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	Author	Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika
	Title	Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics			Type	Conference Article
	Year	2014	Publication	1st Workshop on Computer Vision for Affective Computing	Abbreviated Journal
	Volume		Issue		Pages	1-8
	Keywords
	Abstract	Human-robot interaction is a hot topic nowadays in the social robotics community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this paper, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, view- and texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial expression.
	Address	Singapore; November 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	ACCV
	Notes	LAMP;			Approved	no
	Call Number	Admin @ si @ RBD2014			Serial	2599
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	Author	Sebastian Ramos
	Title	Vision-based Detection of Road Hazards for Autonomous Driving			Type	Report
	Year	2014	Publication	CVC Technical Report	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	UAB; September 2014
	Corporate Author				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; 600.076			Approved	no
	Call Number	Admin @ si @ Ram2014			Serial	2580
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	Author	Pau Riba; Jon Almazan; Alicia Fornes; David Fernandez; Ernest Valveny; Josep Llados
	Title	e-Crowds: a mobile platform for browsing and searching in historical demographyrelated manuscripts			Type	Conference Article
	Year	2014	Publication	14th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages	228 - 233
	Keywords
	Abstract	This paper presents a prototype system running on portable devices for browsing and word searching through historical handwritten document collections. The platform adapts the paradigm of eBook reading, where the narrative is not necessarily sequential, but centered on the user actions. The novelty is to replace digitally born books by digitized historical manuscripts of marriage licenses, so document analysis tasks are required in the browser. With an active reading paradigm, the user can cast queries of people names, so he/she can implicitly follow genealogical links. In addition, the system allows combined searches: the user can refine a search by adding more words to search. As a second contribution, the retrieval functionality involves as a core technology a word spotting module with an unified approach, which allows combined query searches, and also two input modalities: query-by-example, and query-by-string.
	Address	Creete Island; Grecia; 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	2167-6445	ISBN	978-1-4799-4335-7	Medium
	Area		Expedition		Conference	ICFHR
	Notes	DAG; 600.056; 600.045; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ RAF2014			Serial	2463
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	Author	Bogdan Raducanu; Fadi Dornaika
	Title	Embedding new observations via sparse-coding for non-linear manifold learning			Type	Journal Article
	Year	2014	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	47	Issue	1	Pages	480-492
	Keywords
	Abstract	Non-linear dimensionality reduction 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 solutions, in general, 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 achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction 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 six public face datasets. 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
	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;			Approved	no
	Call Number	Admin @ si @ RaD2013b			Serial	2316
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	Author	Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo
	Title	Subspace Procrustes Analysis			Type	Conference Article
	Year	2014	Publication	ECCV Workshop on ChaLearn Looking at People	Abbreviated Journal
	Volume	8925	Issue		Pages	654-668
	Keywords
	Abstract	Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling dierent views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more ecient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benets of our approach.
	Address
	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		Medium
	Area		Expedition		Conference	ECCVW
	Notes	OR; HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ PTI2014			Serial	2539
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	Author	C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger
	Title	Limitations of visual gamma corrections in LCD displays			Type	Journal Article
	Year	2014	Publication	Displays	Abbreviated Journal	Dis
	Volume	35	Issue	5	Pages	227–239
	Keywords	Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration
	Abstract	A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements.
	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; DAG; 600.052; 600.077; 600.074			Approved	no
	Call Number	Admin @ si @ PRK2014			Serial	2511
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	Author	Monica Piñol
	Title	Reinforcement Learning of Visual Descriptors for Object Recognition			Type	Book Whole
	Year	2014	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The human visual system is able to recognize the object in an image even if the object is partially occluded, from various points of view, in different colors, or with independence of the distance to the object. To do this, the eye obtains an image and extracts features that are sent to the brain, and then, in the brain the object is recognized. In computer vision, the object recognition branch tries to learns from the human visual system behaviour to achieve its goal. Hence, an algorithm is used to identify representative features of the scene (detection), then another algorithm is used to describe these points (descriptor) and finally the extracted information is used for classifying the object in the scene. The selection of this set of algorithms is a very complicated task and thus, a very active research field. In this thesis we are focused on the selection/learning of the best descriptor for a given image. In the state of the art there are several descriptors but we do not know how to choose the best descriptor because depends on scenes that we will use (dataset) and the algorithm chosen to do the classification. We propose a framework based on reinforcement learning and bag of features to choose the best descriptor according to the given image. The system can analyse the behaviour of different learning algorithms and descriptor sets. Furthermore the proposed framework for improving the classification/recognition ratio can be used with minor changes in other computer vision fields, such as video retrieval.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Ricardo Toledo;Angel Sappa
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-940902-5-7	Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076			Approved	no
	Call Number	Admin @ si @ Piñ2014			Serial	2464
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	Author	Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Jordi Gonzalez
	Title	A survey on model based approaches for 2D and 3D visual human pose recovery			Type	Journal Article
	Year	2014	Publication	Sensors	Abbreviated Journal	SENS
	Volume	14	Issue	3	Pages	4189-4210
	Keywords	human pose recovery; human body modelling; behavior analysis; computer vision
	Abstract	Human Pose Recovery has been studied in the field of Computer Vision for the last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. However, the problem of Human Pose Recovery in uncontrolled environments is far from being solved. In this paper, we define a general taxonomy to group model based approaches for Human Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. As a result of this comparison, we discuss the main advantages and drawbacks of the reviewed literature.
	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; ISE; 600.046; 600.063; 600.078;MILAB			Approved	no
	Call Number	Admin @ si @ PEA2014			Serial	2443
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	Author	Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta
	Title	Diaphragm border detection in coronary X-ray angiographies: New method and applications			Type	Journal Article
	Year	2014	Publication	Computerized Medical Imaging and Graphics	Abbreviated Journal	CMIG
	Volume	38	Issue	4	Pages	296-305
	Keywords
	Abstract	X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation.
	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; LAMP; 600.079			Approved	no
	Call Number	Admin @ si @ PCR2014			Serial	2468
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	Author	Eloi Puertas; Miguel Angel Bautista; Daniel Sanchez; Sergio Escalera; Oriol Pujol
	Title	Learning to Segment Humans by Stacking their Body Parts,			Type	Conference Article
	Year	2014	Publication	ECCV Workshop on ChaLearn Looking at People	Abbreviated Journal
	Volume	8925	Issue		Pages	685-697
	Keywords	Human body segmentation; Stacked Sequential Learning
	Abstract	Human segmentation in still images is a complex task due to the wide range of body poses and drastic changes in environmental conditions. Usually, human body segmentation is treated in a two-stage fashion. First, a human body part detection step is performed, and then, human part detections are used as prior knowledge to be optimized by segmentation strategies. In this paper, we present a two-stage scheme based on Multi-Scale Stacked Sequential Learning (MSSL). We define an extended feature set by stacking a multi-scale decomposition of body part likelihood maps. These likelihood maps are obtained in a first stage by means of a ECOC ensemble of soft body part detectors. In a second stage, contextual relations of part predictions are learnt by a binary classifier, obtaining an accurate body confidence map. The obtained confidence map is fed to a graph cut optimization procedure to obtain the final segmentation. Results show improved segmentation when MSSL is included in the human segmentation pipeline.
	Address
	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		Medium
	Area		Expedition		Conference	ECCVW
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ PBS2014			Serial	2553
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	Author	C. Alejandro Parraga
	Title	Color Vision, Computational Methods for			Type	Book Chapter
	Year	2014	Publication	Encyclopedia of Computational Neuroscience	Abbreviated Journal
	Volume		Issue		Pages	1-11
	Keywords	Color computational vision; Computational neuroscience of color
	Abstract	The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments.
	Address
	Corporate Author				Thesis
	Publisher	Springer-Verlag Berlin Heidelberg	Place of Publication		Editor	Dieter Jaeger; Ranu Jung
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4614-7320-6	Medium
	Area		Expedition		Conference
	Notes	CIC; 600.074			Approved	no
	Call Number	Admin @ si @ Par2014			Serial	2512
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	Author	Naveen Onkarappa; Angel Sappa
	Title	Speed and Texture: An Empirical Study on Optical-Flow Accuracy in ADAS Scenarios			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Intelligent Transportation Systems	Abbreviated Journal	TITS
	Volume	15	Issue	1	Pages	136-147
	Keywords
	Abstract	IF: 3.064 Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields.
	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	1524-9050	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076			Approved	no
	Call Number	Admin @ si @ OnS2014a			Serial	2386
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	Author	Naveen Onkarappa; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa
	Title	Cross-spectral Stereo Correspondence using Dense Flow Fields			Type	Conference Article
	Year	2014	Publication	9th International Conference on Computer Vision Theory and Applications	Abbreviated Journal
	Volume	3	Issue		Pages	613-617
	Keywords	Cross-spectral Stereo Correspondence; Dense Optical Flow; Infrared and Visible Spectrum
	Abstract	This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach.
	Address	Lisboa; Portugal; January 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	VISAPP
	Notes	ADAS; 600.055; 600.076			Approved	no
	Call Number	Admin @ si @ OAV2014			Serial	2477
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	Author	Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño
	Title	Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos			Type	Conference Article
	Year	2014	Publication	CARE workshop	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection
	Abstract	We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels.
	Address	Boston; USA; 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	CARE
	Notes	MV; DAG; 600.060; 600.047; 600.077;SIAI			Approved	no
	Call Number	Admin @ si @ NBF2014			Serial	2504
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	Author	David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson
	Title	Automated Prediction of Preferences Using Facial Expressions			Type	Journal Article
	Year	2014	Publication	PloS one	Abbreviated Journal	Plos
	Volume	9	Issue	2	Pages	e87434
	Keywords
	Abstract	We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available.
	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	OR;MV			Approved	no
	Call Number	Admin @ si @ MNT2014			Serial	2453
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