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Author Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer edit   pdf
openurl 
  Title Does Multimodality Help Human and Machine for Translation and Image Captioning? Type Conference Article
  Year 2016 Publication 1st conference on machine translation Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR.  
  Address Berlin; Germany; August 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 (down) WMT  
  Notes LAMP; 600.106 ; 600.068 Approved no  
  Call Number Admin @ si @ CAW2016 Serial 2761  
Permanent link to this record
 

 
Author Jose Ramirez Moreno; Juan R Revilla; Miguel Reyes; Sergio Escalera edit  openurl
  Title Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal Type Conference Article
  Year 2016 Publication 4th Congreso WCPT-SAR Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Buenos Aires; Argentina; June 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 (down) WCPT-SAR  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ RRR2016 Serial 2853  
Permanent link to this record
 

 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera edit   pdf
doi  openurl
  Title Support Vector Machines with Time Series Distance Kernels for Action Classification Type Conference Article
  Year 2016 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 1-7  
  Keywords  
  Abstract Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function.
Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are
employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-ofthe-art on the considered datasets.
 
  Address Lake Placid; NY (USA); March 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 (down) WACV  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ BGE2016a Serial 2773  
Permanent link to this record
 

 
Author C. Alejandro Parraga; Arash Akbarinia edit   pdf
doi  openurl
  Title Colour Constancy as a Product of Dynamic Centre-Surround Adaptation Type Conference Article
  Year 2016 Publication 16th Annual meeting in Vision Sciences Society Abbreviated Journal  
  Volume 16 Issue 12 Pages  
  Keywords  
  Abstract Colour constancy refers to the human visual system's ability to preserve the perceived colour of objects despite changes in the illumination. Its exact mechanisms are unknown, although a number of systems ranging from retinal to cortical and memory are thought to play important roles. The strength of the perceptual shift necessary to preserve these colours is usually estimated by the vectorial distances from an ideal match (or canonical illuminant). In this work we explore how much of the colour constancy phenomenon could be explained by well-known physiological properties of V1 and V2 neurons whose receptive fields (RF) vary according to the contrast and orientation of surround stimuli. Indeed, it has been shown that both RF size and the normalization occurring between centre and surround in cortical neurons depend on the local properties of surrounding stimuli. Our stating point is the construction of a computational model which includes this dynamical centre-surround adaptation by means of two overlapping asymmetric Gaussian kernels whose variances are adjusted to the contrast of surrounding pixels to represent the changes in RF size of cortical neurons and the weights of their respective contributions are altered according to differences in centre-surround contrast and orientation. The final output of the model is obtained after convolving an image with this dynamical operator and an estimation of the illuminant is obtained by considering the contrast of the far surround. We tested our algorithm on naturalistic stimuli from several benchmark datasets. Our results show that although our model does not require any training, its performance against the state-of-the-art is highly competitive, even outperforming learning-based algorithms in some cases. Indeed, these results are very encouraging if we consider that they were obtained with the same parameters for all datasets (i.e. just like the human visual system operates).  
  Address Florida; USA; May 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 (down) VSS  
  Notes NEUROBIT Approved no  
  Call Number Admin @ si @ PaA2016b Serial 2901  
Permanent link to this record
 

 
Author Antoni Gurgui; Debora Gil; Enric Marti; Vicente Grau edit  doi
openurl 
  Title Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain Type Conference Article
  Year 2016 Publication 7th International Workshop on Statistical Atlases & Computational Modelling of the Heart Abbreviated Journal  
  Volume 10124 Issue Pages 163-171  
  Keywords Laplacian; Constrained maps; Parameterization; Basal ring  
  Abstract Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries.  
  Address Athens; October 2016  
  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 (down) STACOM  
  Notes IAM; Approved no  
  Call Number Admin @ si @ GGM2016 Serial 2884  
Permanent link to this record
 

 
Author Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil edit   pdf
openurl 
  Title Stable Airway Center Tracking for Bronchoscopic Navigation Type Conference Article
  Year 2016 Publication 28th Conference of the international Society for Medical Innovation and Technology Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Bronchoscopists use X‐ray fluoroscopy to guide bronchoscopes to the lesion to be biopsied without any kind of incisions. Reducing exposure to X‐ray is important for both patients and doctors but alternatives like electromagnetic navigation require specific equipment and increase the cost of the clinical procedure. We propose a guiding system based on the extraction of airway centers from intra‐operative videos. Such anatomical landmarks could be
matched to the airway centerline extracted from a pre‐planned CT to indicate the best path to the lesion. We present an extraction of lumen centers
from intra‐operative videos based on tracking of maximal stable regions of energy maps.
 
  Address Delft; Rotterdam; Leiden; The Netherlands; October 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 (down) SMIT  
  Notes IAM; Approved no  
  Call Number Admin @ si @ LSB2016a Serial 2856  
Permanent link to this record
 

 
Author Carles Sanchez; Debora Gil; T. Gache; N. Koufos; Marta Diez-Ferrer; Antoni Rosell edit   pdf
openurl 
  Title SENSA: a System for Endoscopic Stenosis Assessment Type Conference Article
  Year 2016 Publication 28th Conference of the international Society for Medical Innovation and Technology Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Documenting the severity of a static or dynamic Central Airway Obstruction (CAO) is crucial to establish proper diagnosis and treatment, predict possible treatment effects and better follow-up the patients. The subjective visual evaluation of a stenosis during video-bronchoscopy still remains the most common way to assess a CAO in spite of a consensus among experts for a need to standardize all calculations [1].
The Computer Vision Center in cooperation with the «Hospital de Bellvitge», has developed a System for Endoscopic Stenosis Assessment (SENSA), which computes CAO directly by analyzing standard bronchoscopic data without the need of using other imaging tecnologies.
 
  Address Rotterdam; The Netherlands; October 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 (down) SMIT  
  Notes IAM; Approved no  
  Call Number Admin @ si @ SGG2016 Serial 2942  
Permanent link to this record
 

 
Author Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras edit  doi
openurl 
  Title Information Theoretic Rotationwise Robust Binary Descriptor Learning Type Conference Article
  Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal  
  Volume Issue Pages 368-378  
  Keywords  
  Abstract In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications.  
  Address Mérida; Mexico; November 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 (down) S+SSPR  
  Notes DAG; ADAS; 600.097; 600.086 Approved no  
  Call Number Admin @ si @ RLL2016 Serial 2871  
Permanent link to this record
 

 
Author Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal edit   pdf
doi  openurl
  Title Local Binary Pattern for Word Spotting in Handwritten Historical Document Type Conference Article
  Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal  
  Volume Issue Pages 574-583  
  Keywords Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data  
  Abstract Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm.  
  Address Merida; Mexico; December 2016  
  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 (down) S+SSPR  
  Notes DAG; 600.097; 602.006; 603.053 Approved no  
  Call Number Admin @ si @ DNL2016 Serial 2876  
Permanent link to this record
 

 
Author Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados edit   pdf
url  isbn
openurl 
  Title Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling Type Conference Article
  Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal  
  Volume 10029 Issue Pages 543-552  
  Keywords Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection  
  Abstract The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results.  
  Address Merida; Mexico; December 2016  
  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 ISBN 978-3-319-49054-0 Medium  
  Area Expedition Conference (down) S+SSPR  
  Notes DAG; 600.097; 602.006 Approved no  
  Call Number Admin @ si @ TSF2016 Serial 2877  
Permanent link to this record
 

 
Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades edit  openurl
  Title Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary Type Book Chapter
  Year 2016 Publication Recent Trends in Image Processing and Pattern Recognition Abbreviated Journal  
  Volume 709 Issue Pages  
  Keywords  
  Abstract  
  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 (down) RTIP2R  
  Notes DAG Approved no  
  Call Number Admin @ si @ HTR2016 Serial 2956  
Permanent link to this record
 

 
Author Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez edit   pdf
openurl 
  Title Stereo Matching using SGM on the GPU Type Report
  Year 2016 Publication Programming and Tuning Massively Parallel Systems Abbreviated Journal PUMPS  
  Volume Issue Pages  
  Keywords CUDA; Stereo; Autonomous Vehicle  
  Abstract Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.  
  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 (down) PUMPS  
  Notes ADAS; 600.085; 600.087; 600.076 Approved no  
  Call Number ADAS @ adas @ HCE2016b Serial 2776  
Permanent link to this record
 

 
Author Fernando Vilariño edit  openurl
  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  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Barcelona; Spain; October 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 (down) POETRY  
  Notes MV; 600.097;SIAI Approved no  
  Call Number Admin @ si @Vil2016b Serial 2805  
Permanent link to this record
 

 
Author G. de Oliveira; A. Cartas; Marc Bolaños; Mariella Dimiccoli; Xavier Giro; Petia Radeva edit   pdf
openurl 
  Title LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task Type Conference Article
  Year 2016 Publication 12th NTCIR Conference on Evaluation of Information Access Technologies Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.  
  Address Tokyo; Japan; June 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 (down) NTCIR  
  Notes MILAB; Approved no  
  Call Number Admin @ si @OCB2016 Serial 2789  
Permanent link to this record
 

 
Author Yaxing Wang; L. Zhang; Joost Van de Weijer edit   pdf
openurl 
  Title Ensembles of generative adversarial networks Type Conference Article
  Year 2016 Publication 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Ensembles are a popular way to improve results of discriminative CNNs. The
combination of several networks trained starting from different initializations
improves results significantly. In this paper we investigate the usage of ensembles of GANs. The specific nature of GANs opens up several new ways to construct ensembles. The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal. As such ensembles of GANs can be constructed based on the same network initialization but just taking models which have different amount of iterations. These so-called self ensembles are much faster to train than traditional ensembles. The second method, called cascade GANs, redirects part of the training data which is badly modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset we show that ensembles of GANs obtain model probability distributions which better model the data distribution. In addition, we show that these improved results can be obtained at little additional computational cost.
 
  Address Barcelona; Spain; December 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 (down) NIPSW  
  Notes LAMP; 600.068 Approved no  
  Call Number Admin @ si @ WZW2016 Serial 2905  
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