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	Author	Fadi Dornaika; Bogdan Raducanu
	Title	Out-of-Sample Embedding for Manifold Learning Applied to Face Recognition			Type	Conference Article
	Year	2013	Publication	IEEE International Workshop on Analysis and Modeling of Faces and Gestures	Abbreviated Journal
	Volume		Issue		Pages	862-868
	Keywords
	Abstract	Manifold learning techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data---the out-of-sample problem. For the first aspect, the proposed schemes were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only reached for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that sparse coding theory not only serves for automatic graph reconstruction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the k-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on four public face databases. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes.
	Address	Portland; USA; June 2013
	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	CVPRW
	Notes	OR; 600.046;MV			Approved	no
	Call Number	Admin @ si @ DoR2013			Serial	2236
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	Author	Oriol Rodriguez-Leor; Eduard Fernandez-Nofrerias; J. Mauri; Vicente del Valle; Debora Gil; A.Barrios; E.Garcia; Petia Radeva
	Title	Perfusion ratio: A new tool to objectively assess microcirculation perfusion after primary Percutaneous Coronary Intervention			Type	Conference Article
	Year	2006	Publication	World Congress of Cardiology	Abbreviated Journal
	Volume		Issue		Pages	859
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication	Barcelona (Spain)	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	IAM;MILAB			Approved	no
	Call Number	IAM @ iam @ RFM2006c			Serial	1643
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	Author	Francisco Jose Perales; Juan J. Villanueva; Yuhua Luo
	Title	An automatic two-camera human motion perception system based on biomechanical model matching			Type	Conference Article
	Year	1991	Publication	IEEE International Conference on Systems, Man and Cybernetics	Abbreviated Journal
	Volume	2	Issue		Pages	856-858
	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				Approved	no
	Call Number	ISE @ ise @ PVL1991b			Serial	265
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	Author	Jorge Charco; Angel Sappa; Boris X. Vintimilla
	Title	Human Pose Estimation through a Novel Multi-view Scheme			Type	Conference Article
	Year	2022	Publication	17th International Conference on Computer Vision Theory and Applications (VISAPP 2022)	Abbreviated Journal
	Volume	5	Issue		Pages	855-862
	Keywords	Multi-view Scheme; Human Pose Estimation; Relative Camera Pose; Monocular Approach
	Abstract	This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human pose estimation problem. The proposed approach first obtains the human body joints of a set of images, which are captured from different views at the same time. Then, it enhances the obtained joints by using a multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements in the accuracy of body joints estimations.
	Address	On line; Feb 6, 2022 – Feb 8, 2022
	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	2184-4321	ISBN	978-989-758-555-5	Medium
	Area		Expedition		Conference	VISAPP
	Notes	MSIAU; 600.160			Approved	no
	Call Number	Admin @ si @ CSV2022			Serial	3689
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	Author	Pierluigi Casale; Oriol Pujol; Petia Radeva
	Title	Approximate polytope ensemble for one-class classification			Type	Journal Article
	Year	2014	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	47	Issue	2	Pages	854-864
	Keywords	One-class classification; Convex hull; High-dimensionality; Random projections; Ensemble learning
	Abstract	In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.
	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; 605.203			Approved	no
	Call Number	Admin @ si @ CPR2014a			Serial	2469
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	Author	Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla
	Title	Editorial: Special Issue on Computational Intelligence for Vision and Robotics			Type	Journal Article
	Year	2017	Publication	Neural Computing and Applications	Abbreviated Journal	Neural Computing and Applications
	Volume	28	Issue	5	Pages	853–854
	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;MILAB; no menciona			Approved	no
	Call Number	Admin @ si @ GGE2017			Serial	2845
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	Author	E. Serradell; Adriana Romero; R. Leta; Carlo Gatta; Francesc Moreno-Noguer
	Title	Simultaneous Correspondence and Non-Rigid 3D Reconstruction of the Coronary Tree from Single X-Ray Images			Type	Conference Article
	Year	2011	Publication	13th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	850-857
	Keywords
	Abstract
	Address	Barcelona
	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	ICCV
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ SRL2011			Serial	1803
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	Author	Javier M. Olaso; Alain Vazquez; Leila Ben Letaifa; Mikel de Velasco; Aymen Mtibaa; Mohamed Amine Hmani; Dijana Petrovska-Delacretaz; Gerard Chollet; Cesar Montenegro; Asier Lopez-Zorrilla; Raquel Justo; Roberto Santana; Jofre Tenorio-Laranga; Eduardo Gonzalez-Fraile; Begoña Fernandez-Ruanova; Gennaro Cordasco; Anna Esposito; Kristin Beck Gjellesvik; Anna Torp Johansen; Maria Stylianou Kornes; Colin Pickard; Cornelius Glackin; Gary Cahalane; Pau Buch; Cristina Palmero; Sergio Escalera; Olga Gordeeva; Olivier Deroo; Anaïs Fernandez; Daria Kyslitska; Jose Antonio Lozano; Maria Ines Torres; Stephan Schlogl
	Title	The EMPATHIC Virtual Coach: a demo			Type	Conference Article
	Year	2021	Publication	23rd ACM International Conference on Multimodal Interaction	Abbreviated Journal
	Volume		Issue		Pages	848-851
	Keywords
	Abstract	The main objective of the EMPATHIC project has been the design and development of a virtual coach to engage the healthy-senior user and to enhance well-being through awareness of personal status. The EMPATHIC approach addresses this objective through multimodal interactions supported by the GROW coaching model. The paper summarizes the main components of the EMPATHIC Virtual Coach (EMPATHIC-VC) and introduces a demonstration of the coaching sessions in selected scenarios.
	Address	Virtual; October 2021
	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	ICMI
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ OVB2021			Serial	3644
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	Author	Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez
	Title	Comparison of two non-linear model-based control strategies for autonomous vehicles			Type	Conference Article
	Year	2016	Publication	24th Mediterranean Conference on Control and Automation	Abbreviated Journal
	Volume		Issue		Pages	846-851
	Keywords	Autonomous Driving; Control
	Abstract	This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation.
	Address	Athens; Greece; June 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	MED
	Notes	ADAS; 600.085; 600.082; 600.076			Approved	no
	Call Number	ADAS @ adas @ ASP2016			Serial	2750
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	Author	Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
	Title	Combining Local and Global Learners in the Pairwise Multiclass Classification			Type	Journal Article
	Year	2015	Publication	Pattern Analysis and Applications	Abbreviated Journal	PAA
	Volume	18	Issue	4	Pages	845-860
	Keywords	Multiclass classification; Pairwise approach; One-versus-one
	Abstract	Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
	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	1433-7541	ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BGE2014			Serial	2441
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	Author	Hamdi Dibeklioglu; Albert Ali Salah; Theo Gevers
	Title	A Statistical Method for 2D Facial Landmarking			Type	Journal Article
	Year	2012	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
	Volume	21	Issue	2	Pages	844-858
	Keywords
	Abstract	IF = 3.32 Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE).
	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	1057-7149	ISBN		Medium
	Area		Expedition		Conference
	Notes	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @ DSG 2012			Serial	1853
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	Author	Pejman Rasti; Salma Samiei; Mary Agoyi; Sergio Escalera; Gholamreza Anbarjafari
	Title	Robust non-blind color video watermarking using QR decomposition and entropy analysis			Type	Journal Article
	Year	2016	Publication	Journal of Visual Communication and Image Representation	Abbreviated Journal	JVCIR
	Volume	38	Issue		Pages	838-847
	Keywords	Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition
	Abstract	Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks.
	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;MILAB;			Approved	no
	Call Number	Admin @ si @RSA2016			Serial	2766
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	Author	Gloria Fernandez Esparrach; Jorge Bernal; Maria Lopez Ceron; Henry Cordova; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; F. Javier Sanchez
	Title	Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps			Type	Journal Article
	Year	2016	Publication	Endoscopy	Abbreviated Journal	END
	Volume	48	Issue	9	Pages	837-842
	Keywords
	Abstract	Background and aims: Polyp miss-rate is a drawback of colonoscopy that increases significantly in small polyps. We explored the efficacy of an automatic computer vision method for polyp detection. Methods: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps which represent the likelihood of polyp presence. Results: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. Mean values of the maximum of energy map were higher in frames with polyps than without (p<0.001). Performance improved in high quality frames (AUC= 0.79, 95%CI: 0.70-0.87 vs 0.75, 95%CI: 0.66-0.83). Using 3.75 as maximum threshold value, sensitivity and specificity for detection of polyps were 70.4% (95%CI: 60.3-80.8) and 72.4% (95%CI: 61.6-84.6), respectively. Conclusion: Energy maps showed a good performance for colonic polyp detection. This indicates a potential applicability in clinical practice.
	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 @FBL2016			Serial	2778
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	Author	Zeynep Yucel; Albert Ali Salah; Çetin Meriçli; Tekin Meriçli; Roberto Valenti; Theo Gevers
	Title	Joint Attention by Gaze Interpolation and Saliency			Type	Journal
	Year	2013	Publication	IEEE Transactions on cybernetics	Abbreviated Journal	T-CIBER
	Volume	43	Issue	3	Pages	829-842
	Keywords
	Abstract	Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenter's eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention.
	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	2168-2267	ISBN		Medium
	Area		Expedition		Conference
	Notes	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @ YSM2013			Serial	2363
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	Author	Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez
	Title	Enhancing spatio-chromatic representation with more-than-three color coding for image description			Type	Journal Article
	Year	2017	Publication	Journal of the Optical Society of America A	Abbreviated Journal	JOSA A
	Volume	34	Issue	5	Pages	827-837
	Keywords
	Abstract	Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding.
	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; 600.087			Approved	no
	Call Number	Admin @ si @ RVB2017			Serial	2892
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