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	Author	Joan M. Nuñez; Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
	Title	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	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
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	Author	David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri
	Title	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	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	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
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	Author	Anton Cervantes; Gemma Sanchez; Josep Llados; Agnes Borras; Ana Rodriguez
	Title	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
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	Author	Anton Cervantes
	Title	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	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	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
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	Author	Xavier Perez Sala; Cecilio Angulo; Sergio Escalera
	Title	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
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	Author	Xavier Perez Sala; Cecilio Angulo; Sergio Escalera
	Title	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
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	Author	Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen
	Title	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
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	Author	Carlo Gatta; Petia Radeva
	Title	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
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	Author	Volkmar Frinken; Alicia Fornes; Josep Llados; Jean-Marc Ogier
	Title	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
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	Author	Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera
	Title	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
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