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	Author	Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero
	Title	Evaluation of Texture Descriptors for Validation of Counterfeit Documents			Type	Conference Article
	Year	2017	Publication	14th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1237-1242
	Keywords
	Abstract	This paper describes an exhaustive comparative analysis and evaluation of different existing texture descriptor algorithms to differentiate between genuine and counterfeit documents. We include in our experiments different categories of algorithms and compare them in different scenarios with several counterfeit datasets, comprising banknotes and identity documents. Computational time in the extraction of each descriptor is important because the final objective is to use it in a real industrial scenario. HoG and CNN based descriptors stands out statistically over the rest in terms of the F1-score/time ratio 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	2379-2140	ISBN		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG; 600.061; 601.269; 600.097; 600.121			Approved	no
	Call Number	Admin @ si @ BRL2017			Serial	3092
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	Author	Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes
	Title	Optical Music Recognition by Recurrent Neural Networks			Type	Conference Article
	Year	2017	Publication	14th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume		Issue		Pages	25-26
	Keywords	Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory
	Abstract	Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level
	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	ICDAR
	Notes	DAG; 600.097; 601.302; 600.121			Approved	no
	Call Number	Admin @ si @ BRC2017			Serial	3056
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	Author	Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva
	Title	VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering			Type	Conference Article
	Year	2017	Publication	8th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks
	Abstract	In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed.
	Address	Faro; Portugal; June 2017
	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	IbPRIA
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ BPC2017			Serial	2939
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	Author	Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad
	Title	Locality Regularized Group Sparse Coding for Action Recognition			Type	Journal Article
	Year	2017	Publication	Computer Vision and Image Understanding	Abbreviated Journal	CVIU
	Volume	158	Issue		Pages	106-114
	Keywords	Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition
	Abstract	Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered 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	HuPBA; no proj			Approved	no
	Call Number	Admin @ si @ BGE2017			Serial	3014
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	Author	Marc Bolaños; Mariella Dimiccoli; Petia Radeva
	Title	Towards Storytelling from Visual Lifelogging: An Overview			Type	Journal Article
	Year	2017	Publication	IEEE Transactions on Human-Machine Systems	Abbreviated Journal	THMS
	Volume	47	Issue	1	Pages	77 - 90
	Keywords
	Abstract	Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging.
	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; 601.235			Approved	no
	Call Number	Admin @ si @ BDR2017			Serial	2712
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	Author	Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva
	Title	Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study			Type	Conference Article
	Year	2017	Publication	International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT)	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI). Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback. The pipeline of the method requires the identication of the stable frames of the sequence and the reliable detection of stent struts. Using this data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores.
	Address	Quebec; Canada; September 2017
	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	MICCAIW
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ BCR2017			Serial	2968
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	Author	Aitor Alvarez-Gila; Joost Van de Weijer; Estibaliz Garrote
	Title	Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB			Type	Conference Article
	Year	2017	Publication	1st International Workshop on Physics Based Vision meets Deep Learning	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44:7% and a Relative RMSE drop of 47:0% on the ICVL natural hyperspectral image dataset.
	Address	Venice; Italy; October 2017
	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-PBDL
	Notes	LAMP; 600.109; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ AWG2017			Serial	2969
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	Author	Eirikur Agustsson; Radu Timofte; Sergio Escalera; Xavier Baro; Isabelle Guyon; Rasmus Rothe
	Title	Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database			Type	Conference Article
	Year	2017	Publication	12th IEEE International Conference on Automatic Face and Gesture Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	After decades of research, the real (biological) age estimation from a single face image reached maturity thanks to the availability of large public face databases and impressive accuracies achieved by recently proposed methods. The estimation of “apparent age” is a related task concerning the age perceived by human observers. Significant advances have been also made in this new research direction with the recent Looking At People challenges. In this paper we make several contributions to age estimation research. (i) We introduce APPA-REAL, a large face image database with both real and apparent age annotations. (ii) We study the relationship between real and apparent age. (iii) We develop a residual age regression method to further improve the performance. (iv) We show that real age estimation can be successfully tackled as an apparent age estimation followed by an apparent to real age residual regression. (v) We graphically reveal the facial regions on which the CNN focuses in order to perform apparent and real age estimation tasks.
	Address	Washington;USA; May 2017
	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	FG
	Notes	HUPBA; no menciona			Approved	no
	Call Number	Admin @ si @ ATE2017			Serial	3013
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	Author	Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo
	Title	RGBN Multispectral Images: a Novel Color Restoration Approach			Type	Conference Article
	Year	2017	Publication	15th International Conference on Practical Applications of Agents and Multi-Agent System	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Multispectral Imaging; Free Sensor Model; Neural Network
	Abstract	This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired\|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square error minimization are provided.
	Address	Porto; Portugal; June 2017
	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	PAAMS
	Notes	ADAS; MSIAU; 600.118; 600.122			Approved	no
	Call Number	Admin @ si @ ASS2017			Serial	2918
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	Author	Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo
	Title	Cross-Spectral Local Descriptors via Quadruplet Network			Type	Journal Article
	Year	2017	Publication	Sensors	Abbreviated Journal	SENS
	Volume	17	Issue	4	Pages	873
	Keywords
	Abstract	This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.
	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; 600.086; 600.118			Approved	no
	Call Number	Admin @ si @ ASA2017			Serial	2914
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	Author	David Aldavert; Marçal Rusiñol; Ricardo Toledo
	Title	Automatic Static/Variable Content Separation in Administrative Document Images			Type	Conference Article
	Year	2017	Publication	14th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset.
	Address	Kyoto; Japan; November 2017
	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	ICDAR
	Notes	DAG; 600.084; 600.121			Approved	no
	Call Number	Admin @ si @ ART2017			Serial	3001
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	Author	Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu
	Title	Can biological solutions help computers detect symmetry?			Type	Conference Article
	Year	2017	Publication	40th European Conference on Visual Perception	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Berlin; Germany; August 2017
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ECVP
	Notes	NEUROBIT			Approved	no
	Call Number	Admin @ si @ APE2017			Serial	2995
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	Author	Arash Akbarinia; Karl R. Gegenfurtner
	Title	Metameric Mismatching in Natural and Artificial Reflectances			Type	Journal Article
	Year	2017	Publication	Journal of Vision	Abbreviated Journal	JV
	Volume	17	Issue	10	Pages	390-390
	Keywords	Metamer; colour perception; spectral discrimination; photoreceptors
	Abstract	The human visual system and most digital cameras sample the continuous spectral power distribution through three classes of receptors. This implies that two distinct spectral reflectances can result in identical tristimulus values under one illuminant and differ under another – the problem of metamer mismatching. It is still debated how frequent this issue arises in the real world, using naturally occurring reflectance functions and common illuminants. We gathered more than ten thousand spectral reflectance samples from various sources, covering a wide range of environments (e.g., flowers, plants, Munsell chips) and evaluated their responses under a number of natural and artificial source of lights. For each pair of reflectance functions, we estimated the perceived difference using the CIE-defined distance ΔE2000 metric in Lab color space. The degree of metamer mismatching depended on the lower threshold value l when two samples would be considered to lead to equal sensor excitations (ΔE < l), and on the higher threshold value h when they would be considered different. For example, for l=h=1, we found that 43.129 comparisons out of a total of 6×107 pairs would be considered metameric (1 in 104). For l=1 and h=5, this number reduced to 705 metameric pairs (2 in 106). Extreme metamers, for instance l=1 and h=10, were rare (22 pairs or 6 in 108), as were instances where the two members of a metameric pair would be assigned to different color categories. Not unexpectedly, we observed variations among different reflectance databases and illuminant spectra with more frequency under artificial illuminants than natural ones. Overall, our numbers are not very different from those obtained earlier (Foster et al, JOSA A, 2006). However, our results also show that the degree of metamerism is typically not very strong and that category switches hardly ever occur.
	Address	Florida, USA; May 2017
	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	NEUROBIT; no menciona			Approved	no
	Call Number	Admin @ si @ AkG2017			Serial	2899
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	Author	Arash Akbarinia
	Title	Computational Model of Visual Perception: From Colour to Form			Type	Book Whole
	Year	2017	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The original idea of this project was to study the role of colour in the challenging task of object recognition. We started by extending previous research on colour naming showing that it is feasible to capture colour terms through parsimonious ellipsoids. Although, the results of our model exceeded state-of-the-art in two benchmark datasets, we realised that the two phenomena of metameric lights and colour constancy must be addressed prior to any further colour processing. Our investigation of metameric pairs reached the conclusion that they are infrequent in real world scenarios. Contrary to that, the illumination of a scene often changes dramatically. We addressed this issue by proposing a colour constancy model inspired by the dynamical centre-surround adaptation of neurons in the visual cortex. This was implemented through two overlapping asymmetric Gaussians whose variances and heights are adjusted according to the local contrast of pixels. We complemented this model with a generic contrast-variant pooling mechanism that inversely connect the percentage of pooled signal to the local contrast of a region. The results of our experiments on four benchmark datasets were indeed promising: the proposed model, although simple, outperformed even learning-based approaches in many cases. Encouraged by the success of our contrast-variant surround modulation, we extended this approach to detect boundaries of objects. We proposed an edge detection model based on the first derivative of the Gaussian kernel. We incorporated four types of surround: full, far, iso- and orthogonal-orientation. Furthermore, we accounted for the pooling mechanism at higher cortical areas and the shape feedback sent to lower areas. Our results in three benchmark datasets showed significant improvement over non-learning algorithms. To summarise, we demonstrated that biologically-inspired models offer promising solutions to computer vision problems, such as, colour naming, colour constancy and edge detection. We believe that the greatest contribution of this Ph.D dissertation is modelling the concept of dynamic surround modulation that shows the significance of contrast-variant surround integration. The models proposed here are grounded on only a portion of what we know about the human visual system. Therefore, it is only natural to complement them accordingly in future works.
	Address	October 2017
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	C. Alejandro Parraga
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-945373-4-9	Medium
	Area		Expedition		Conference
	Notes	NEUROBIT			Approved	no
	Call Number	Admin @ si @ Akb2017			Serial	3019
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	Author	Cristhian Aguilera
	Title	Local feature description in cross-spectral imagery			Type	Book Whole
	Year	2017	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Over the last few years, the number of consumer computer vision applications has increased dramatically. Today, computer vision solutions can be found in video game consoles, smartphone applications, driving assistance – just to name a few. Ideally, we require the performance of those applications, particularly those that are safety critical to remain constant under any external environment factors, such as changes in illumination or weather conditions. However, this is not always possible or very difficult to obtain by only using visible imagery, due to the inherent limitations of the images from that spectral band. For that reason, the use of images from different or multiple spectral bands is becoming more appealing. The aforementioned possible advantages of using images from multiples spectral bands on various vision applications make multi-spectral image processing a relevant topic for research and development. Like in visible image processing, multi-spectral image processing needs tools and algorithms to handle information from various spectral bands. Furthermore, traditional tools such as local feature detection, which is the basis of many vision tasks such as visual odometry, image registration, or structure from motion, must be adjusted or reformulated to operate under new conditions. Traditional feature detection, description, and matching methods tend to underperform in multi-spectral settings, in comparison to mono-spectral settings, due to the natural differences between each spectral band. The work in this thesis is focused on the local feature description problem when cross-spectral images are considered. In this context, this dissertation has three main contributions. Firstly, the work starts by proposing the usage of a combination of frequency and spatial information, in a multi-scale scheme, as feature description. Evaluations of this proposal, based on classical hand-made feature descriptors, and comparisons with state of the art cross-spectral approaches help to find and understand limitations of such strategy. Secondly, different convolutional neural network (CNN) based architectures are evaluated when used to describe cross-spectral image patches. Results showed that CNN-based methods, designed to work with visible monocular images, could be successfully applied to the description of images from two different spectral bands, with just minor modifications. In this framework, a novel CNN-based network model, specifically intended to describe image patches from two different spectral bands, is proposed. This network, referred to as Q-Net, outperforms state of the art in the cross-spectral domain, including both previous hand-made solutions as well as L2 CNN-based architectures. The third contribution of this dissertation is in the cross-spectral feature description application domain. The multispectral odometry problem is tackled showing a real application of cross-spectral descriptors In addition to the three main contributions mentioned above, in this dissertation, two different multi-spectral datasets are generated and shared with the community to be used as benchmarks for further studies.
	Address	October 2017
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Angel Sappa
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-945373-6-3	Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.118			Approved	no
	Call Number	Admin @ si @ Agu2017			Serial	3020
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