of bilateral filtering (BF) became popular in the image processing community. The core of the idea is to blend the effect of a spatial filter, as e.g. the Gaussian filter, with the effect of a filter that acts on image values. The two filters acts on orthogonal domains of a picture: the 2D lattice of the image support and the intensity (or color) domain. The BF approach is an intuitive way to blend these two filters giving rise to algorithms that perform difficult tasks requiring a relatively simple design. In this paper we extend the concept of BF, proposing the bilateral enhancers (BE). We show how to design proper functions to obtain an edge-preserving smoothing and a selective sharpening. Moreover, we show that the proposed algorithm can perform edge-preserving smoothing and selective sharpening simultaneously in a single filtering.
Address Cairo, Egypt
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 1522-4880 ISBN 978-1-4244-5653-6 Medium
Area Expedition Conference ICIP
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ GaR2009b Serial 1243
Permanent link to this record
 
 
Author Volkmar Frinken; Alicia Fornes; Josep Llados; Jean-Marc Ogier
Title (down) Bidirectional Language Model for Handwriting Recognition Type Conference Article
Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal
Volume 7626 Issue Pages 611-619
Keywords
Abstract In order to improve the results of automatically recognized handwritten text, information about the language is commonly included in the recognition process. A common approach is to represent a text line as a sequence. It is processed in one direction and the language information via n-grams is directly included in the decoding. This approach, however, only uses context on one side to estimate a word’s probability. Therefore, we propose a bidirectional recognition in this paper, using distinct forward and a backward language models. By combining decoding hypotheses from both directions, we achieve a significant increase in recognition accuracy for the off-line writer independent handwriting recognition task. Both language models are of the same type and can be estimated on the same corpus. Hence, the increase in recognition accuracy comes without any additional need for training data or language modeling complexity.
Address Japan
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-34165-6 Medium
Area Expedition Conference SSPR&SPR
Notes DAG Approved no
Call Number Admin @ si @ FFL2012 Serial 2057
Permanent link to this record
 
 
Author Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera
Title (down) Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions Type Conference Article
Year 2019 Publication Visual Recognition for Medical Images workshop Abbreviated Journal
Volume Issue Pages 406-415
Keywords
Abstract In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance.
Address Seul; Korea; October 2019
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 ICCVW
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ AAF2019 Serial 3324
Permanent link to this record