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Author Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva
Title 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 (up) 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 Enrique Cabello; Cristina Conde; Angel Serrano; Licesio Rodriguez; David Vazquez
Title Empleo de sistemas biométricos para el reconocimiento de personas en aeropuertos Type Journal Article
Year 2006 Publication Instituto Universitario de Investigación sobre Seguridad Interior (IUSI 2006) Abbreviated Journal
Volume Issue Pages
Keywords Surveillance; Face detection; Face recognition
Abstract (up) El presente proyecto se desarrolló a lo largo del año 2005, probando un prototipo de un sistema de verificación facial con imágenes extraídas de las cámaras de video vigilancia del aeropuerto de Barajas. Se diseñaron varios experimentos, agrupados en dos clases. En el primer tipo, el sistema es entrenado con imágenes obtenidas en condiciones de laboratorio y luego probado con imágenes extraídas de las cámaras de video vigilancia del aeropuerto de Barajas. En el segundo caso, tanto las imágenes de entrenamiento como las de prueba corresponden a imágenes extraídas de Barajas. Se ha desarrollado un sistema completo, que incluye adquisición y digitalización de las imágenes, localización y recorte de las caras en escena, verificación de sujetos y obtención de resultados. Los resultados muestran, que, en general, un sistema de verificación facial basado en imágenes puede ser una ayuda a un operario que deba estar vigilando amplias zonas.
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Notes invisible;ADAS Approved no
Call Number ADAS @ adas @ CCS2006a Serial 1672
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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 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 (up) 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 Debora Gil; Rosa Maria Ortiz; Carles Sanchez; Antoni Rosell
Title Objective endoscopic measurements of central airway stenosis. A pilot study Type Journal Article
Year 2018 Publication Respiration Abbreviated Journal RES
Volume 95 Issue Pages 63–69
Keywords Bronchoscopy; Tracheal stenosis; Airway stenosis; Computer-assisted analysis
Abstract (up) Endoscopic estimation of the degree of stenosis in central airway obstruction is subjective and highly variable. Objective: To determine the benefits of using SENSA (System for Endoscopic Stenosis Assessment), an image-based computational software, for obtaining objective stenosis index (SI) measurements among a group of expert bronchoscopists and general pulmonologists. Methods: A total of 7 expert bronchoscopists and 7 general pulmonologists were enrolled to validate SENSA usage. The SI obtained by the physicians and by SENSA were compared with a reference SI to set their precision in SI computation. We used SENSA to efficiently obtain this reference SI in 11 selected cases of benign stenosis. A Web platform with three user-friendly microtasks was designed to gather the data. The users had to visually estimate the SI from videos with and without contours of the normal and the obstructed area provided by SENSA. The users were able to modify the SENSA contours to define the reference SI using morphometric bronchoscopy. Results: Visual SI estimation accuracy was associated with neither bronchoscopic experience (p = 0.71) nor the contours of the normal and the obstructed area provided by the system (p = 0.13). The precision of the SI by SENSA was 97.7% (95% CI: 92.4-103.7), which is significantly better than the precision of the SI by visual estimation (p < 0.001), with an improvement by at least 15%. Conclusion: SENSA provides objective SI measurements with a precision of up to 99.5%, which can be calculated from any bronchoscope using an affordable scalable interface. Providing normal and obstructed contours on bronchoscopic videos does not improve physicians' visual estimation of the SI.
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Notes IAM; 600.075; 600.096; 600.145 Approved no
Call Number Admin @ si @ GOS2018 Serial 3043
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Author Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera
Title Error-Correcting Factorization Type Journal Article
Year 2018 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 40 Issue Pages 2388-2401
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Abstract (up) Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches.
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ISSN 0162-8828 ISBN Medium
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Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ BPT2018 Serial 3015
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Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes Type Journal Article
Year 2009 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 30 Issue 3 Pages 285–297
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Abstract (up) Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied.
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Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2009a Serial 1153
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Author Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell
Title Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects Type Journal Article
Year 2020 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A
Volume 37 Issue 1 Pages 1-15
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Abstract (up) Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results.
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Notes CIC; 600.140; 600.12; 600.118 Approved no
Call Number Admin @ si @ SBV2019 Serial 3311
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Author Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez
Title A reduced feature set for driver head pose estimation Type Journal Article
Year 2016 Publication Applied Soft Computing Abbreviated Journal ASOC
Volume 45 Issue Pages 98-107
Keywords Head pose estimation; driving performance evaluation; subspace based methods; linear regression
Abstract (up) Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application.
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Notes ADAS; 600.085; 600.076; Approved no
Call Number Admin @ si @ DHL2016 Serial 2760
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Author Arjan Gijsenij; Theo Gevers
Title Color Constancy Using Natural Image Statistics and Scene Semantics Type Journal Article
Year 2011 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 33 Issue 4 Pages 687-698
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Abstract (up) Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms.
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ISSN 0162-8828 ISBN Medium
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Notes ISE Approved no
Call Number Admin @ si @ GiG2011 Serial 1724
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Author Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat
Title Monitoring war destruction from space using machine learning Type Journal Article
Year 2021 Publication Proceedings of the National Academy of Sciences of the United States of America Abbreviated Journal PNAS
Volume 118 Issue 23 Pages e2025400118
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Abstract (up) Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.
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Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ MGH2021 Serial 3584
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Author Ruben Tito; Dimosthenis Karatzas; Ernest Valveny
Title Hierarchical multimodal transformers for Multipage DocVQA Type Journal Article
Year 2023 Publication Pattern Recognition Abbreviated Journal PR
Volume 144 Issue 109834 Pages
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Abstract (up) Existing work on DocVQA only considers single-page documents. However, in real applications documents are mostly composed of multiple pages that should be processed altogether. In this work, we propose a new multimodal hierarchical method Hi-VT5, that overcomes the limitations of current methods to process long multipage documents. In contrast to previous hierarchical methods that focus on different semantic granularity (He et al., 2021) or different subtasks (Zhou et al., 2022) used in image classification. Our method is a hierarchical transformer architecture where the encoder learns to summarize the most relevant information of every page and then, the decoder uses this summarized representation to generate the final answer, following a bottom-up approach. Moreover, due to the lack of multipage DocVQA datasets, we also introduce MP-DocVQA, an extension of SP-DocVQA where questions are posed over multipage documents instead of single pages. Through extensive experimentation, we demonstrate that Hi-VT5 is able, in a single stage, to answer the questions and provide the page that contains the answer, which can be used as a kind of explainability measure.
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Notes DAG Approved no
Call Number Admin @ si @ TKV2023 Serial 3836
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Author Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier
Title Modeling, Recognizing, and Explaining Apparent Personality from Videos Type Journal Article
Year 2022 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC
Volume 13 Issue 2 Pages 894-911
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Abstract (up) Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future.
Address 1 April-June 2022
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Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ EKS2022 Serial 3406
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Author Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez
Title Enhancing spatio-chromatic representation with more-than-three color coding for image description Type Journal Article
Year 2017 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A
Volume 34 Issue 5 Pages 827-837
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Abstract (up) Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding.
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Notes CIC; 600.087 Approved no
Call Number Admin @ si @ RVB2017 Serial 2892
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Author Pau Rodriguez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez
Title Age and gender recognition in the wild with deep attention Type Journal Article
Year 2017 Publication Pattern Recognition Abbreviated Journal PR
Volume 72 Issue Pages 563-571
Keywords Age recognition; Gender recognition; Deep neural networks; Attention mechanisms
Abstract (up) Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy.
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Notes ISE; 600.098; 602.133; 600.119 Approved no
Call Number Admin @ si @ RCG2017b Serial 2962
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Author Hao Fang; Ajian Liu; Jun Wan; Sergio Escalera; Chenxu Zhao; Xu Zhang; Stan Z Li; Zhen Lei
Title Surveillance Face Anti-spoofing Type Journal Article
Year 2024 Publication IEEE Transactions on Information Forensics and Security Abbreviated Journal TIFS
Volume 19 Issue Pages 1535-1546
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Abstract (up) Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.
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Notes HUPBA Approved no
Call Number Admin @ si @ FLW2024 Serial 3869
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