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	Author	Patrick Brandao; O. Zisimopoulos; E. Mazomenos; G. Ciutib; Jorge Bernal; M. Visentini-Scarzanell; A. Menciassi; P. Dario; A. Koulaouzidis; A. Arezzo; D.J. Hawkes; D. Stoyanov
	Title	Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks			Type	Journal
	Year	2018	Publication	Journal of Medical Robotics Research	Abbreviated Journal	JMRR
	Volume	3	Issue	2	Pages
	Keywords	convolutional neural networks; colonoscopy; computer aided diagnosis
	Abstract	Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MV; no menciona			Approved	no
	Call Number	BZM2018			Serial	2976
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	Author	Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora
	Title	Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts			Type	Conference Article
	Year	2018	Publication	16th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages	528-533
	Keywords	Crowdsourcing; Gamification; Handwritten documents; Performance evaluation
	Abstract	Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance.
	Address	Niagara Falls, USA; August 2018
	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	ICFHR
	Notes	DAG; 600.097; 603.057; 600.121			Approved	no
	Call Number	Admin @ si @ CRF2018			Serial	3169
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	Author	Jorge Charco; Boris X. Vintimilla; Angel Sappa
	Title	Deep learning based camera pose estimation in multi-view environment			Type	Conference Article
	Year	2018	Publication	14th IEEE International Conference on Signal Image Technology & Internet Based System	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Deep learning; Camera pose estimation; Multiview environment; Siamese architecture
	Abstract	This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of imagesfrom the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.
	Address	Las Palmas de Gran Canaria; November 2018
	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	SITIS
	Notes	MSIAU; 600.086; 600.130; 600.122			Approved	no
	Call Number	Admin @ si @ CVS2018			Serial	3194
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	Author	Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa
	Title	Color Naming for Multi-Color Fashion Items			Type	Conference Article
	Year	2018	Publication	6th World Conference on Information Systems and Technologies	Abbreviated Journal
	Volume	747	Issue		Pages	64-73
	Keywords	Deep learning; Color; Multi-label
	Abstract	There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results.
	Address	Naples; March 2018
	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	WORLDCIST
	Notes	LAMP; 600.109; 601.309; 600.120			Approved	no
	Call Number	Admin @ si @ YWR2018			Serial	3161
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	Author	Pau Rodriguez; Josep M. Gonfaus; Guillem Cucurull; Xavier Roca; Jordi Gonzalez
	Title	Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery			Type	Conference Article
	Year	2018	Publication	15th European Conference on Computer Vision	Abbreviated Journal
	Volume	11212	Issue		Pages	357-372
	Keywords	Deep Learning; Convolutional Neural Networks; Attention
	Abstract	We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100.
	Address	Munich; September 2018
	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	ECCV
	Notes	ISE; 600.098; 602.121; 600.119			Approved	no
	Call Number	Admin @ si @ RGC2018			Serial	3139
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	Author	Francisco Cruz; Oriol Ramos Terrades
	Title	A probabilistic framework for handwritten text line segmentation			Type	Miscellaneous
	Year	2018	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning
	Abstract	We successfully combine Expectation-Maximization algorithm and variational approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer vision tasks. In this paper, we apply it to handwritten text line segmentation. We conduct several experiments that demonstrate that our method deal with common issues of this task, such as complex document layout or non-latin scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine tuning step.
	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; 600.097; 600.121			Approved	no
	Call Number	Admin @ si @ CrR2018			Serial	3253
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	Author	Antonio Lopez; David Vazquez; Gabriel Villalonga
	Title	Data for Training Models, Domain Adaptation			Type	Book Chapter
	Year	2018	Publication	Intelligent Vehicles. Enabling Technologies and Future Developments	Abbreviated Journal
	Volume		Issue		Pages	395–436
	Keywords	Driving simulator; hardware; software; interface; traffic simulation; macroscopic simulation; microscopic simulation; virtual data; training data
	Abstract	Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection.
	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; 600.118			Approved	no
	Call Number	Admin @ si @ LVV2018			Serial	3047
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	Author	Xavier Soria; Angel Sappa
	Title	Improving Edge Detection in RGB Images by Adding NIR Channel			Type	Conference Article
	Year	2018	Publication	14th IEEE International Conference on Signal Image Technology & Internet Based System	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images
	Abstract	The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum.
	Address	Las Palmas de Gran Canaria; November 2018
	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	SITIS
	Notes	MSIAU; 600.122			Approved	no
	Call Number	Admin @ si @ SoS2018			Serial	3192
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	Author	Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli
	Title	Batch-based activity recognition from egocentric photo-streams revisited			Type	Journal Article
	Year	2018	Publication	Pattern Analysis and Applications	Abbreviated Journal	PAA
	Volume	21	Issue	4	Pages	953–965
	Keywords	Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks
	Abstract	Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average.
	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; no proj			Approved	no
	Call Number	Admin @ si @ CMR2018			Serial	3186
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	Author	Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva
	Title	Egocentric video description based on temporally-linked sequences			Type	Journal Article
	Year	2018	Publication	Journal of Visual Communication and Image Representation	Abbreviated Journal	JVCIR
	Volume	50	Issue		Pages	205-216
	Keywords	egocentric vision; video description; deep learning; multi-modal learning
	Abstract	Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures. In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description.
	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; no proj			Approved	no
	Call Number	Admin @ si @ BPC2018			Serial	3109
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	Author	Laura Lopez-Fuentes; Joost Van de Weijer; Manuel Gonzalez-Hidalgo; Harald Skinnemoen; Andrew Bagdanov
	Title	Review on computer vision techniques in emergency situations			Type	Journal Article
	Year	2018	Publication	Multimedia Tools and Applications	Abbreviated Journal	MTAP
	Volume	77	Issue	13	Pages	17069–17107
	Keywords	Emergency management; Computer vision; Decision makers; Situational awareness; Critical situation
	Abstract	In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.
	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; 600.068; 600.120			Approved	no
	Call Number	Admin @ si @ LWG2018			Serial	3041
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	Author	Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari
	Title	Review on Emotion Recognition Databases			Type	Book Chapter
	Year	2018	Publication	Human-Robot Interaction: Theory and Application	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	emotion; computer vision; databases
	Abstract	Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format.
	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	978-1-78923-316-2	Medium
	Area		Expedition		Conference
	Notes	HUPBA; 602.133			Approved	no
	Call Number	Admin @ si @ HRL2018			Serial	3212
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	Author	Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez
	Title	Beyond Oneshot Encoding: lower dimensional target embedding			Type	Journal Article
	Year	2018	Publication	Image and Vision Computing	Abbreviated Journal	IMAVIS
	Volume	75	Issue		Pages	21-31
	Keywords	Error correcting output codes; Output embeddings; Deep learning; Computer vision
	Abstract	Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ISE; HuPBA; 600.098; 602.133; 602.121; 600.119			Approved	no
	Call Number	Admin @ si @ RBE2018			Serial	3120
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	Author	Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud
	Title	Deep Learning based Single Image Dehazing			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop	Abbreviated Journal
	Volume		Issue		Pages	1250 - 12507
	Keywords	Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis
	Abstract	This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently. A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images.
	Address	Salt Lake City; USA; June 2018
	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	MSIAU; 600.086; 600.130; 600.122			Approved	no
	Call Number	Admin @ si @ SSV2018d			Serial	3197
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	Author	Yaxing Wang; Chenshen Wu; Luis Herranz; Joost Van de Weijer; Abel Gonzalez-Garcia; Bogdan Raducanu
	Title	Transferring GANs: generating images from limited data			Type	Conference Article
	Year	2018	Publication	15th European Conference on Computer Vision	Abbreviated Journal
	Volume	11210	Issue		Pages	220-236
	Keywords	Generative adversarial networks; Transfer learning; Domain adaptation; Image generation
	Abstract	ransferring knowledge of pre-trained networks to new domains by means of fine-tuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domain adaptation applied to image generation with generative adversarial networks. We evaluate several aspects of domain adaptation, including the impact of target domain size, the relative distance between source and target domain, and the initialization of conditional GANs. Our results show that using knowledge from pre-trained networks can shorten the convergence time and can significantly improve the quality of the generated images, especially when target data is limited. We show that these conclusions can also be drawn for conditional GANs even when the pre-trained model was trained without conditioning. Our results also suggest that density is more important than diversity and a dataset with one or few densely sampled classes is a better source model than more diverse datasets such as ImageNet or Places.
	Address	Munich; September 2018
	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	ECCV
	Notes	LAMP; 600.109; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ WWH2018a			Serial	3130
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