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Author |
C. Alejandro Parraga; Robert Benavente; Maria Vanrell; Ramon Baldrich |
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Title |
Modelling Inter-Colour Regions of Colour Naming Space |
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Conference Article |
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2008 |
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4th European Conference on Colour in Graphics, Imaging and Vision Proceedings |
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218–222 |
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Terrassa (Spain) |
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CGIV08 |
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CAT;CIC |
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no |
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CAT @ cat @ PBV2008 |
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969 |
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Author |
Jose Ramirez Moreno; Juan R Revilla; Miguel Reyes; Sergio Escalera |
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Title |
Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal |
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Conference Article |
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2016 |
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4th Congreso WCPT-SAR |
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Buenos Aires; Argentina; June 2016 |
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WCPT-SAR |
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HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ RRR2016 |
Serial |
2853 |
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Author |
Sergio Alloza; Flavio Escribano; Sergi Delgado; Ciprian Corneanu; Sergio Escalera |
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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 |
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Year |
2017 |
Publication |
4th Congreso de la Sociedad Española para las Ciencias del Videojuego |
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Volume |
1957 |
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Pages |
13-28 |
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Keywords |
Video Games; Soft Skills; Training; Skilling Development; Emotions; Cognitive Abilities; Flappy Bird; Pacman; Tetris |
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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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Barcelona; June 2017 |
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COSECIVI; CEUR-WS |
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HUPBA; no menciona |
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no |
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Call Number |
Admin @ si @ AED2017 |
Serial |
3065 |
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Author |
Joost Van de Weijer; Fahad Shahbaz Khan |
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Title |
Fusing Color and Shape for Bag-of-Words Based Object Recognition |
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Conference Article |
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Year |
2013 |
Publication |
4th Computational Color Imaging Workshop |
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7786 |
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Pages |
25-34 |
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Keywords |
Object Recognition; color features; bag-of-words; image classification |
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Abstract |
In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research. |
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Chiba; Japan; March 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-36699-4 |
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CCIW |
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CIC; 600.048 |
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no |
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Admin @ si @ WeK2013 |
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2283 |
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Author |
Fernando Vilariño; Dan Norton; Onur Ferhat |
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Title |
The Eye Doesn't Click – Eyetracking and Digital Content Interaction |
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Conference Article |
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Year |
2016 |
Publication |
4S/EASST Conference |
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Barcelona; Spain; September 2016 |
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EASST |
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MV; 600.097;SIAI |
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no |
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Admin @ si @VNF2016 |
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2801 |
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Author |
Saiping Zhang; Luis Herranz; Marta Mrak; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang |
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Title |
DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video |
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Conference Article |
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Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
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In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
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Virtual; May 2022 |
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ICASSP |
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MACO; 600.161; 601.379 |
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no |
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Call Number |
Admin @ si @ ZHM2022a |
Serial |
3765 |
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Author |
Guillem Martinez; Maya Aghaei; Martin Dijkstra; Bhalaji Nagarajan; Femke Jaarsma; Jaap van de Loosdrecht; Petia Radeva; Klaas Dijkstra |
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Title |
Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation |
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Conference Article |
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Year |
2022 |
Publication |
47th International Conference on Acoustics, Speech, and Signal Processing |
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Keywords |
Hyper-spectral imaging; plastic sorting; multi-label segmentation; bitfield encoding |
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Abstract |
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
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Singapore; May 2022 |
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ICASSP |
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MILAB; no proj |
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no |
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Call Number |
Admin @ si @ MAD2022 |
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3767 |
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Author |
Bartlomiej Twardowski; Pawel Zawistowski; Szymon Zaborowski |
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Title |
Metric Learning for Session-Based Recommendations |
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Conference Article |
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Year |
2021 |
Publication |
43rd edition of the annual BCS-IRSG European Conference on Information Retrieval |
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12656 |
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650-665 |
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Session-based recommendations; Deep metric learning; Learning to rank |
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Session-based recommenders, used for making predictions out of users’ uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study. |
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Virtual; March 2021 |
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LNCS |
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ECIR |
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LAMP; 600.120 |
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no |
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Call Number |
Admin @ si @ TZZ2021 |
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3586 |
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Author |
C. Alejandro Parraga; Xavier Otazu; Arash Akbarinia |
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Title |
Modelling symmetry perception with banks of quadrature convolutional Gabor kernels |
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Conference Article |
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Year |
2019 |
Publication |
42nd edition of the European Conference on Visual Perception |
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224-224 |
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Mirror symmetry is a property most likely to be encountered in animals than in medium scale vegetation or inanimate objects in the natural world. This might be the reason why the human visual system has evolved to detect it quickly and robustly. Indeed, the perception of symmetry assists higher-level visual processing that are crucial for survival such as target recognition and identification irrespective of position and location. Although the task of detecting symmetrical objects seems effortless to us, it is very challenging for computers (to the extent that it has been proposed as a robust “captcha” by Funk & Liu in 2016). Indeed, the exact mechanism of symmetry detection in primates is not well understood: fMRI studies have shown that symmetrical shapes activate specific higher-level areas of the visual cortex (Sasaki et al.; 2005) and similarly, a large body of psychophysical experiments suggest that the symmetry perception is critically influenced by low-level mechanisms (Treder; 2010). In this work we attempt to find plausible low-level mechanisms that might form the basis for symmetry perception. Our simple model is made from banks of (i) odd-symmetric Gabors (resembling edge-detecting V1 neurons); and (ii) banks of larger odd- and even-symmetric Gabors (resembling higher visual cortex neurons), that pool signals from the 'edge image'. As reported previously (Akbarinia et al, ECVP2017), the convolution of the symmetrical lines with the two Gabor kernels of alternative phase produces a minimum in one and a maximum in the other (Osorio; 1996), and the rectification and combination of these signals create lines which hint of mirror symmetry in natural images. We improved the algorithm by combining these signals across several spatial scales. Our preliminary results suggest that such multiscale combination of convolutional operations might form the basis for much of the operation of the HVS in terms of symmetry detection and representation. |
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Leuven; Belgium; August 2019 |
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ECVP |
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NEUROBIT; 600.128 |
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no |
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Admin @ si @ POA2019 |
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3371 |
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Author |
Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil |
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Title |
Selection of Radiomics Features based on their Reproducibility |
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Conference Article |
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Year |
2019 |
Publication |
41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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403-408 |
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Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network. |
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Berlin; Alemanya; July 2019 |
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EMBC |
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IAM; 600.139; 600.145 |
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no |
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Admin @ si @ LTS2019 |
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3358 |
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Author |
David Roche; Debora Gil; Jesus Giraldo |
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Title |
Assessing agonist efficacy in an uncertain Em world |
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Conference Article |
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2012 |
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40th Keystone Symposia on mollecular and celular biology |
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79 |
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The operational model of agonism has been widely used for the analysis of agonist action since its formulation in 1983. The model includes the Em parameter, which is defined as the maximum response of the system. The methods for Em estimation provide Em values not significantly higher than the maximum responses achieved by full agonists. However, it has been found that that some classes of compounds as, for instance, superagonists and positive allosteric modulators can increase the full agonist maximum response, implying upper limits for Em and thereby posing doubts on the validity of Em estimates. Because of the correlation between Em and operational efficacy, τ, wrong Em estimates will yield wrong τ estimates.
In this presentation, the operational model of agonism and various methods for the simulation of allosteric modulation will be analyzed. Alternatives for curve fitting will be presented and discussed. |
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Fairmont Banff Springs, Banff, Alberta, Canada |
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Keystone Symposia |
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Keystone Symposia |
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A. Christopoulus and M. Bouvier |
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english |
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english |
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Keystone Symposia |
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KSMCB |
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IAM |
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IAM @ iam @ RGG2012 |
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1855 |
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Author |
Lei Kang; Juan Ignacio Toledo; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol |
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Title |
Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition |
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Conference Article |
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2018 |
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40th German Conference on Pattern Recognition |
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459-472 |
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This paper proposes Convolve, Attend and Spell, an attention based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR. |
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Stuttgart; Germany; October 2018 |
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GCPR |
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DAG; 600.097; 603.057; 302.065; 601.302; 600.084; 600.121; 600.129 |
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no |
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Admin @ si @ KTR2018 |
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3167 |
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Author |
Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu |
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Title |
Can biological solutions help computers detect symmetry? |
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Conference Article |
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2017 |
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40th European Conference on Visual Perception |
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Berlin; Germany; August 2017 |
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ECVP |
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Notes |
NEUROBIT |
Approved |
no |
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Call Number |
Admin @ si @ APE2017 |
Serial |
2995 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
Motion Segmentation from Feature Trajectories with Missing Data |
Type |
Conference Article |
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Year |
2007 |
Publication |
3rd. Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
IbPRIA 2007 |
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Volume |
LNCS 4477 |
Issue |
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Pages |
483–490 |
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Address |
Girona (Spain) |
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Publisher |
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Place of Publication |
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Editor |
J. Marti et al. (Eds.) |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ JSL2007a |
Serial |
814 |
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Author |
Josep Llados |
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Title |
The 5G of Document Intelligence |
Type |
Conference Article |
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Year |
2021 |
Publication |
3rd Workshop on Future of Document Analysis and Recognition |
Abbreviated Journal |
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Issue |
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Pages |
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Address |
Lausanne; Suissa; September 2021 |
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Expedition |
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Conference |
FDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ |
Serial |
3677 |
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