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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla edit   pdf
isbn  openurl
  Title Cross-spectral image dehaze through a dense stacked conditional GAN based approach Type Conference Article
  Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet Based System Abbreviated Journal (up)  
  Volume Issue Pages  
  Keywords Infrared imaging; Dense; Stacked CGAN; Crossspectral; Convolutional networks  
  Abstract This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented
receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors
and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
 
  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 978-1-5386-9385-8 Medium  
  Area Expedition Conference SITIS  
  Notes MSIAU; 600.086; 600.130; 600.122 Approved no  
  Call Number Admin @ si @ SSV2018a Serial 3193  
Permanent link to this record
 

 
Author Jorge Charco; Boris X. Vintimilla; Angel Sappa edit   pdf
openurl 
  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 (up)  
  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  
Permanent link to this record
 

 
Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud edit   pdf
doi  openurl
  Title Near InfraRed Imagery Colorization Type Conference Article
  Year 2018 Publication 25th International Conference on Image Processing Abbreviated Journal (up)  
  Volume Issue Pages 2237 - 2241  
  Keywords Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization  
  Abstract This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics.
 
  Address Athens; Greece; 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 ICIP  
  Notes MSIAU; 600.086; 600.130; 600.122 Approved no  
  Call Number Admin @ si @ SSV2018b Serial 3195  
Permanent link to this record
 

 
Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla edit   pdf
url  openurl
  Title Vegetation Index Estimation from Monospectral Images Type Conference Article
  Year 2018 Publication 15th International Conference on Images Analysis and Recognition Abbreviated Journal (up)  
  Volume 10882 Issue Pages 353-362  
  Keywords  
  Abstract This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index.
 
  Address Povoa de Varzim; Portugal; June 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 ICIAR  
  Notes MSIAU; 600.086; 600.130; 600.122 Approved no  
  Call Number Admin @ si @ SSV2018c Serial 3196  
Permanent link to this record
 

 
Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud edit   pdf
doi  openurl
  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 (up)  
  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  
Permanent link to this record
 

 
Author Sergio Escalera; Markus Weimer; Mikhail Burtsev; Valentin Malykh; Varvara Logacheva; Ryan Lowe; Iulian Vlad Serban; Yoshua Bengio; Alexander Rudnicky; Alan W. Black; Shrimai Prabhumoye; Łukasz Kidzinski; Mohanty Sharada; Carmichael Ong; Jennifer Hicks; Sergey Levine; Marcel Salathe; Scott Delp; Iker Huerga; Alexander Grigorenko; Leifur Thorbergsson; Anasuya Das; Kyla Nemitz; Jenna Sandker; Stephen King; Alexander S. Ecker; Leon A. Gatys; Matthias Bethge; Jordan Boyd Graber; Shi Feng; Pedro Rodriguez; Mohit Iyyer; He He; Hal Daume III; Sean McGregor; Amir Banifatemi; Alexey Kurakin; Ian Goodfellow; Samy Bengio edit  url
isbn  openurl
  Title Introduction to NIPS 2017 Competition Track Type Book Chapter
  Year 2018 Publication The NIPS ’17 Competition: Building Intelligent Systems Abbreviated Journal (up)  
  Volume Issue Pages 1-23  
  Keywords  
  Abstract Competitions have become a popular tool in the data science community to solve hard problems, assess the state of the art and spur new research directions. Companies like Kaggle and open source platforms like Codalab connect people with data and a data science problem to those with the skills and means to solve it. Hence, the question arises: What, if anything, could NIPS add to this rich ecosystem?

In 2017, we embarked to find out. We attracted 23 potential competitions, of which we selected five to be NIPS 2017 competitions. Our final selection features competitions advancing the state of the art in other sciences such as “Classifying Clinically Actionable Genetic Mutations” and “Learning to Run”. Others, like “The Conversational Intelligence Challenge” and “Adversarial Attacks and Defences” generated new data sets that we expect to impact the progress in their respective communities for years to come. And “Human-Computer Question Answering Competition” showed us just how far we as a field have come in ability and efficiency since the break-through performance of Watson in Jeopardy. Two additional competitions, DeepArt and AI XPRIZE Milestions, were also associated to the NIPS 2017 competition track, whose results are also presented within this chapter.
 
  Address  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor Sergio Escalera; Markus Weimer  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-319-94042-7 Medium  
  Area Expedition Conference  
  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ EWB2018 Serial 3200  
Permanent link to this record
 

 
Author Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes edit   pdf
url  openurl
  Title Lost in Transcription of Graphic Signs in Ciphers Type Conference Article
  Year 2022 Publication International Conference on Historical Cryptology (HistoCrypt 2022) Abbreviated Journal (up)  
  Volume Issue Pages 153-158  
  Keywords transcription of ciphers; hand-written text recognition of symbols; graphic signs  
  Abstract Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings.  
  Address Amsterdam, Netherlands, June 20-22, 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 HystoCrypt  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ MBS2022 Serial 3731  
Permanent link to this record
 

 
Author Marc Oliu; Javier Selva; Sergio Escalera edit   pdf
url  openurl
  Title Folded Recurrent Neural Networks for Future Video Prediction Type Conference Article
  Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal (up)  
  Volume 11218 Issue Pages 745-761  
  Keywords  
  Abstract Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach.  
  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 HUPBA; no menciona Approved no  
  Call Number Admin @ si @ OSE2018 Serial 3204  
Permanent link to this record
 

 
Author Ciprian Corneanu; Meysam Madadi; Sergio Escalera edit   pdf
url  openurl
  Title Deep Structure Inference Network for Facial Action Unit Recognition Type Conference Article
  Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal (up)  
  Volume 11216 Issue Pages 309-324  
  Keywords Computer Vision; Machine Learning; Deep Learning; Facial Expression Analysis; Facial Action Units; Structure Inference  
  Abstract Facial expressions are combinations of basic components called Action Units (AU). Recognizing AUs is key for general facial expression analysis. Recently, efforts in automatic AU recognition have been dedicated to learning combinations of local features and to exploiting correlations between AUs. We propose a deep neural architecture that tackles both problems by combining learned local and global features in its initial stages and replicating a message passing algorithm between classes similar to a graphical model inference approach in later stages. We show that by training the model end-to-end with increased supervision we improve state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets, respectively.  
  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 HUPBA; no proj Approved no  
  Call Number Admin @ si @ CME2018 Serial 3205  
Permanent link to this record
 

 
Author Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro edit   pdf
url  openurl
  Title Residual Stacked RNNs for Action Recognition Type Conference Article
  Year 2018 Publication 9th International Workshop on Human Behavior Understanding Abbreviated Journal (up)  
  Volume Issue Pages 534-548  
  Keywords Action recognition; Deep residual learning; Two-stream RNN  
  Abstract Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset.  
  Address Munich; September 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 ECCVW  
  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ LCE2018b Serial 3206  
Permanent link to this record
 

 
Author Cristina Palmero; Javier Selva; Mohammad Ali Bagueri; Sergio Escalera edit   pdf
openurl 
  Title Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues Type Conference Article
  Year 2018 Publication 29th British Machine Vision Conference Abbreviated Journal (up)  
  Volume Issue Pages  
  Keywords  
  Abstract Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included.
 
  Address Newcastle; UK; September 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 BMVC  
  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ PSB2018 Serial 3208  
Permanent link to this record
 

 
Author Gabriela Ramirez; Esau Villatoro; Bogdan Ionescu; Hugo Jair Escalante; Sergio Escalera; Martha Larson; Henning Muller; Isabelle Guyon edit  openurl
  Title Overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contes Type Conference Article
  Year 2018 Publication Multimedia Information Processing for Personality and Social Networks Analysis (MIPPSNA 2018) Abbreviated Journal (up)  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Beijing; China; 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 ICPRW  
  Notes HUPBA Approved no  
  Call Number Admin @ si @ RVI2018 Serial 3211  
Permanent link to this record
 

 
Author Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari edit  doi
isbn  openurl
  Title Review on Emotion Recognition Databases Type Book Chapter
  Year 2018 Publication Human-Robot Interaction: Theory and Application Abbreviated Journal (up)  
  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  
Permanent link to this record
 

 
Author Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya edit  openurl
  Title Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” Type Journal
  Year 2018 Publication Informaciones Psiquiatricas Abbreviated Journal (up)  
  Volume 232 Issue Pages 47-59  
  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 0210-7279 ISBN Medium  
  Area Expedition Conference  
  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ FAA2018 Serial 3214  
Permanent link to this record
 

 
Author Suman Ghosh edit  isbn
openurl 
  Title Word Spotting and Recognition in Images from Heterogeneous Sources A Type Book Whole
  Year 2018 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal (up)  
  Volume Issue Pages  
  Keywords  
  Abstract Text is the most common way of information sharing from ages. With recent development of personal images databases and handwritten historic manuscripts the demand for algorithms to make these databases accessible for browsing and indexing are in rise. Enabling search or understanding large collection of manuscripts or image databases needs fast and robust methods. Researchers have found different ways to represent cropped words for understanding and matching, which works well when words are already segmented. However there is no trivial way to extend these for non-segmented documents. In this thesis we explore different methods for text retrieval and recognition from unsegmented document and scene images. Two different ways of representation exist in literature, one uses a fixed length representation learned from cropped words and another a sequence of features of variable length. Throughout this thesis, we have studied both these representation for their suitability in segmentation free understanding of text. In the first part we are focused on segmentation free word spotting using a fixed length representation. We extended the use of the successful PHOC (Pyramidal Histogram of Character) representation to segmentation free retrieval. In the second part of the thesis, we explore sequence based features and finally, we propose a unified solution where the same framework can generate both kind of representations.  
  Address November 2018  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Ernest Valveny  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-948531-0-4 Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ Gho2018 Serial 3217  
Permanent link to this record
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