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	Author	Michal Drozdzal; Santiago Segui; Carolina Malagelada; Fernando Azpiroz; Petia Radeva
	Title	Adaptable image cuts for motility inspection using WCE			Type	Journal Article
	Year	2013	Publication	Computerized Medical Imaging and Graphics	Abbreviated Journal	CMIG
	Volume	37	Issue	1	Pages	72-80
	Keywords
	Abstract	The Wireless Capsule Endoscopy (WCE) technology allows the visualization of the whole small intestine tract. Since the capsule is freely moving, mainly by the means of peristalsis, the data acquired during the study gives a lot of information about the intestinal motility. However, due to: (1) huge amount of frames, (2) complex intestinal scene appearance and (3) intestinal dynamics that make difficult the visualization of the small intestine physiological phenomena, the analysis of the WCE data requires computer-aided systems to speed up the analysis. In this paper, we propose an efficient algorithm for building a novel representation of the WCE video data, optimal for motility analysis and inspection. The algorithm transforms the 3D video data into 2D longitudinal view by choosing the most informative, from the intestinal motility point of view, part of each frame. This step maximizes the lumen visibility in its longitudinal extension. The task of finding “the best longitudinal view” has been defined as a cost function optimization problem which global minimum is obtained by using Dynamic Programming. Validation on both synthetic data and WCE data shows that the adaptive longitudinal view is a good alternative to the traditional motility analysis done by video analysis. The proposed novel data representation a new, holistic insight into the small intestine motility, allowing to easily define and analyze motility events that are difficult to spot by analyzing WCE video. Moreover, the visual inspection of small intestine motility is 4 times faster then by means of video skimming of the WCE.
	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; OR; 600.046; 605.203			Approved	no
	Call Number	Admin @ si @ DSM2012			Serial	2151
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	Author	Ernest Valveny; Robert Benavente; Agata Lapedriza; Miquel Ferrer; Jaume Garcia; Gemma Sanchez
	Title	Adaptation of a computer programming course to the EXHE requirements: evaluation five years later			Type	Miscellaneous
	Year	2012	Publication	European Journal of Engineering Education	Abbreviated Journal
	Volume	37	Issue	3	Pages	243-254
	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	DAG; CIC; OR; invisible;MV			Approved	no
	Call Number	Admin @ si @ VBL2012			Serial	2070
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	Author	Monica Piñol
	Title	Adaptative Vocabulary Tree for Image Classification using Reinforcement Learning			Type	Report
	Year	2010	Publication	CVC Technical Report	Abbreviated Journal
	Volume	162	Issue		Pages
	Keywords
	Abstract
	Address	Bellaterra (Barcelona)
	Corporate Author	Computer Vision Center			Thesis	Master's 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 @ Piñ2010			Serial	1936
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	Author	Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa
	Title	Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers			Type	Conference Article
	Year	2013	Publication	CVPR Workshop on Ground Truth – What is a good dataset?	Abbreviated Journal
	Volume		Issue		Pages	688 - 693
	Keywords	Pedestrian Detection; Domain Adaptation
	Abstract	Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.
	Address	Portland; oregon; June 2013
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	ADAS; 600.054; 600.057; 601.217			Approved	yes
	Call Number	XVR2013; ADAS @ adas @ xvr2013a			Serial	2220
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	Author	Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers
	Title	Adapting Pedestrian Detection from Synthetic to Far Infrared Images			Type	Conference Article
	Year	2013	Publication	ICCV Workshop on Visual Domain Adaptation and Dataset Bias	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Domain Adaptation; Far Infrared; Pedestrian Detection
	Abstract	We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes.
	Address	Sydney; Australia; December 2013
	Corporate Author				Thesis
	Publisher		Place of Publication	Sydney, Australy	Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICCVW-VisDA
	Notes	ADAS; 600.054; 600.055; 600.057; 601.217;ISE			Approved	no
	Call Number	ADAS @ adas @ SRV2013			Serial	2334
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	Author	M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer
	Title	Adaptive color attributes for real-time visual tracking			Type	Conference Article
	Year	2014	Publication	27th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1090 - 1097
	Keywords
	Abstract	Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.
	Address	Nottingham; UK; September 2014
	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	CVPR
	Notes	CIC; LAMP; 600.074; 600.079			Approved	no
	Call Number	Admin @ si @ DKF2014			Serial	2509
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	Author	V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich
	Title	Adaptive Correlation Filters for Pattern Recognition			Type	Journal
	Year	2006	Publication	Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	16	Issue	3	Pages	425-431
	Keywords	Pattern recognition, Correlation filters, A adaptive filters
	Abstract	Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance.
	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	ISE			Approved	no
	Call Number	ISE @ ise @ KMA2006a			Serial	673
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	Author	Monica Piñol; Angel Sappa; Ricardo Toledo
	Title	Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy			Type	Journal Article
	Year	2015	Publication	Neurocomputing	Abbreviated Journal	NEUCOM
	Volume	150	Issue	A	Pages	106–115
	Keywords	Reinforcement learning; Q-learning; Bag of features; Descriptors
	Abstract	This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach.
	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 @ PST2015			Serial	2473
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	Author	David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa
	Title	Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection			Type	Conference Article
	Year	2007	Publication	Proceedings of the 5th International Conference on Computer Vision Systems	Abbreviated Journal	ICVS
	Volume		Issue		Pages
	Keywords	Pedestrian Detection
	Abstract	On–board pedestrian detection is in the frontier of the state–of–the–art since it implies processing outdoor scenarios from a mobile platform and searching for aspect–changing objects in cluttered urban environments. Most promising approaches include the development of classifiers based on feature selection and machine learning. However, they use a large number of features which compromises real–time. Thus, methods for running the classifiers in only a few image windows must be provided. In this paper we contribute in both aspects, proposing a camera pose estimation method for adaptive sparse image sampling, as well as a classifier for pedestrian detection based on Haar wavelets and edge orientation histograms as features and AdaBoost as learning machine. Both proposals are compared with relevant approaches in the literature, showing comparable results but reducing processing time by four for the sampling tasks and by ten for the classification one.
	Address	Bielefeld (Germany)
	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 @ gsl2007a			Serial	786
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	Author	J.R. Serra; J.B. Subirana
	Title	Adaptive non-cartesian networks for vision.			Type	Miscellaneous
	Year	1997	Publication	IX International Conference on Image Analysis and Processing.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Florence
	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 @ SeS1997			Serial	212
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	Author	Miguel Reyes; Jose Ramirez Moreno; Juan R Revilla; Petia Radeva; Sergio Escalera
	Title	ADiBAS: Sistema Multisensor de Adquisicion Automatica de Datos Corporales Objetivos, Robustos y Fiables para el Analisis de la Postura y el Movimiento			Type	Conference Article
	Year	2011	Publication	6th Congreso Iberoamericano de Tecnologia de Apoyo a la Discapacidad	Abbreviated Journal
	Volume		Issue		Pages	939-944
	Keywords
	Abstract	El análisis de la postura y del rango de movimiento son fundamentales para conocer la optimización del gesto y mejorar, de este modo, el rendimiento y la detección de posibles lesiones. Esta cuantificación es especialmente interesante en deportistas o en pacientes que presentan alguna lesión neurológica o del sistema musculo-esquelético, ya que permite conocer el proceso evolutivo de estos pacientes, evaluar la eficacia de la terapia aplicada y proponer, en caso necesario, una modificación del protocolo de tratamiento. En este trabajo presentamos un sistema automático que permite, mediante una tecnología no invasiva, la captación automática de marcadores LED situados sobre el paciente y su posterior análisis con el fin de mostrar al especialista datos objetivos que permitan un mejor soporte diagnóstico. También se describe un sistema analítico de la postura corporal sin marcadores, donde su ejecución durante secuencias dinámicas aporta un alto grado de naturalidad al paciente a la hora de realizar los ejercicios funcionales.
	Address	Palma de Mallorca
	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	IBERDISCAP
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ RRR2011			Serial	1768
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	Author	Maya Dimitrova; Petia Radeva; David Rotger; D. Boyadjiev; Juan J. Villanueva
	Title	Advanced Cardiological Diagnosis via Intelligent Image Analysis			Type	Miscellaneous
	Year	2004	Publication		Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Varna (Bulgaria)
	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 @ DRR2004			Serial	477
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	Author	David Rotger; Cristina Cañero; Petia Radeva; J. Mauri; E. Fernandez; A. Tovar; V. Valle
	Title	Advanced Visualization of 3D data of Intravascular Ultrasound Images.			Type	Miscellaneous
	Year	2001	Publication	Medical Data Analysis, Second International Symposium, ISMDA 2001, 245–250.	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	MILAB			Approved	no
	Call Number	BCNPCL @ bcnpcl @ RCR2001b			Serial	157
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	Author	Debora Gil; Antoni Rosell
	Title	Advances in Artificial Intelligence – How Lung Cancer CT Screening Will Progress?			Type	Abstract
	Year	2019	Publication	World Lung Cancer Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Invited speaker
	Address	Barcelona; September 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	IASLC WCLC
	Notes	IAM; 600.139; 600.145			Approved	no
	Call Number	Admin @ si @ GiR2019			Serial	3361
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	Author	Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li
	Title	Advances in Face Presentation Attack Detection			Type	Book Whole
	Year	2023	Publication	Advances in Face Presentation Attack Detection	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	HUPBA			Approved	no
	Call Number	Admin @ si @ WGE2023a			Serial	3955
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