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Author Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari
Title (up) Dominant and Complementary Emotion Recognition from Still Images of Faces Type Journal Article
Year 2018 Publication IEEE Access Abbreviated Journal ACCESS
Volume 6 Issue Pages 26391 - 26403
Keywords
Abstract Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ GLW2018 Serial 3122
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Author Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone
Title (up) DSD: document sparse-based denoising algorithm Type Journal Article
Year 2019 Publication Pattern Analysis and Applications Abbreviated Journal PAA
Volume 22 Issue 1 Pages 177–186
Keywords Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models
Abstract In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising.
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Notes DAG; 600.097; 600.140; 600.121 Approved no
Call Number Admin @ si @ DRT2019 Serial 3254
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Author Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera
Title (up) Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps Type Journal Article
Year 2019 Publication IEEE Transactions on Circuits and Systems for Video Technology Abbreviated Journal TCSVT
Volume 29 Issue 6 Pages 1729-1740
Keywords Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding
Abstract Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset.
Address June 2019,
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ AAK2018 Serial 3213
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Author Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira
Title (up) Dynamic Comparison of Headlights Type Journal Article
Year 2008 Publication Journal of Automobile Engineering Abbreviated Journal
Volume 222 Issue 5 Pages 643–656
Keywords video alignment
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Notes ADAS Approved no
Call Number ADAS @ adas @ SDL2008a Serial 958
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Author Debora Gil; Aura Hernandez-Sabate; Julien Enconniere; Saryani Asmayawati; Pau Folch; Juan Borrego-Carazo; Miquel Angel Piera
Title (up) E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights Type Journal Article
Year 2022 Publication IEEE Access Abbreviated Journal ACCESS
Volume 10 Issue Pages 7489-7503
Keywords
Abstract More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.
This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.
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Notes IAM; 600.139; 600.118; 600.145 Approved no
Call Number Admin @ si @ GHE2022 Serial 3721
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Author Francesco Ciompi; Oriol Pujol; Petia Radeva
Title (up) ECOC-DRF: Discriminative random fields based on error correcting output codes Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 6 Pages 2193-2204
Keywords Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models
Abstract We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.
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Notes LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 Approved no
Call Number Admin @ si @ CPR2014b Serial 2470
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Author J. Mauri; E Fernandez-Nofrerias; A. Tovar; E. Martinez; L. Cano; V. Valle; David Rotger; Cristina Cañero; Petia Radeva
Title (up) Ecografia Intracoronaria: Un Nou Pas, la Fusio de Imatges amb la Angiografia, el Software. Type Journal Article
Year 2001 Publication Revista de la Societat Catalana de Cardiologia, XIIIe Congres de la Societat Catalana de Cardiologia, 4(1):48. Abbreviated Journal
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ MFT2001 Serial 136
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Author Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla
Title (up) Editorial: Special Issue on Computational Intelligence for Vision and Robotics Type Journal Article
Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications
Volume 28 Issue 5 Pages 853–854
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Notes HuPBA;MILAB; no menciona Approved no
Call Number Admin @ si @ GGE2017 Serial 2845
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Author Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil
Title (up) EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment Type Journal Article
Year 2024 Publication Sensors Abbreviated Journal SENS
Volume 24 Issue 4 Pages 1174
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Abstract High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.
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Notes IAM Approved no
Call Number Admin @ si @ HYF2024 Serial 4019
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Author Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier
Title (up) Effects of Non-Driving Related Tasks during Self-Driving mode Type Journal Article
Year 2022 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 23 Issue 2 Pages 1391-1399
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Abstract Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study.
Address Feb. 2022
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Notes IAM; 600.139; 600.145 Approved no
Call Number Admin @ si @ MHE2022 Serial 3468
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Author Judit Martinez; F. Thomas
Title (up) Efficient Computation of Local Geometric Moments Type Journal Article
Year 2002 Publication IEEE Transactions on Image Porcessing, (IF: 2.553) Abbreviated Journal
Volume 11 Issue 9 Pages 1102-1111
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Notes Approved no
Call Number Admin @ si @ MaT2002 Serial 271
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Author Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca
Title (up) Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications Type Journal Article
Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 32 Issue 13 Pages 1581-1587
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Abstract Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods.

This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one.

In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster.
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Notes ISE Approved no
Call Number Admin @ si @ PGB2011a Serial 1707
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Author Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados
Title (up) Efficient segmentation-free keyword spotting in historical document collections Type Journal Article
Year 2015 Publication Pattern Recognition Abbreviated Journal PR
Volume 48 Issue 2 Pages 545–555
Keywords Historical documents; Keyword spotting; Segmentation-free; Dense SIFT features; Latent semantic analysis; Product quantization
Abstract In this paper we present an efficient segmentation-free word spotting method, applied in the context of historical document collections, that follows the query-by-example paradigm. We use a patch-based framework where local patches are described by a bag-of-visual-words model powered by SIFT descriptors. By projecting the patch descriptors to a topic space with the latent semantic analysis technique and compressing the descriptors with the product quantization method, we are able to efficiently index the document information both in terms of memory and time. The proposed method is evaluated using four different collections of historical documents achieving good performances on both handwritten and typewritten scenarios. The yielded performances outperform the recent state-of-the-art keyword spotting approaches.
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Notes DAG; ADAS; 600.076; 600.077; 600.061; 601.223; 602.006; 600.055 Approved no
Call Number Admin @ si @ RAT2015a Serial 2544
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Author Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva
Title (up) Egocentric video description based on temporally-linked sequences Type Journal Article
Year 2018 Publication Journal of Visual Communication and Image Representation Abbreviated Journal JVCIR
Volume 50 Issue Pages 205-216
Keywords egocentric vision; video description; deep learning; multi-modal learning
Abstract Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ BPC2018 Serial 3109
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Author Jordi Vitria; Petia Radeva; X. Binefa
Title (up) EigenHistograms: using low dimensional models of color distribution for real time object recognition Type Journal Article
Year 1999 Publication Abbreviated Journal
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Address Ljubliana, Slovenia, Springer-Verlag
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ VRB1999a Serial 29
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