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Author Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados
Title Table detection in business document images by message passing networks Type Journal Article
Year 2022 Publication Pattern Recognition Abbreviated Journal PR
Volume 127 Issue (up) Pages 108641
Keywords
Abstract Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches.
Address July 2022
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 DAG; 600.162; 600.121 Approved no
Call Number Admin @ si @ RGR2022 Serial 3729
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Author Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez
Title Top-down model fitting for hand pose recovery in sequences of depth images Type Journal Article
Year 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS
Volume 79 Issue (up) Pages 63-75
Keywords
Abstract State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs.
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 HUPBA; 600.098 Approved no
Call Number Admin @ si @ MEC2018 Serial 3203
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Author Marc Oliu; Javier Selva; Sergio Escalera
Title Folded Recurrent Neural Networks for Future Video Prediction Type Conference Article
Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal
Volume 11218 Issue (up) 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
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Author Ciprian Corneanu; Meysam Madadi; Sergio Escalera
Title Deep Structure Inference Network for Facial Action Unit Recognition Type Conference Article
Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal
Volume 11216 Issue (up) 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
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Author Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro
Title Residual Stacked RNNs for Action Recognition Type Conference Article
Year 2018 Publication 9th International Workshop on Human Behavior Understanding Abbreviated Journal
Volume Issue (up) 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
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Author Giuseppe Pezzano; Oliver Diaz; Vicent Ribas Ripoll; Petia Radeva
Title CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation Type Journal Article
Year 2021 Publication Computers in Biology and Medicine Abbreviated Journal CBM
Volume 136 Issue (up) Pages 104689
Keywords
Abstract The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19.
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; no menciona Approved no
Call Number Admin @ si @ PDR2021 Serial 3635
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Author Cristina Palmero; Javier Selva; Mohammad Ali Bagheri; Sergio Escalera
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
Volume Issue (up) 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
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Author Gabriela Ramirez; Esau Villatoro; Bogdan Ionescu; Hugo Jair Escalante; Sergio Escalera; Martha Larson; Henning Muller; Isabelle Guyon
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
Volume Issue (up) 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
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Author Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari
Title Review on Emotion Recognition Databases Type Book Chapter
Year 2018 Publication Human-Robot Interaction: Theory and Application Abbreviated Journal
Volume Issue (up) 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
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Author Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya
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
Volume 232 Issue (up) 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
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Author Suman Ghosh
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
Volume Issue (up) 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
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Author Gholamreza Anbarjafari; Sergio Escalera
Title Human-Robot Interaction: Theory and Application Type Book Whole
Year 2018 Publication Human-Robot Interaction: Theory and Application Abbreviated Journal
Volume Issue (up) 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 978-1-78923-316-2 Medium
Area Expedition Conference
Notes HUPBA Approved no
Call Number Admin @ si @ AnE2018 Serial 3216
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Author Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi
Title WiCV 2018: The Fourth Women In Computer Vision Workshop Type Conference Article
Year 2018 Publication 4th Women in Computer Vision Workshop Abbreviated Journal
Volume Issue (up) Pages 1941-19412
Keywords Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning
Abstract We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
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 WiCV
Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ DBR2018 Serial 3222
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Author Arnau Baro; Pau Riba; Alicia Fornes
Title A Starting Point for Handwritten Music Recognition Type Conference Article
Year 2018 Publication 1st International Workshop on Reading Music Systems Abbreviated Journal
Volume Issue (up) Pages 5-6
Keywords Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA
Abstract In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community.
Address Paris; France; 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 WORMS
Notes DAG; 600.097; 601.302; 601.330; 600.121 Approved no
Call Number Admin @ si @ BRF2018 Serial 3223
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Author Laura Lopez-Fuentes; Alessandro Farasin; Harald Skinnemoen; Paolo Garza
Title Deep Learning models for passability detection of flooded roads Type Conference Article
Year 2018 Publication MediaEval 2018 Multimedia Benchmark Workshop Abbreviated Journal
Volume 2283 Issue (up) Pages
Keywords
Abstract In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task.
Address Sophia Antipolis; France; 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 MediaEval
Notes LAMP; 600.084; 600.109; 600.120 Approved no
Call Number Admin @ si @ LFS2018 Serial 3224
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