| Records |
| Author |
Fernando Vilariño; Dimosthenis Karatzas |
| Title |
The Library Living Lab |
Type |
Conference Article |
| Year |
2015 |
Publication |
Open Living Lab Days |
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| Address |
Istanbul; Turkey; August 2015 |
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Conference |
OLLD |
| Notes |
MV; DAG;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ViK2015 |
Serial |
2797 |
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| Author |
Fernando Vilariño; Dimosthenis Karatzas; Marcos Catalan; Alberto Valcarcel |
| Title |
An horizon for the Public Library as a place for innovation and creativity. The Library Living Lab in Volpelleres |
Type |
Book Chapter |
| Year |
2015 |
Publication |
The White Book on Public Library Network from Diputació de Barcelona |
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MV; DAG;SIAI |
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no |
| Call Number |
Admin @ si @VKC2015 |
Serial |
2798 |
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| Author |
Fernando Vilariño |
| Title |
Computer Vision and Performing Arts |
Type |
Conference Article |
| Year |
2015 |
Publication |
Korean Scholars of Marketing Science |
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| Address |
Seoul; Korea; October 2015 |
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Conference |
KAMS |
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @Vil2015 |
Serial |
2799 |
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| Author |
Fernando Vilariño; Dan Norton; Onur Ferhat |
| Title |
Memory Fields: DJs in the Library |
Type |
Conference Article |
| Year |
2015 |
Publication |
21 st Symposium of Electronic Arts |
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Pages |
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| Address |
Vancouver; Canada; August 2015 |
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Conference |
ISEA |
| Notes |
;SIAI |
Approved |
no |
| Call Number |
Admin @ si @VNF2015 |
Serial |
2800 |
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| Author |
Fernando Vilariño; Dan Norton; Onur Ferhat |
| Title |
The Eye Doesn't Click – Eyetracking and Digital Content Interaction |
Type |
Conference Article |
| Year |
2016 |
Publication |
4S/EASST Conference |
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| Address |
Barcelona; Spain; September 2016 |
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Medium |
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Expedition |
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Conference |
EASST |
| Notes |
MV; 600.097;SIAI |
Approved |
no |
| Call Number |
Admin @ si @VNF2016 |
Serial |
2801 |
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| Author |
Fernando Vilariño |
| Title |
Giving Value to digital collections in the Public Library |
Type |
Conference Article |
| Year |
2016 |
Publication |
Librarian 2020 |
Abbreviated Journal |
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Issue |
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Pages |
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| Address |
Brussels; Belgium; October 2016 |
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Conference |
LIB |
| Notes |
MV; 600.097;SIAI |
Approved |
no |
| Call Number |
Admin @ si @Vil2016a |
Serial |
2802 |
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| Author |
Fernando Vilariño; Dimosthenis Karatzas |
| Title |
A Living Lab approach for Citizen Science in Libraries |
Type |
Conference Article |
| Year |
2016 |
Publication |
1st International ECSA Conference |
Abbreviated Journal |
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Issue |
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Pages |
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| Address |
Berlin; Germany; May 2016 |
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Conference |
ECSA |
| Notes |
MV; DAG; 600.084; 600.097;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ViK2016 |
Serial |
2804 |
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| Author |
Fernando Vilariño |
| Title |
Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation |
Type |
Conference Article |
| Year |
2016 |
Publication |
International Workshop on Poetry: Archives, Poetries and Receptions |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Address |
Barcelona; Spain; October 2016 |
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Place of Publication |
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Editor |
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Conference |
POETRY |
| Notes |
MV; 600.097;SIAI |
Approved |
no |
| Call Number |
Admin @ si @Vil2016b |
Serial |
2805 |
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| Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira |
| Title |
Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives |
Type |
Journal Article |
| Year |
2016 |
Publication |
Robotics and Autonomous Systems |
Abbreviated Journal |
RAS |
| Volume |
83 |
Issue |
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Pages |
312-325 |
| Keywords |
Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives |
| Abstract |
When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. |
| Address |
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| Publisher |
Elsevier B.V. |
Place of Publication |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ADAS; 600.086, 600.076 |
Approved |
no |
| Call Number |
Admin @ si @OSS2016a |
Serial |
2806 |
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| Author |
Victor Ponce |
| Title |
Evolutionary Bags of Space-Time Features for Human Analysis |
Type |
Book Whole |
| Year |
2016 |
Publication |
PhD Thesis Universitat de Barcelona, UOC and CVC |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
Computer algorithms; Digital image processing; Digital video; Analysis of variance; Dynamic programming; Evolutionary computation; Gesture |
| Abstract |
The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. In computer vision, a very common representation has adopted the form of the well-known Bag of Visual Words. This representation appears implicitly in most approaches where images are described, and is also present in a huge number of areas and domains: image content retrieval, pedestrian detection, human-computer interaction, surveillance, e-health, and social computing, amongst others. The early stages of this dissertation provide an approach for learning visual representations inside evolutionary algorithms, which consists of evolving weighting schemes to improve the BoVW representations for the task of recognizing categories of videos and images. Thus, we demonstrate the applicability of the most common weighting schemes, which are often used in text mining but are less frequently found in computer vision tasks. Beyond learning these visual representations, we provide an approach based on fusion strategies for learning spatiotemporal representations, from multimodal data obtained by depth sensors. Besides, we specially aim at the evolutionary and dynamic modelling, where the temporal factor is present in the nature of the data, such as video sequences of gestures and actions. Indeed, we explore the effects of probabilistic modelling for those approaches based on dynamic programming, so as to handle the temporal deformation and variance amongst video sequences of different categories. Finally, we integrate dynamic programming and generative models into an evolutionary computation framework, with the aim of learning Bags of SubGestures (BoSG) representations and hence to improve the generalization capability of standard gesture recognition approaches. The results obtained in the experimentation demonstrate, first, that evolutionary algorithms are useful for improving the representation of BoVW approaches in several datasets for recognizing categories in still images and video sequences. On the other hand, our experimentation reveals that both, the use of dynamic programming and generative models to align video sequences, and the representations obtained from applying fusion strategies in multimodal data, entail an enhancement on the performance when recognizing some gesture categories. Furthermore, the combination of evolutionary algorithms with models based on dynamic programming and generative approaches results, when aiming at the classification of video categories on large video datasets, in a considerable improvement over standard gesture and action recognition approaches. Finally, we demonstrate the applications of these representations in several domains for human analysis: classification of images where humans may be present, action and gesture recognition for general applications, and in particular for conversational settings within the field of restorative justice |
| Address |
June 2016 |
| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Sergio Escalera;Xavier Baro;Hugo Jair Escalante |
| Language |
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Summary Language |
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Conference |
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| Notes |
HuPBA |
Approved |
no |
| Call Number |
Pon2016 |
Serial |
2814 |
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| Author |
Cristhian A. Aguilera-Carrasco; F. Aguilera; Angel Sappa; C. Aguilera; Ricardo Toledo |
| Title |
Learning cross-spectral similarity measures with deep convolutional neural networks |
Type |
Conference Article |
| Year |
2016 |
Publication |
29th IEEE Conference on Computer Vision and Pattern Recognition Worshops |
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Issue |
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Pages |
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| Abstract |
The simultaneous use of images from different spectracan be helpful to improve the performance of many computer vision tasks. The core idea behind the usage of crossspectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN architectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Experimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Additionally, our experiments show that some CNN architectures are capable of generalizing between different crossspectral domains. |
| Address |
Las vegas; USA; June 2016 |
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Conference |
CVPRW |
| Notes |
ADAS; 600.086; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @AAS2016 |
Serial |
2809 |
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| Author |
Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo |
| Title |
Monocular visual odometry: A cross-spectral image fusion based approach |
Type |
Journal Article |
| Year |
2016 |
Publication |
Robotics and Autonomous Systems |
Abbreviated Journal |
RAS |
| Volume |
85 |
Issue |
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Pages |
26-36 |
| Keywords |
Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion |
| Abstract |
This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. |
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Elsevier B.V. |
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| Notes |
ADAS;600.086; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @SAC2016 |
Serial |
2811 |
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| Author |
Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure |
| Title |
Slanted Stixels: Representing San Francisco's Steepest Streets} |
Type |
Conference Article |
| Year |
2017 |
Publication |
28th British Machine Vision Conference |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. |
| Address |
London; uk; September 2017 |
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Conference |
BMVC |
| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ HSE2017a |
Serial |
2945 |
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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 |
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Issue |
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Pages |
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| 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. |
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Conference |
WMT |
| Notes |
LAMP; 600.106; 600.120 |
Approved |
no |
| Call Number |
Admin @ si @ CAB2017 |
Serial |
3035 |
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| Author |
Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville |
| Title |
PixelVAE: A Latent Variable Model for Natural Images |
Type |
Conference Article |
| Year |
2017 |
Publication |
5th International Conference on Learning Representations |
Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
Deep Learning; Unsupervised Learning |
| Abstract |
Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms. |
| Address |
Toulon; France; April 2017 |
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Conference |
ICLR |
| Notes |
ADAS; 600.085; 600.076; 601.281; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ GKA2017 |
Serial |
2815 |
| Permanent link to this record |
