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	Author	Cristina Sanchez Montes; Jorge Bernal; Ana Garcia Rodriguez; Henry Cordova; Gloria Fernandez Esparrach
	Title	Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia			Type	Journal Article
	Year	2020	Publication	Gastroenterología y Hepatología	Abbreviated Journal	GH
	Volume	43	Issue	4	Pages	222-232
	Keywords
	Abstract	Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented.
	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	MV;			Approved	no
	Call Number	Admin @ si @ SBG2020			Serial	3404
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	Author	Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Fabio Ferreira; Isabelle Guyon; Sirui Hong; Frank Hutter; Rongrong Ji; Julio C. S. Jacques Junior; Ge Li; Marius Lindauer; Zhipeng Luo; Meysam Madadi; Thomas Nierhoff; Kangning Niu; Chunguang Pan; Danny Stoll; Sebastien Treguer; Jin Wang; Peng Wang; Chenglin Wu; Youcheng Xiong; Arber Zela; Yang Zhang
	Title	Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019			Type	Journal Article
	Year	2021	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	43	Issue	9	Pages	3108 - 3125
	Keywords
	Abstract	This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a “meta-learner”, “data ingestor”, “model selector”, “model/learner”, and “evaluator”. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free “AutoDL self-service.”
	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; no proj			Approved	no
	Call Number	Admin @ si @ LPX2021			Serial	3587
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	Author	Daniel Ponsa; Robert Benavente; Felipe Lumbreras; Judit Martinez; Xavier Roca
	Title	Quality control of safety belts by machine vision inspection for real-time production			Type	Journal Article
	Year	2003	Publication	Optical Engineering (IF: 0.877)	Abbreviated Journal
	Volume	42	Issue	4	Pages	1114-1120
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher	SPIE	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;ISE;CIC			Approved	no
	Call Number	ADAS @ adas @ PRL2003			Serial	399
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	Author	Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez
	Title	On-board camera extrinsic parameter estimation			Type	Journal Article
	Year	2006	Publication	Electronics Letters	Abbreviated Journal	EL
	Volume	42	Issue	13	Pages	745–746
	Keywords
	Abstract	An efficient technique for real-time estimation of camera extrinsic parameters is presented. It is intended to be used on on-board vision systems for driving assistance applications. The proposed technique is based on the use of a commercial stereo vision system that does not need any visual feature extraction.
	Address
	Corporate Author				Thesis
	Publisher	IEE	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 @ SGD2006a			Serial	655
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	Author	Jaume Amores
	Title	MILDE: multiple instance learning by discriminative embedding			Type	Journal Article
	Year	2015	Publication	Knowledge and Information Systems	Abbreviated Journal	KAIS
	Volume	42	Issue	2	Pages	381-407
	Keywords	Multi-instance learning; Codebook; Bag of words
	Abstract	While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data.
	Address
	Corporate Author				Thesis
	Publisher	Springer London	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0219-1377	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 601.042; 600.057; 600.076			Approved	no
	Call Number	Admin @ si @ Amo2015			Serial	2383
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	Author	Jose Antonio Rodriguez; Florent Perronnin
	Title	Handwritten word-spotting using hidden Markov models and universal vocabularies			Type	Journal Article
	Year	2009	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	42	Issue	9	Pages	2103-2116
	Keywords	Word-spotting; Hidden Markov model; Score normalization; Universal vocabulary; Handwriting recognition
	Abstract	Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ RoP2009			Serial	1053
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	Author	Ignasi Rius; Jordi Gonzalez; Javier Varona; Xavier Roca
	Title	Action-specific motion prior for efficient bayesian 3D human body tracking			Type	Journal Article
	Year	2009	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	42	Issue	11	Pages	2907–2921
	Keywords
	Abstract	In this paper, we aim to reconstruct the 3D motion parameters of a human body model from the known 2D positions of a reduced set of joints in the image plane. Towards this end, an action-speciﬁc motion model is trained from a database of real motion-captured performances. The learnt motion model is used within a particle ﬁltering framework as a priori knowledge on human motion. First, our dynamic model guides the particles according to similar situations previously learnt. Then, the solution space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, we are able to track the 3D conﬁguration of the full human body from several cycles of walking motion sequences using only the 2D positions of a very reduced set of joints from lateral or frontal viewpoints.
	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	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	ISE @ ise @ RGV2009			Serial	1159
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	Author	Miquel Ferrer; Ernest Valveny; F. Serratosa
	Title	Median Graphs: A Genetic Approach based on New Theoretical Properties			Type	Journal Article
	Year	2009	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	42	Issue	9	Pages	2003–2012
	Keywords	Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition
	Abstract	Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.
	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			Approved	no
	Call Number	DAG @ dag @ FVS2009b			Serial	1167
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	Author	Daniel Ponsa; Antonio Lopez
	Title	Variance reduction techniques in particle-based visual contour Tracking			Type	Journal Article
	Year	2009	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	42	Issue	11	Pages	2372–2391
	Keywords	Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling
	Abstract	This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done.
	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 @ PoL2009a			Serial	1168
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	Author	Oriol Pujol; Sergio Escalera; Petia Radeva
	Title	An Incremental Node Embedding Technique for Error Correcting Output Codes			Type	Journal
	Year	2008	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	41	Issue	2	Pages	713–725
	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;HuPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ PER2008			Serial	942
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	Author	Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva
	Title	Circular Blurred Shape Model for Multiclass Symbol Recognition			Type	Journal Article
	Year	2011	Publication	IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE)	Abbreviated Journal	TSMCB
	Volume	41	Issue	2	Pages	497-506
	Keywords
	Abstract	In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.
	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	1083-4419	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB; DAG;HuPBA			Approved	no
	Call Number	Admin @ si @ EFP2011			Serial	1784
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	Author	Xavier Otazu
	Title	Perceptual tone-mapping operator based on multiresolution contrast decomposition			Type	Abstract
	Year	2012	Publication	Perception	Abbreviated Journal	PER
	Volume	41	Issue		Pages	86
	Keywords
	Abstract	Tone-mapping operators (TMO) are used to display high dynamic range(HDR) images in low dynamic range (LDR) displays. Many computational and biologically inspired approaches have been used in the literature, being many of them based on multiresolution decompositions. In this work, a simple two stage model for TMO is presented. The first stage is a novel multiresolution contrast decomposition, which is inspired in a pyramidal contrast decomposition (Peli, 1990 Journal of the Optical Society of America7(10), 2032-2040). This novel multiresolution decomposition represents the Michelson contrast of the image at different spatial scales. This multiresolution contrast representation, applied on the intensity channel of an opponent colour decomposition, is processed by a non-linear saturating model of V1 neurons (Albrecht et al, 2002 Journal ofNeurophysiology 88(2) 888-913). This saturation model depends on the visual frequency, and it has been modified in order to include information from the extended Contrast Sensitivity Function (e-CSF) (Otazu et al, 2010 Journal ofVision10(12) 5). A set of HDR images in Radiance RGBE format (from CIS HDR Photographic Survey and Greg Ward database) have been used to test the model, obtaining a set of LDR images. The resulting LDR images do not show the usual halo or color modification artifacts.
	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	0301-0066	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ Ota2012			Serial	2179
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	Author	Olivier Penacchio; Laura Dempere-Marco; Xavier Otazu
	Title	Switching off brightness induction through induction-reversed images			Type	Abstract
	Year	2012	Publication	Perception	Abbreviated Journal	PER
	Volume	41	Issue		Pages	208
	Keywords
	Abstract	Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Although V1 is traditionally regarded as an area mostly responsive to retinal information, neurophysiological evidence suggests that it may explicitly represent brightness information. In this work, we investigate possible neural mechanisms underlying brightness induction. To this end, we consider the model by Z Li (1999 Computation and Neural Systems10187-212) which is constrained by neurophysiological data and focuses on the part of V1 responsible for contextual influences. This model, which has proven to account for phenomena such as contour detection and preattentive segmentation, shares with brightness induction the relevant effect of contextual influences. Importantly, the input to our network model derives from a complete multiscale and multiorientation wavelet decomposition, which makes it possible to recover an image reflecting the perceived luminance and successfully accounts for well known psychophysical effects for both static and dynamic contexts. By further considering inverse problem techniques we define induction-reversed images: given a target image, we build an image whose perceived luminance matches the actual luminance of the original stimulus, thus effectively canceling out brightness induction effects. We suggest that induction-reversed images may help remove undesired perceptual effects and can find potential applications in fields such as radiological image interpretation
	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	CIC			Approved	no
	Call Number	Admin @ si @ PDO2012a			Serial	2180
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	Author	Jordi Roca; C. Alejandro Parraga; Maria Vanrell
	Title	Predicting categorical colour perception in successive colour constancy			Type	Abstract
	Year	2012	Publication	Perception	Abbreviated Journal	PER
	Volume	41	Issue		Pages	138
	Keywords
	Abstract	Colour constancy is a perceptual mechanism that seeks to keep the colour of objects relatively stable under an illumination shift. Experiments haveshown that its effects depend on the number of colours present in the scene. We studied categorical colour changes under different adaptation states, in particular, whether the colour categories seen under a chromatically neutral illuminant are the same after a shift in the chromaticity of the illumination. To do this, we developed the chromatic setting paradigm (2011 Journal of Vision11 349), which is as an extension of achromatic setting to colour categories. The paradigm exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. Our experiments were run on a CRT monitor (inside a dark room) under various simulated illuminants and restricting the number of colours of the Mondrian background to three, thus weakening the adaptation effect. Our results show a change in the colour categories present before (under neutral illumination) and after adaptation (under coloured illuminants) with a tendency for adapted colours to be less saturated than before adaptation. This behaviour was predicted by a simple affine matrix model, adjusted to the chromatic setting results.
	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	0301-0066	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ RPV2012			Serial	2188
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	Author	Joan Marc Llargues Asensio; Juan Peralta; Raul Arrabales; Manuel Gonzalez Bedia; Paulo Cortez; Antonio Lopez
	Title	Artificial Intelligence Approaches for the Generation and Assessment of Believable Human-Like Behaviour in Virtual Characters			Type	Journal Article
	Year	2014	Publication	Expert Systems With Applications	Abbreviated Journal	EXSY
	Volume	41	Issue	16	Pages	7281–7290
	Keywords	Turing test; Human-like behaviour; Believability; Non-player characters; Cognitive architectures; Genetic algorithm; Artificial neural networks
	Abstract	Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.
	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; 600.057; 600.076			Approved	no
	Call Number	Admin @ si @ LPA2014			Serial	2500
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