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Author Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo edit   pdf
openurl 
  Title RGBN Multispectral Images: a Novel Color Restoration Approach Type Conference Article
  Year 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent System Abbreviated Journal  
  Volume Issue Pages  
  Keywords Multispectral Imaging; Free Sensor Model; Neural Network  
  Abstract This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided.
 
  Address Porto; Portugal; June 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference PAAMS  
  Notes ADAS; MSIAU; 600.118; 600.122 Approved no  
  Call Number Admin @ si @ ASS2017 Serial (down) 2918  
Permanent link to this record
 

 
Author Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa edit   pdf
openurl 
  Title A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers Type Conference Article
  Year 2017 Publication IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point
that minimizes the vacuum force needed to guarantee a grasp.
Experimental results in real scenarios are presented to show the validity of the proposed approach.
 
  Address San Sebastian; Spain; May 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECMSM  
  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ VIS2017 Serial (down) 2917  
Permanent link to this record
 

 
Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla edit   pdf
doi  openurl
  Title Cross-Spectral Image Patch Similarity using Convolutional Neural Network Type Conference Article
  Year 2017 Publication IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach.
 
  Address San Sebastian; Spain; May 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECMSM  
  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ SSV2017a Serial (down) 2916  
Permanent link to this record
 

 
Author Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo edit   pdf
doi  openurl
  Title Cross-Spectral Local Descriptors via Quadruplet Network Type Journal Article
  Year 2017 Publication Sensors Abbreviated Journal SENS  
  Volume 17 Issue 4 Pages 873  
  Keywords  
  Abstract This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ ASA2017 Serial (down) 2914  
Permanent link to this record
 

 
Author Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa edit   pdf
doi  openurl
  Title Fine-tuning based deep convolutional networks for lepidopterous genus recognition Type Conference Article
  Year 2016 Publication 21st Ibero American Congress on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 467-475  
  Keywords  
  Abstract This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%.  
  Address Lima; Perú; November 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 CIARP  
  Notes ADAS; 600.086 Approved no  
  Call Number Admin @ si @ CRS2016 Serial (down) 2913  
Permanent link to this record
 

 
Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira edit   pdf
url  openurl
  Title Incremental texture mapping for autonomous driving Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Abbreviated Journal RAS  
  Volume 84 Issue Pages 113-128  
  Keywords Scene reconstruction; Autonomous driving; Texture mapping  
  Abstract Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.  
  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 Approved no  
  Call Number Admin @ si @ OSS2016b Serial (down) 2912  
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Author Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas edit  openurl
  Title Coompetitions in machine learning: case studies Type Conference Article
  Year 2016 Publication 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  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 NIPSW  
  Notes HuPBA Approved no  
  Call Number Admin @ si @ BEG2016 Serial (down) 2911  
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Author Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari edit  doi
openurl 
  Title Human Head Pose Estimation on SASE database using Random Hough Regression Forests Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Workshops Abbreviated Journal  
  Volume 10165 Issue Pages  
  Keywords  
  Abstract In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests.  
  Address Cancun; 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 ICPRW  
  Notes HuPBA; Approved no  
  Call Number Admin @ si @ LEA2016b Serial (down) 2910  
Permanent link to this record
 

 
Author Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez edit   pdf
openurl 
  Title Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books Type Conference Article
  Year 2016 Publication 15th international conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Handwritten marriage licenses books have been used for centuries by ecclesiastical and secular institutions to register marriages. The information contained in these historical documents is useful for demography studies and
genealogical research, among others. Despite the generally simple structure of the text in these documents, automatic transcription and semantic information extraction is difficult due to the distinct and evolutionary vocabulary, which is composed mainly of proper names that change along the time. In previous
works we studied the use of category-based language models to both improve the automatic transcription accuracy and make easier the extraction of semantic information. Here we analyze the main causes of the semantic errors observed in previous results and apply a Grammatical Inference technique known as MGGI to improve the semantic accuracy of the language model obtained. Using this language model, full handwritten text recognition experiments have been carried out, with results supporting the interest of the proposed approach.
 
  Address Shenzhen; China; 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 ICFHR  
  Notes DAG; 600.097; 602.006 Approved no  
  Call Number Admin @ si @ RFV2016 Serial (down) 2909  
Permanent link to this record
 

 
Author Oriol Vicente; Alicia Fornes; Ramon Valdes edit   pdf
openurl 
  Title The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities Type Conference Article
  Year 2016 Publication Digital Humanities Centres: Experiences and Perspectives Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Warsaw; Poland; 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 DHLABS  
  Notes DAG; 600.097 Approved no  
  Call Number Admin @ si @ VFV2016 Serial (down) 2908  
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Author Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre edit   pdf
isbn  openurl
  Title Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources Type Book Chapter
  Year 2016 Publication The future of historical demography. Upside down and inside out Abbreviated Journal  
  Volume Issue Pages 127-131  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Acco Publishers Place of Publication Editor K.Matthijs; S.Hin; H.Matsuo; J.Kok  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-94-6292-722-3 Medium  
  Area Expedition Conference  
  Notes DAG; 600.097 Approved no  
  Call Number Admin @ si @ PFL2016 Serial (down) 2907  
Permanent link to this record
 

 
Author Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez edit   pdf
openurl 
  Title Invertible conditional gans for image editing Type Conference Article
  Year 2016 Publication 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications.
 
  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 NIPSW  
  Notes LAMP; ADAS; 600.068 Approved no  
  Call Number Admin @ si @ PWR2016 Serial (down) 2906  
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 NIPSW  
  Notes LAMP; 600.068 Approved no  
  Call Number Admin @ si @ WZW2016 Serial (down) 2905  
Permanent link to this record
 

 
Author Arnau Baro; Pau Riba; Alicia Fornes edit   pdf
doi  openurl
  Title Towards the recognition of compound music notes in handwritten music scores Type Conference Article
  Year 2016 Publication 15th international conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work we focus on this second problem and propose a method based on perceptual grouping for the recognition of compound music notes. Our method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition (OMR) software. Given that our method is learning-free, the obtained results are promising.  
  Address Shenzhen; China; 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 2167-6445 ISBN Medium  
  Area Expedition Conference ICFHR  
  Notes DAG; 600.097 Approved no  
  Call Number Admin @ si @ BRF2016 Serial (down) 2903  
Permanent link to this record
 

 
Author Marco Bellantonio; Mohammad A. Haque; Pau Rodriguez; Kamal Nasrollahi; Taisi Telve; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund; Pejman Rasti; Golamreza Anbarjafari edit  doi
openurl 
  Title Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal  
  Volume 10165 Issue Pages  
  Keywords  
  Abstract Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution.  
  Address Cancun; 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 ICPR  
  Notes HuPBA; ISE; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ BHR2016 Serial (down) 2902  
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