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| Author | Victor Ponce; Baiyu Chen; Marc Oliu; Ciprian Corneanu; Albert Clapes; Isabelle Guyon; Xavier Baro; Hugo Jair Escalante; Sergio Escalera | ||||
| Title | ChaLearn LAP 2016: First Round Challenge on First Impressions – Dataset and Results | Type | Conference Article | ||
| Year | 2016 | Publication  |
14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Behavior Analysis; Personality Traits; First Impressions | ||||
| Abstract | This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the rst round of the competition. The goal of the competition was to automatically evaluate ve \apparent“ personality traits (the so-called \Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by tting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source
platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the nal phase. Despite the diculty of the task, the teams made great advances in this round of the challenge. |
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| Address | Amsterdam; The Netherlands; October 2016 | ||||
| Corporate Author | Thesis | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCVW | ||
| Notes | HuPBA;MV; 600.063 | Approved | no | ||
| Call Number | Admin @ si @ PCP2016 | Serial | 2828 | ||
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| Author | Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu | ||||
| Title | Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits | Type | Conference Article | ||
| Year | 2016 | Publication |
14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels | ||||
| Abstract | We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge. | ||||
| Address | Amsterdam; The Netherlands; October 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCVW | ||
| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ CEG2016 | Serial | 2829 | ||
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| Author | Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | SASE: RGB-Depth Database for Human Head Pose Estimation | Type | Conference Article | ||
| Year | 2016 | Publication |
14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Slides | ||||
| Address | Amsterdam; The Netherlands; October 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCVW | ||
| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ LEA2016a | Serial | 2840 | ||
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| Author | Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier | ||||
| Title | LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode | Type | Conference Article | ||
| Year | 2016 | Publication |
14th European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 9915 | Issue | Pages | 894-900 | |
| Keywords | Simulation environment; Automated Driving; Driver-Vehicle interaction | ||||
| Abstract | Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. | ||||
| Address | Amsterdam; The Netherlands; October 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ECCVW | ||
| Notes | ADAS;IAM; 600.085; 600.076 | Approved | no | ||
| Call Number | MHE2016 | Serial | 2865 | ||
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| Author | Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero | ||||
| Title | e-Counterfeit: a mobile-server platform for document counterfeit detection | Type | Conference Article | ||
| Year | 2017 | Publication |
14th IAPR International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms. | ||||
| Address | Kyoto; Japan; November 2017 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.061; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRL2018 | Serial | 3084 | ||
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| Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
| Title | Optical Music Recognition by Recurrent Neural Networks | Type | Conference Article | ||
| Year | 2017 | Publication |
14th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 25-26 | ||
| Keywords | Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory | ||||
| Abstract | Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRC2017 | Serial | 3056 | ||
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| Author | Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes | ||||
| Title | A Transcription Is All You Need: Learning to Align through Attention | Type | Conference Article | ||
| Year | 2021 | Publication |
14th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | 12916 | Issue | Pages | 141–146 | |
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| Abstract | Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset. | ||||
| Address | Virtual; September 2021 | ||||
| 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 | GREC | ||
| Notes | DAG; 602.230; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ TSC2021 | Serial | 3619 | ||
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| Author | Julio C. S. Jacques Junior; Cagri Ozcinar; Marina Marjanovic; Xavier Baro; Gholamreza Anbarjafari; Sergio Escalera | ||||
| Title | On the effect of age perception biases for real age regression | Type | Conference Article | ||
| Year | 2019 | Publication |
14th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels benefit real age estimation, rather than direct real to real age regression, our main contribution is the integration, in an end-to-end architecture, of face attributes for apparent age prediction with an additional loss for real age regression. Experimental results on the APPA-REAL dataset indicate the proposed network successfully take advantage of the adopted attributes to improve both apparent and real age estimation. Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression. Finally, we present preliminary results and discussion of a proof of concept application using the proposed model to regress the apparent age of an individual based on the gender of an external observer. | ||||
| Address | Lille; France; May 2019 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | FG | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ JOM2019 | Serial | 3262 | ||
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| Author | Daniel Sanchez; Meysam Madadi; Marc Oliu; Sergio Escalera | ||||
| Title | Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation | Type | Conference Article | ||
| Year | 2019 | Publication |
14th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | While many individual tasks in the domain of human analysis have recently received an accuracy boost from deep learning approaches, multi-task learning has mostly been ignored due to a lack of data. New synthetic datasets are being released, filling this gap with synthetic generated data. In this work, we analyze four related human analysis tasks in still images in a multi-task scenario by leveraging such datasets. Specifically, we study the correlation of 2D/3D pose estimation, body part segmentation and full-body depth estimation. These tasks are learned via the well-known Stacked Hourglass module such that each of the task-specific streams shares information with the others. The main goal is to analyze how training together these four related tasks can benefit each individual task for a better generalization. Results on the newly released SURREAL dataset show that all four tasks benefit from the multi-task approach, but with different combinations of tasks: while combining all four tasks improves 2D pose estimation the most, 2D pose improves neither 3D pose nor full-body depth estimation. On the other hand 2D parts segmentation can benefit from 2D pose but not from 3D pose. In all cases, as expected, the maximum improvement is achieved on those human body parts that show more variability in terms of spatial distribution, appearance and shape, e.g. wrists and ankles. | ||||
| Address | Lille; France; May 2019 | ||||
| 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 | FG | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ SMO2019 | Serial | 3326 | ||
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| Author | H. Emrah Tasli; Cevahir Çigla; Theo Gevers; A. Aydin Alatan | ||||
| Title | Super pixel extraction via convexity induced boundary adaptation | Type | Conference Article | ||
| Year | 2013 | Publication |
14th IEEE International Conference on Multimedia and Expo | Abbreviated Journal | |
| Volume | Issue | Pages | 1-6 | ||
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| Abstract | This study presents an efficient super-pixel extraction algorithm with major contributions to the state-of-the-art in terms of accuracy and computational complexity. Segmentation accuracy is improved through convexity constrained geodesic distance utilization; while computational efficiency is achieved by replacing complete region processing with boundary adaptation idea. Starting from the uniformly distributed rectangular equal-sized super-pixels, region boundaries are adapted to intensity edges iteratively by assigning boundary pixels to the most similar neighboring super-pixels. At each iteration, super-pixel regions are updated and hence progressively converging to compact pixel groups. Experimental results with state-of-the-art comparisons, validate the performance of the proposed technique in terms of both accuracy and speed. | ||||
| Address | San Jose; USA; July 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1945-7871 | ISBN | Medium | ||
| Area | Expedition | Conference | ICME | ||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ TÇG2013 | Serial | 2367 | ||
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| Author | Xavier Soria; Angel Sappa | ||||
| Title | Improving Edge Detection in RGB Images by Adding NIR Channel | Type | Conference Article | ||
| Year | 2018 | Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images | ||||
| Abstract | The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum
images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum. |
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| 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; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SoS2018 | Serial | 3192 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| 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 | |
| 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. |
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| 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 | ||||
| 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 | |
| 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. |
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| 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 | ||
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| Author | Muhammad Anwer Rao; David Vazquez; Antonio Lopez | ||||
| Title | Color Contribution to Part-Based Person Detection in Different Types of Scenarios | Type | Conference Article | ||
| Year | 2011 | Publication |
14th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 6855 | Issue | II | Pages | 463-470 |
| Keywords | Pedestrian Detection; Color | ||||
| Abstract | Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution. | ||||
| Address | Seville, Spain | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer | Place of Publication | Berlin Heidelberg | Editor | P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch |
| Language | English | Summary Language | english | Original Title | Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-23677-8 | Medium | |
| Area | Expedition | Conference | CAIP | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ RVL2011b | Serial | 1665 | ||
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| Author | Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie | ||||
| Title | Inferring the Performance of Medical Imaging Algorithms | Type | Conference Article | ||
| Year | 2011 | Publication |
14th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 6854 | Issue | Pages | 520-528 | |
| Keywords | Validation, Statistical Inference, Medical Imaging Algorithms. | ||||
| Abstract | Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence. |
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| Address | Sevilla | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer-Verlag Berlin Heidelberg | Place of Publication | Berlin | Editor | Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | L | Abbreviated Series Title | LNCS | |
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CAIP | ||
| Notes | IAM; ADAS | Approved | no | ||
| Call Number | IAM @ iam @ HGR2011 | Serial | 1676 | ||
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