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David Masip; Jordi Vitria |
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On the Nearest Neighbor Approach for Gender Recognition |
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Miscellaneous |
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2003 |
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In I. Aguilo, Ll. Valverde M.T. Escrig, editors. Artificial Intelligence Research and Development. IOS PRESS pp.178–188 |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV2003b |
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387 |
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Jordi Vitria; Petia Radeva; I. Aguilo |
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Recent Advances in Artificial Intelligence Research and Development |
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Book Chapter |
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2004 |
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Frontiers in Artificial Intelligence and Applications, 113, J. Vitria, P. Radeva, I. Aguilo (Eds.), ISBN: 1–58603–466–9 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ VRA2004 |
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509 |
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Agata Lapedriza; Jordi Vitria |
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Title |
Experimental Study of the Usefulness of External Face Features for Face Classification |
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Book Chapter |
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2005 |
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Artificial Intelligence Research and Development, IOS Press, 99–106 |
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OR;MV |
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BCNPCL @ bcnpcl @ LaV2005 |
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610 |
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Bogdan Raducanu; Jordi Vitria |
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Title |
Real-Time Face Tracking for Context-Aware Computing |
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2005 |
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Artificial Intelligence Research and Development, IOS Press, 91–98 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2005b |
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616 |
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Author |
Francisco Javier Orozco; Jordi Gonzalez |
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Title |
Confidence Assessment on Eyelid and Eyebrow Expression Recognition |
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Conference Article |
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2008 |
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2008 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2008) |
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Amsterdam (Holanda) |
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ISE |
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ISE @ ise @ OrG2008 |
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1111 |
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Author |
Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
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Title |
Lost in Transcription of Graphic Signs in Ciphers |
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Conference Article |
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2022 |
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International Conference on Historical Cryptology (HistoCrypt 2022) |
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153-158 |
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transcription of ciphers; hand-written text recognition of symbols; graphic signs |
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Abstract |
Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. |
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Amsterdam, Netherlands, June 20-22, 2022 |
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HystoCrypt |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ MBS2022 |
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3731 |
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Author |
Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester |
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Title |
Anatomical parameterization for volumetric meshing of the liver |
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Conference Article |
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Year |
2014 |
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SPIE – Medical Imaging |
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9036 |
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Coordinate System; Anatomy Modeling; Parameterization |
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Abstract |
A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values
at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites
of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the
volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient’s liver, and allows comparing livers from several patients in a common framework of reference. |
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Amsterdam; September 2014 |
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SPIE-MI |
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IAM; 600.075 |
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Admin @ si @ VGG2014 |
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2456 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Fast Structural Matching for Document Image Retrieval through Spatial Databases |
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Conference Article |
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2014 |
Publication |
Document Recognition and Retrieval XXI |
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9021 |
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Document image retrieval; distance transform; MSER; spatial database |
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The structure of document images plays a signi cant role in document analysis thus considerable e orts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signi cant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors. |
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Amsterdam; September 2014 |
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SPIE-DRR |
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DAG; 600.056; 600.061; 600.077 |
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Admin @ si @ GRK2014a |
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2496 |
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Author |
Joel Barajas; Jaume Garcia; Francesc Carreras; Sandra Pujades; Petia Radeva |
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Angle Images Using Gabor Filters in Cardiac Tagged MRI |
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Conference Article |
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2005 |
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Proceeding of the 2005 conference on Artificial Intelligence Research and Development |
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107-114 |
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Angle Images, Gabor Filters, Harp, Tagged Mri |
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Tagged Magnetic Resonance Imaging (MRI) is a non-invasive technique used to examine cardiac deformation in vivo. An Angle Image is a representation of a Tagged MRI which recovers the relative position of the tissue respect to the distorted tags. Thus cardiac deformation can be estimated. This paper describes a new approach to generate Angle Images using a bank of Gabor filters in short axis cardiac Tagged MRI. Our method improves the Angle Images obtained by global techniques, like HARP, with a local frequency analysis. We propose to use the phase response of a combination of a Gabor filters bank, and use it to find a more precise deformation of the left ventricle. We demonstrate the accuracy of our method over HARP by several experimental results. |
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Amsterdam; The Netherlands |
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IOS Press |
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Amsterdam, The Netherlands |
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1-58603-560-6 |
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CAIRD |
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IAM;MILAB |
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BCNPCL @ bcnpcl @ BGC2005; IAM @ iam |
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595 |
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Author |
Bhaskar Chakraborty; Ognjen Rudovic; Jordi Gonzalez |
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View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component-Wise HMM of Body Parts |
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Conference Article |
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2008 |
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8th IEEE International Conference on Automatic Face and Gesture Recognition |
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Amsterdam; The Netherlands |
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FG |
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ISE @ ise @ CRG2008 |
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1113 |
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Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Dynamic Lexicon Generation for Natural Scene Images |
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Conference Article |
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2016 |
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14th European Conference on Computer Vision Workshops |
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395-410 |
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scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN |
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Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge bene t from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed.
In this paper we propose a method that generates contextualized lexicons
for scene images using only visual information. For this, we exploit
the correlation between visual and textual information in a dataset consisting
of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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DAG; 600.084 |
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Admin @ si @ PGR2016 |
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2825 |
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Victor Ponce; Baiyu Chen; Marc Oliu; Ciprian Corneanu; Albert Clapes; Isabelle Guyon; Xavier Baro; Hugo Jair Escalante; Sergio Escalera |
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Title |
ChaLearn LAP 2016: First Round Challenge on First Impressions – Dataset and Results |
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2016 |
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14th European Conference on Computer Vision Workshops |
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Behavior Analysis; Personality Traits; First Impressions |
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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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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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HuPBA;MV; 600.063 |
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Admin @ si @ PCP2016 |
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2828 |
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Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez |
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Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition |
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2016 |
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14th European Conference on Computer Vision |
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697-716 |
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Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos. |
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Amsterdam; The Netherlands; October 2016 |
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ECCV |
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ADAS; 600.076; 600.085 |
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Admin @ si @ SGV2016 |
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2824 |
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Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu |
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Title |
Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits |
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Conference Article |
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2016 |
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14th European Conference on Computer Vision Workshops |
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Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels |
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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. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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HuPBA;MILAB; |
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no |
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Call Number |
Admin @ si @ CEG2016 |
Serial |
2829 |
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Author |
Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
SASE: RGB-Depth Database for Human Head Pose Estimation |
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Conference Article |
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Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
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Address |
Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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Notes |
HuPBA;MILAB; |
Approved |
no |
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Call Number |
Admin @ si @ LEA2016a |
Serial |
2840 |
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| Permanent link to this record |
