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Author Dani Rowe; Jordi Gonzalez; Ivan Huerta; Juan J. Villanueva edit  openurl
  Title On Reasoning over Tracking Events Type Conference Article
  Year 2007 Publication (up) 15th Scandinavian Conference on Image Analysis Abbreviated Journal  
  Volume 4522 Issue Pages 502–511  
  Keywords  
  Abstract  
  Address Aalborg (Denmark)  
  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 SCIA´07  
  Notes ISE Approved no  
  Call Number ISE @ ise @ RGH2007 Serial 784  
Permanent link to this record
 

 
Author Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati Ardakani; Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi edit   pdf
url  openurl
  Title Thermal Image Super-Resolution Challenge – PBVS 2020 Type Conference Article
  Year 2020 Publication (up) 16h IEEE Workshop on Perception Beyond the Visible Spectrum Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state-of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high-resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.  
  Address Virtual CVPR  
  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 CVPRW  
  Notes MSIAU; ISE; 600.119; 600.122 Approved no  
  Call Number Admin @ si @ RSV2020 Serial 3431  
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Author Albert Rial-Farras; Meysam Madadi; Sergio Escalera edit   pdf
url  doi
openurl 
  Title UV-based reconstruction of 3D garments from a single RGB image Type Conference Article
  Year 2021 Publication (up) 16th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal  
  Volume Issue Pages 1-8  
  Keywords  
  Abstract Garments are highly detailed and dynamic objects made up of particles that interact with each other and with other objects, making the task of 2D to 3D garment reconstruction extremely challenging. Therefore, having a lightweight 3D representation capable of modelling fine details is of great importance. This work presents a deep learning framework based on Generative Adversarial Networks (GANs) to reconstruct 3D garment models from a single RGB image. It has the peculiarity of using UV maps to represent 3D data, a lightweight representation capable of dealing with high-resolution details and wrinkles. With this model and kind of 3D representation, we achieve state-of-the-art results on the CLOTH3D++ dataset, generating good quality and realistic garment reconstructions regardless of the garment topology and shape, human pose, occlusions and lightning.  
  Address Virtual; December 2021  
  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 @ RME2021 Serial 3639  
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Author C. Alejandro Parraga; Arash Akbarinia edit   pdf
doi  openurl
  Title Colour Constancy as a Product of Dynamic Centre-Surround Adaptation Type Conference Article
  Year 2016 Publication (up) 16th Annual meeting in Vision Sciences Society Abbreviated Journal  
  Volume 16 Issue 12 Pages  
  Keywords  
  Abstract Colour constancy refers to the human visual system's ability to preserve the perceived colour of objects despite changes in the illumination. Its exact mechanisms are unknown, although a number of systems ranging from retinal to cortical and memory are thought to play important roles. The strength of the perceptual shift necessary to preserve these colours is usually estimated by the vectorial distances from an ideal match (or canonical illuminant). In this work we explore how much of the colour constancy phenomenon could be explained by well-known physiological properties of V1 and V2 neurons whose receptive fields (RF) vary according to the contrast and orientation of surround stimuli. Indeed, it has been shown that both RF size and the normalization occurring between centre and surround in cortical neurons depend on the local properties of surrounding stimuli. Our stating point is the construction of a computational model which includes this dynamical centre-surround adaptation by means of two overlapping asymmetric Gaussian kernels whose variances are adjusted to the contrast of surrounding pixels to represent the changes in RF size of cortical neurons and the weights of their respective contributions are altered according to differences in centre-surround contrast and orientation. The final output of the model is obtained after convolving an image with this dynamical operator and an estimation of the illuminant is obtained by considering the contrast of the far surround. We tested our algorithm on naturalistic stimuli from several benchmark datasets. Our results show that although our model does not require any training, its performance against the state-of-the-art is highly competitive, even outperforming learning-based algorithms in some cases. Indeed, these results are very encouraging if we consider that they were obtained with the same parameters for all datasets (i.e. just like the human visual system operates).  
  Address Florida; USA; May 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 VSS  
  Notes NEUROBIT Approved no  
  Call Number Admin @ si @ PaA2016b Serial 2901  
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Author Alvaro Cepero; Albert Clapes; Sergio Escalera edit   pdf
openurl 
  Title Quantitative analysis of non-verbal communication for competence analysis Type Conference Article
  Year 2013 Publication (up) 16th Catalan Conference on Artificial Intelligence Abbreviated Journal  
  Volume 256 Issue Pages 105-114  
  Keywords  
  Abstract  
  Address Vic; October 2013  
  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 CCIA  
  Notes HUPBA;MILAB Approved no  
  Call Number Admin @ si @ CCE2013 Serial 2324  
Permanent link to this record
 

 
Author Vitaliy Konovalov; Albert Clapes; Sergio Escalera edit   pdf
openurl 
  Title Automatic Hand Detection in RGB-Depth Data Sequences Type Conference Article
  Year 2013 Publication (up) 16th Catalan Conference on Artificial Intelligence Abbreviated Journal  
  Volume Issue Pages 91-100  
  Keywords  
  Abstract Detecting hands in multi-modal RGB-Depth visual data has become a challenging Computer Vision problem with several applications of interest. This task involves dealing with changes in illumination, viewpoint variations, the articulated nature of the human body, the high flexibility of the wrist articulation, and the deformability of the hand itself. In this work, we propose an accurate and efficient automatic hand detection scheme to be applied in Human-Computer Interaction (HCI) applications in which the user is seated at the desk and, thus, only the upper body is visible. Our main hypothesis is that hand landmarks remain at a nearly constant geodesic distance from an automatically located anatomical reference point.
In a given frame, the human body is segmented first in the depth image. Then, a
graph representation of the body is built in which the geodesic paths are computed from the reference point. The dense optical flow vectors on the corresponding RGB image are used to reduce ambiguities of the geodesic paths’ connectivity, allowing to eliminate false edges interconnecting different body parts. Finally, we are able to detect the position of both hands based on invariant geodesic distances and optical flow within the body region, without involving costly learning procedures.
 
  Address Vic; October 2013  
  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 CCIA  
  Notes HuPBA;MILAB Approved no  
  Call Number Admin @ si @ KCE2013 Serial 2323  
Permanent link to this record
 

 
Author Adriana Romero; Simeon Petkov; Carlo Gatta; M.Sabate; Petia Radeva edit   pdf
openurl 
  Title Efficient automatic segmentation of vessels Type Conference Article
  Year 2012 Publication (up) 16th Conference on Medical Image Understanding and Analysis Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Swansea, United Kingdom  
  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 MIUA  
  Notes MILAB Approved no  
  Call Number Admin @ si @ Serial 2137  
Permanent link to this record
 

 
Author Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas edit   pdf
openurl 
  Title Location Sensitive Image Retrieval and Tagging Type Conference Article
  Year 2020 Publication (up) 16th European Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging.  
  Address Virtual; August 2020  
  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 ECCV  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GGG2020b Serial 3420  
Permanent link to this record
 

 
Author Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas edit   pdf
openurl 
  Title GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images Type Conference Article
  Year 2020 Publication (up) 16th European Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images.  
  Address Virtual; August 2020  
  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 ECCV  
  Notes DAG; 600.140; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ KPW2020 Serial 3426  
Permanent link to this record
 

 
Author Hugo Bertiche; Meysam Madadi; Sergio Escalera edit   pdf
openurl 
  Title CLOTH3D: Clothed 3D Humans Type Conference Article
  Year 2020 Publication (up) 16th European Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape.  
  Address Virtual; August 2020  
  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 ECCV  
  Notes HUPBA Approved no  
  Call Number Admin @ si @ BME2020 Serial 3519  
Permanent link to this record
 

 
Author Hugo Bertiche; Meysam Madadi; Sergio Escalera edit   pdf
url  doi
openurl 
  Title Deep Parametric Surfaces for 3D Outfit Reconstruction from Single View Image Type Conference Article
  Year 2021 Publication (up) 16th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal  
  Volume Issue Pages 1-8  
  Keywords  
  Abstract We present a methodology to retrieve analytical surfaces parametrized as a neural network. Previous works on 3D reconstruction yield point clouds, voxelized objects or meshes. Instead, our approach yields 2-manifolds in the euclidean space through deep learning. To this end, we implement a novel formulation for fully connected layers as parametrized manifolds that allows continuous predictions with differential geometry. Based on this property we propose a novel smoothness loss. Results on CLOTH3D++ dataset show the possibility to infer different topologies and the benefits of the smoothness term based on differential geometry.  
  Address Virtual; December 2021  
  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 @ BME2021 Serial 3640  
Permanent link to this record
 

 
Author Adria Ruiz; Joost Van de Weijer; Xavier Binefa edit   pdf
url  doi
openurl 
  Title From emotions to action units with hidden and semi-hidden-task learning Type Conference Article
  Year 2015 Publication (up) 16th IEEE International Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 3703-3711  
  Keywords  
  Abstract Limited annotated training data is a challenging problem in Action Unit recognition. In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers. For this purpose, we propose a novel learning framework: Hidden-Task Learning. HTL aims to learn a set of Hidden-Tasks (Action Units)for which samples are not available but, in contrast, training data is easier to obtain from a set of related VisibleTasks (Facial Expressions). To that end, HTL is able to exploit prior knowledge about the relation between Hidden and Visible-Tasks. In our case, we base this prior knowledge on empirical psychological studies providing statistical correlations between Action Units and universal facial expressions. Additionally, we extend HTL to Semi-Hidden Task Learning (SHTL) assuming that Action Unit training samples are also provided. Performing exhaustive experiments over four different datasets, we show that HTL and SHTL improve the generalization ability of AU classifiers by training them with additional facial expression data. Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training.  
  Address Santiago de Chile; Chile; December 2015  
  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 ICCV  
  Notes LAMP; 600.068; 600.079 Approved no  
  Call Number Admin @ si @ RWB2015 Serial 2671  
Permanent link to this record
 

 
Author Sergio Escalera; Junior Fabian; Pablo Pardo; Xavier Baro; Jordi Gonzalez; Hugo Jair Escalante; Marc Oliu; Dusan Misevic; Ulrich Steiner; Isabelle Guyon edit   pdf
url  doi
openurl 
  Title ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results Type Conference Article
  Year 2015 Publication (up) 16th IEEE International Conference on Computer Vision Workshops Abbreviated Journal  
  Volume Issue Pages 243 - 251  
  Keywords  
  Abstract Following previous series on Looking at People (LAP) competitions [14, 13, 11, 12, 2], in 2015 ChaLearn ran two new competitions within the field of Looking at People: (1) age estimation, and (2) cultural event recognition, both in
still images. We developed a crowd-sourcing application to collect and label data about the apparent age of people (as opposed to the real age). In terms of cultural event recognition, one hundred categories had to be recognized. These
tasks involved scene understanding and human body analysis. This paper summarizes both challenges and data, as well as the results achieved by the participants of the competition.
 
  Address Santiago de Chile; December 2015  
  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 ICCVW  
  Notes ISE; 600.063; 600.078;MV Approved no  
  Call Number Admin @ si @ EFP2015 Serial 2704  
Permanent link to this record
 

 
Author Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas edit   pdf
url  doi
openurl 
  Title Evaluating Real-Time Mirroring of Head Gestures using Smart Glasses Type Conference Article
  Year 2015 Publication (up) 16th IEEE International Conference on Computer Vision Workshops Abbreviated Journal  
  Volume Issue Pages 452-460  
  Keywords  
  Abstract Mirroring occurs when one person tends to mimic the non-verbal communication of their counterparts. Even though mirroring is a complex phenomenon, in this study, we focus on the detection of head-nodding as a simple non-verbal communication cue due to its significance as a gesture displayed during social interactions. This paper introduces a computer vision-based method to detect mirroring through the analysis of head gestures using wearable cameras (smart glasses). In addition, we study how such a method can be used to explore perceived competence. The proposed method has been evaluated and the experiments demonstrate how static and wearable cameras seem to be equally effective to gather the information required for the analysis.  
  Address Santiago de Chile; December 2015  
  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 ICCVW  
  Notes OR; 600.068; 600.072;MV Approved no  
  Call Number Admin @ si @ TRM2015 Serial 2722  
Permanent link to this record
 

 
Author Sergio Escalera; Alicia Fornes; Oriol Pujol; Alberto Escudero; Petia Radeva edit  url
isbn  openurl
  Title Circular Blurred Shape Model for Symbol Spotting in Documents Type Conference Article
  Year 2009 Publication (up) 16th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages 1985-1988  
  Keywords  
  Abstract Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors.  
  Address Cairo, Egypt  
  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 978-1-4244-5653-6 Medium  
  Area Expedition Conference ICIP  
  Notes MILAB;HuPBA;DAG Approved no  
  Call Number BCNPCL @ bcnpcl @ EFP2009b Serial 1184  
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