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	Author	Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer
	Title	LIUM-CVC Submissions for WMT17 Multimodal Translation Task			Type	Conference Article
	Year	2017	Publication	2nd Conference on Machine Translation	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU.
	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	WMT
	Notes	LAMP; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ CAB2017			Serial	3035
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	Author	Ozan Caglayan; Adrien Bardet; Fethi Bougares; Loic Barrault; Kai Wang; Marc Masana; Luis Herranz; Joost Van de Weijer
	Title	LIUM-CVC Submissions for WMT18 Multimodal Translation Task			Type	Conference Article
	Year	2018	Publication	3rd Conference on Machine Translation	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This year we propose several modifications to our previou multimodal attention architecture in order to better integrate convolutional features and refine them using encoder-side information. Our final constrained submissions ranked first for English→French and second for English→German language pairs among the constrained submissions according to the automatic evaluation metric METEOR.
	Address	Brussels; Belgium; October 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	WMT
	Notes	LAMP; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ CBB2018			Serial	3240
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	Author	Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich
	Title	Multi-Modal Aerial View Object Classification Challenge Results – PBVS 2022			Type	Conference Article
	Year	2022	Publication	IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)	Abbreviated Journal
	Volume		Issue		Pages	350-358
	Keywords
	Abstract	This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied Coincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021.
	Address	New Orleans; USA; June 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		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ LNS2022			Serial	3768
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	Author	Ole Larsen; Petia Radeva; Enric Marti
	Title	Bounds on the optimal elasticity parameters for a snake			Type	Journal Article
	Year	1995	Publication	Image Analysis and Processing	Abbreviated Journal
	Volume		Issue		Pages	37-42
	Keywords
	Abstract	This paper develops a formalism by which an estimate for the upper and lower bounds for the elasticity parameters for a snake can be obtained. Objects different in size and shape give rise to different bounds. The bounds can be obtained based on an analysis of the shape of the object of interest. Experiments on synthetic images show a good correlation between the estimated behaviour of the snake and the one actually observed. Experiments on real X-ray images show that the parameters for optimal segmentation lie within the estimated bounds.
	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;IAM			Approved	no
	Call Number	IAM @ iam @ LRM1995a			Serial	1559
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	Author	Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla
	Title	Wavelet based visible and infrared image fusion: a comparative study			Type	Journal Article
	Year	2016	Publication	Sensors	Abbreviated Journal	SENS
	Volume	16	Issue	6	Pages	1-15
	Keywords	Image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform
	Abstract	This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR).
	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; 600.076			Approved	no
	Call Number	Admin @ si @SCA2016			Serial	2807
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	Author	Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas
	Title	Selective Style Transfer for Text			Type	Conference Article
	Year	2019	Publication	15th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	805-812
	Keywords	transfer; text style transfer; data augmentation; scene text detection
	Abstract	This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available.
	Address	Sydney; Australia; September 2019
	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; 600.129; 600.135; 601.338; 601.310; 600.121			Approved	no
	Call Number	GBG2019			Serial	3265
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	Author	Anjan Dutta; Jaume Gibert; Josep Llados; Horst Bunke; Umapada Pal
	Title	Combination of Product Graph and Random Walk Kernel for Symbol Spotting in Graphical Documents			Type	Conference Article
	Year	2012	Publication	21st International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1663-1666
	Keywords
	Abstract	This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging.
	Address	Tsukuba, Japan
	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	1051-4651	ISBN	978-1-4673-2216-4	Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ DGL2012			Serial	2125
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	Author	Patricia Suarez; Angel Sappa; Boris X. Vintimilla
	Title	Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture			Type	Conference Article
	Year	2017	Publication	19th international conference on image analysis and processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	CNN in Multispectral Imaging; Image Colorization
	Abstract	This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results.
	Address	Catania; Italy; September 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	ICIAP
	Notes	ADAS; MSIAU; 600.086; 600.122; 600.118			Approved	no
	Call Number	Admin @ si @ SSV2017c			Serial	3016
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	Author	Patricia Suarez; Angel Sappa; Boris X. Vintimilla
	Title	Learning to Colorize Infrared Images			Type	Conference Article
	Year	2017	Publication	15th International Conference on Practical Applications of Agents and Multi-Agent System	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	CNN in multispectral imaging; Image colorization
	Abstract	This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very dierent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach.
	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; 600.086; 600.122; 600.118			Approved	no
	Call Number	Admin @ si @			Serial	2919
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	Author	Lluis Gomez; Dimosthenis Karatzas
	Title	A fine-grained approach to scene text script identification			Type	Conference Article
	Year	2016	Publication	12th IAPR Workshop on Document Analysis Systems	Abbreviated Journal
	Volume		Issue		Pages	192-197
	Keywords
	Abstract	This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online.
	Address	Santorini; Grecia; April 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	DAS
	Notes	DAG; 601.197; 600.084			Approved	no
	Call Number	Admin @ si @ GoK2016b			Serial	2863
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	Author	Ruth Aylett; Ginevra Castellano; Bogdan Raducanu; Ana Paiva; Marc Hanheide
	Title	Long-term socially perceptive and interactive robot companions: challenges and future perspectives			Type	Conference Article
	Year	2011	Publication	13th International Conference on Multimodal Interaction	Abbreviated Journal
	Volume		Issue		Pages	323-326
	Keywords	human-robot interaction, multimodal interaction, social robotics
	Abstract	This paper gives a brief overview of the challenges for multi-model perception and generation applied to robot companions located in human social environments. It reviews the current position in both perception and generation and the immediate technical challenges and goes on to consider the extra issues raised by embodiment and social context. Finally, it briefly discusses the impact of systems that must function continually over months rather than just for a few hours.
	Address	Alicante
	Corporate Author				Thesis
	Publisher	ACM	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4503-0641-6	Medium
	Area		Expedition		Conference	ICMI
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ ACR2011			Serial	1888
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	Author	Francesco Brughi; Debora Gil; Llorenç Badiella; Eva Jove Casabella; Oriol Ramos Terrades
	Title	Exploring the impact of inter-query variability on the performance of retrieval systems			Type	Conference Article
	Year	2014	Publication	11th International Conference on Image Analysis and Recognition	Abbreviated Journal
	Volume	8814	Issue		Pages	413–420
	Keywords
	Abstract	This paper introduces a framework for evaluating the performance of information retrieval systems. Current evaluation metrics provide an average score that does not consider performance variability across the query set. In this manner, conclusions lack of any statistical significance, yielding poor inference to cases outside the query set and possibly unfair comparisons. We propose to apply statistical methods in order to obtain a more informative measure for problems in which different query classes can be identified. In this context, we assess the performance variability on two levels: overall variability across the whole query set and specific query class-related variability. To this end, we estimate confidence bands for precision-recall curves, and we apply ANOVA in order to assess the significance of the performance across different query classes.
	Address	Algarve; Portugal; October 2014
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-319-11757-7	Medium
	Area		Expedition		Conference	ICIAR
	Notes	IAM; DAG; 600.060; 600.061; 600.077; 600.075			Approved	no
	Call Number	Admin @ si @ BGB2014			Serial	2559
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	Author	Victor Ponce; Hugo Jair Escalante; Sergio Escalera; Xavier Baro
	Title	Gesture and Action Recognition by Evolved Dynamic Subgestures			Type	Conference Article
	Year	2015	Publication	26th British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages	129.1-129.13
	Keywords
	Abstract	This paper introduces a framework for gesture and action recognition based on the evolution of temporal gesture primitives, or subgestures. Our work is inspired on the principle of producing genetic variations within a population of gesture subsequences, with the goal of obtaining a set of gesture units that enhance the generalization capability of standard gesture recognition approaches. In our context, gesture primitives are evolved over time using dynamic programming and generative models in order to recognize complex actions. In few generations, the proposed subgesture-based representation of actions and gestures outperforms the state of the art results on the MSRDaily3D and MSRAction3D datasets.
	Address	Swansea; uk; September 2015
	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	BMVC
	Notes	HuPBA;MV			Approved	no
	Call Number	Admin @ si @ PEE2015			Serial	2657
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	Author	Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo
	Title	Detailed 3D face reconstruction from a single RGB image			Type	Journal
	Year	2019	Publication	Journal of WSCG	Abbreviated Journal	JWSCG
	Volume	27	Issue	2	Pages	103-112
	Keywords	3D Wrinkle Reconstruction; Face Analysis, Optimization.
	Abstract	This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image. To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles.
	Address	2019/11
	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	MSIAU; 600.086; 600.130; 600.122			Approved	no
	Call Number	Admin @ si @			Serial	3708
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	Author	Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria
	Title	Automatic garment retexturing based on infrared information			Type	Journal Article
	Year	2016	Publication	Computers & Graphics	Abbreviated Journal	CG
	Volume	59	Issue		Pages	28-38
	Keywords	Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading
	Abstract	This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA;MILAB;			Approved	no
	Call Number	Admin @ si @ ADT2016			Serial	2759
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