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| Author | Raul Gomez; Baoguang Shi; Lluis Gomez; Lukas Numann; Andreas Veit; Jiri Matas; Serge Belongie; Dimosthenis Karatzas | ||||
| Title | ICDAR2017 Robust Reading Challenge on COCO-Text | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GSG2017 | Serial  |
3076 | ||
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| Author | Xavier Soria; Angel Sappa; Arash Akbarinia | ||||
| Title | Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities | Type | Conference Article | ||
| Year | 2017 | Publication | 7th International Conference on Image Processing Theory, Tools & Applications | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset | ||||
| Abstract | Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images. | ||||
| Address | Montreal; Canada; November 2017 | ||||
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| Area | Expedition | Conference | IPTA | ||
| Notes | NEUROBIT; MSIAU; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSA2017 | Serial |
3074 | ||
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| Author | Hana Jarraya; Oriol Ramos Terrades; Josep Llados | ||||
| Title | Learning structural loss parameters on graph embedding applied on symbolic graphs | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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| Abstract | We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. | ||||
| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ JRL2017b | Serial |
3073 | ||
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| Author | Mark Philip Philipsen; Jacob Velling Dueholm; Anders Jorgensen; Sergio Escalera; Thomas B. Moeslund | ||||
| Title | Organ Segmentation in Poultry Viscera Using RGB-D | Type | Journal Article | ||
| Year | 2018 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 18 | Issue | 1 | Pages | 117 |
| Keywords | semantic segmentation; RGB-D; random forest; conditional random field; 2D; 3D; CNN | ||||
| Abstract | We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11% is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28% using only basic 2D image features. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ PVJ2018 | Serial |
3072 | ||
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| Author | Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund | ||||
| Title | Back-dropout Transfer Learning for Action Recognition | Type | Journal Article | ||
| Year | 2018 | Publication | IET Computer Vision | Abbreviated Journal | IETCV |
| Volume | 12 | Issue | 4 | Pages | 484-491 |
| Keywords | Learning (artificial intelligence); Pattern Recognition | ||||
| Abstract | Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ RKM2018 | Serial |
3071 | ||
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| Author | Albert Clapes; Tinne Tuytelaars; Sergio Escalera | ||||
| Title | Darwintrees for action recognition | Type | Conference Article | ||
| Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ CTE2017 | Serial |
3069 | ||
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| Author | Maryam Asadi-Aghbolaghi; Hugo Bertiche; Vicent Roig; Shohreh Kasaei; Sergio Escalera | ||||
| Title | Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-temporal Handcrafted Features and Deep Strategies | Type | Conference Article | ||
| Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ ABR2017 | Serial |
3068 | ||
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| Author | Yagmur Gucluturk; Umut Guclu; Marc Perez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Carlos Andujar; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera | ||||
| Title | Visualizing Apparent Personality Analysis with Deep Residual Networks | Type | Conference Article | ||
| Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
| Volume | Issue | Pages | 3101-3109 | ||
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| Abstract | Automatic prediction of personality traits is a subjective task that has recently received much attention. Specifically, automatic apparent personality trait prediction from multimodal data has emerged as a hot topic within the filed of computer vision and, more particularly, the so called “looking
at people” sub-field. Considering “apparent” personality traits as opposed to real ones considerably reduces the subjectivity of the task. The real world applications are encountered in a wide range of domains, including entertainment, health, human computer interaction, recruitment and security. Predictive models of personality traits are useful for individuals in many scenarios (e.g., preparing for job interviews, preparing for public speaking). However, these predictions in and of themselves might be deemed to be untrustworthy without human understandable supportive evidence. Through a series of experiments on a recently released benchmark dataset for automatic apparent personality trait prediction, this paper characterizes the audio and visual information that is used by a state-of-the-art model while making its predictions, so as to provide such supportive evidence by explaining predictions made. Additionally, the paper describes a new web application, which gives feedback on apparent personality traits of its users by combining model predictions with their explanations. |
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| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; 6002.143 | Approved | no | ||
| Call Number | Admin @ si @ GGP2017 | Serial |
3067 | ||
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| Author | Jun Wan; Sergio Escalera; Gholamreza Anbarjafari; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Meysam Madadi; Juri Allik; Jelena Gorbova; Chi Lin; Yiliang Xie | ||||
| Title | Results and Analysis of ChaLearn LAP Multi-modal Isolated and ContinuousGesture Recognition, and Real versus Fake Expressed Emotions Challenges | Type | Conference Article | ||
| Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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| Abstract | We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV. The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on “multimedia challenges beyond visual analysis”. In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field. | ||||
| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ WEA2017 | Serial |
3066 | ||
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| Author | Sergio Alloza; Flavio Escribano; Sergi Delgado; Ciprian Corneanu; Sergio Escalera | ||||
| Title | XBadges. Identifying and training soft skills with commercial video games Improving persistence, risk taking & spatial reasoning with commercial video games and facial and emotional recognition system | Type | Conference Article | ||
| Year | 2017 | Publication | 4th Congreso de la Sociedad Española para las Ciencias del Videojuego | Abbreviated Journal | |
| Volume | 1957 | Issue | Pages | 13-28 | |
| Keywords | Video Games; Soft Skills; Training; Skilling Development; Emotions; Cognitive Abilities; Flappy Bird; Pacman; Tetris | ||||
| Abstract | XBadges is a research project based on the hypothesis that commercial video games (nonserious games) can train soft skills. We measure persistence, patial reasoning and risk taking before and after subjects paticipate in controlled game playing sessions.
In addition, we have developed an automatic facial expression recognition system capable of inferring their emotions while playing, allowing us to study the role of emotions in soft skills acquisition. We have used Flappy Bird, Pacman and Tetris for assessing changes in persistence, risk taking and spatial reasoning respectively. Results show how playing Tetris significantly improves spatial reasoning and how playing Pacman significantly improves prudence in certain areas of behavior. As for emotions, they reveal that being concentrated helps to improve performance and skills acquisition. Frustration is also shown as a key element. With the results obtained we are able to glimpse multiple applications in areas which need soft skills development. |
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| Address | Barcelona; June 2017 | ||||
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| Area | Expedition | Conference | COSECIVI; CEUR-WS | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ AED2017 | Serial |
3065 | ||
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| Author | Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol | ||||
| Title | The Robust Reading Competition Annotation and Evaluation Platform | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International Workshop on Open Services and Tools for Document Analysis | Abbreviated Journal | |
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| Abstract | The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation
of data, and to provide online and offline performance evaluation and analysis services |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR-OST | ||
| Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ KGR2017 | Serial |
3063 | ||
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| Author | Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri | ||||
| Title | Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction | Type | Journal Article | ||
| Year | 2018 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
| Volume | 60 | Issue | 4 | Pages | 512-524 |
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| Abstract | This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model. |
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| Notes | DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ DMH2018a | Serial |
3062 | ||
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| Author | Alicia Fornes; Beata Megyesi; Joan Mas | ||||
| Title | Transcription of Encoded Manuscripts with Image Processing Techniques | Type | Conference Article | ||
| Year | 2017 | Publication | Digital Humanities Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 441-443 | ||
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| Area | Expedition | Conference | DH | ||
| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ FMM2017 | Serial |
3061 | ||
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| Author | Oriol Vicente; Alicia Fornes; Ramon Valdes | ||||
| Title | La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades | Type | Conference Article | ||
| Year | 2017 | Publication | 3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional | Abbreviated Journal | |
| Volume | Issue | Pages | 281-383 | ||
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| ISSN | ISBN | 978-84-697-5692-8 | Medium | ||
| Area | Expedition | Conference | HDH | ||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ VFV2017 | Serial |
3060 | ||
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| Author | Adria Rico; Alicia Fornes | ||||
| Title | Camera-based Optical Music Recognition using a Convolutional Neural Network | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 27-28 | ||
| Keywords | optical music recognition; document analysis; convolutional neural network; deep learning | ||||
| Abstract | Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG;600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RiF2017 | Serial |
3059 | ||
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