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Author R. Herault; Franck Davoine; Fadi Dornaika; Y. Grandvalet
Title Simultaneous and robust face and facial action tracking Type Miscellaneous
Year 2006 Publication 15eme Congres Francophone AFRIF–AFIA de Reconnaissance des Formes et Intelligence Artificielle (RFIA´06) Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (down) Tours (France)
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
Notes Approved no
Call Number Admin @ si @ HDD2006 Serial 735
Permanent link to this record
 
 
Author Zhengying Liu; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera; Adrien Pavao; Hugo Jair Escalante; Wei-Wei Tu; Zhen Xu; Sebastien Treguer
Title AutoCV Challenge Design and Baseline Results Type Conference Article
Year 2019 Publication La Conference sur l’Apprentissage Automatique Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We present the design and beta tests of a new machine learning challenge called AutoCV (for Automated Computer Vision), which is the first event in a series of challenges we are planning on the theme of Automated Deep Learning. We target applications for which Deep Learning methods have had great success in the past few years, with the aim of pushing the state of the art in fully automated methods to design the architecture of neural networks and train them without any human intervention. The tasks are restricted to multi-label image classification problems, from domains including medical, areal, people, object, and handwriting imaging. Thus the type of images will vary a lot in scales, textures, and structure. Raw data are provided (no features extracted), but all datasets are formatted in a uniform tensor manner (although images may have fixed or variable sizes within a dataset). The participants's code will be blind tested on a challenge platform in a controlled manner, with restrictions on training and test time and memory limitations. The challenge is part of the official selection of IJCNN 2019.
Address (down) Toulouse; Francia; July 2019
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LGJ2019 Serial 3323
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Author Andrew Nolan; Daniel Serrano; Aura Hernandez-Sabate; Daniel Ponsa; Antonio Lopez
Title Obstacle mapping module for quadrotors on outdoor Search and Rescue operations Type Conference Article
Year 2013 Publication International Micro Air Vehicle Conference and Flight Competition Abbreviated Journal
Volume Issue Pages
Keywords UAV
Abstract Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments.
Address (down) Toulouse; France; September 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 IMAV
Notes ADAS; 600.054; 600.057;IAM Approved no
Call Number Admin @ si @ NSH2013 Serial 2371
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Author Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier
Title Filtrage de descripteurs locaux pour l'amélioration de la détection de documents Type Conference Article
Year 2016 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal
Volume Issue Pages
Keywords Local descriptors; mobile capture; document matching; keypoint selection
Abstract In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework.In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements.
Address (down) Toulouse; France; March 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 CIFED
Notes DAG; 600.084; 600.077 Approved no
Call Number Admin @ si @ RCO2016 Serial 2755
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Author Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville
Title PixelVAE: A Latent Variable Model for Natural Images Type Conference Article
Year 2017 Publication 5th International Conference on Learning Representations Abbreviated Journal
Volume Issue Pages
Keywords Deep Learning; Unsupervised Learning
Abstract Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms.
Address (down) Toulon; France; April 2017
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 ICLR
Notes ADAS; 600.085; 600.076; 601.281; 600.118 Approved no
Call Number ADAS @ adas @ GKA2017 Serial 2815
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Author Pau Rodriguez; Jordi Gonzalez; Jordi Cucurull; Josep M. Gonfaus; Xavier Roca
Title Regularizing CNNs with Locally Constrained Decorrelations Type Conference Article
Year 2017 Publication 5th International Conference on Learning Representations Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (down) Toulon; France; April 2017
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 ICLR
Notes ISE; 602.143; 600.119; 600.098 Approved no
Call Number Admin @ si @ RGC2017 Serial 2927
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Author Sergio Vera; Debora Gil; Agnes Borras; F. Javier Sanchez; Frederic Perez; Marius G. Linguraru; Miguel Angel Gonzalez Ballester
Title Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs Type Book Chapter
Year 2012 Publication Workshop on Computational and Clinical Applications in Abdominal Imaging Abbreviated Journal
Volume 7029 Issue Pages 223–230
Keywords medial manifolds, abdomen.
Abstract Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D
objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial
manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our
method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs,
exploring the use of medial manifolds for the representation of multi-organ relations.
Address (down) Toronto; Canada;
Corporate Author Thesis
Publisher Springer Link Place of Publication Berlin Editor H. Yoshida et al
Language English Summary Language English Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-28556-1 Medium
Area Expedition Conference ABDI
Notes IAM;MV Approved no
Call Number IAM @ iam @ VGB2012 Serial 1834
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Efficient pairwise classification using Local Cross Off strategy Type Conference Article
Year 2012 Publication 25th Canadian Conference on Artificial Intelligence Abbreviated Journal
Volume 7310 Issue Pages 25-36
Keywords
Abstract The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
Address (down) Toronto, Ontario
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 0302-9743 ISBN 978-3-642-30352-4 Medium
Area Expedition Conference AI
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2012c Serial 2044
Permanent link to this record
 
 
Author Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
Title Integration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy Type Conference Article
Year 2011 Publication In MICCAI 2011 Workshop on Computational and Clinical Applications in Abdominal Imaging Abbreviated Journal
Volume 6668 Issue Pages 76-83
Keywords
Abstract This work presents a region descriptor based on the integration of the information that the depth of valleys image provides. The depth of valleys image is based on the presence of intensity valleys around polyps due to the image acquisition. Our proposed method consists of defining, for each point, a series of radial sectors around it and then accumulates the maxima of the depth of valleys image only if the orientation of the intensity valley coincides with the orientation of the sector above. We apply our descriptor to a prior segmentation of the images and we present promising results on polyp detection, outperforming other approaches that also integrate depth of valleys information.
Address (down) Toronto, Canada
Corporate Author Thesis
Publisher Springer Link Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area 800 Expedition Conference ABI
Notes MV;SIAI Approved no
Call Number IAM @ iam @ BSV2011d Serial 1698
Permanent link to this record
 
 
Author Francesco Ciompi; Oriol Pujol; Carlo Gatta; Xavier Carrillo; Josepa Mauri; Petia Radeva
Title A Holistic Approach for the Detection of Media-Adventitia Border in IVUS Type Conference Article
Year 2011 Publication 14th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal
Volume 6893 Issue Pages 401-408
Keywords
Abstract In this paper we present a methodology for the automatic detection of media-adventitia border (MAb) in Intravascular Ultrasound. A robust computation of the MAb is achieved through a holistic approach where the position of the MAb with respect to other tissues of the vessel is used. A learned quality measure assures that the resulting MAb is optimal with respect to all other tissues. The mean distance error computed through a set of 140 images is 0.2164 (±0.1326) mm.
Address (down) Toronto, Canada
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-23625-9 Medium
Area Expedition Conference MICCAI
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPG2011 Serial 1739
Permanent link to this record
 
 
Author Antonio Hernandez; Carlo Gatta; Sergio Escalera; Laura Igual; Victoria Martin Yuste; Petia Radeva
Title Accurate and Robust Fully-Automatic QCA: Method and Numerical Validation Type Conference Article
Year 2011 Publication 14th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal
Volume 14 Issue 3 Pages 496-503
Keywords
Abstract The Quantitative Coronary Angiography (QCA) is a methodology used to evaluate the arterial diseases and, in particular, the degree of stenosis. In this paper we propose AQCA, a fully automatic method for vessel segmentation based on graph cut theory. Vesselness, geodesic paths and a new multi-scale edgeness map are used to compute a globally optimal artery segmentation. We evaluate the method performance in a rigorous numerical way on two datasets. The method can detect an artery with precision 92.9 +/- 5% and sensitivity 94.2 +/- 6%. The average absolute distance error between detected and ground truth centerline is 1.13 +/- 0.11 pixels (about 0.27 +/- 0.025 mm) and the absolute relative error in the vessel caliber estimation is 2.93% with almost no bias. Moreover, the method can discriminate between arteries and catheter with an accuracy of 96.4%.
Address (down) Toronto, Canada
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-23625-9 Medium
Area Expedition Conference MICCAI
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ HGE2011 Serial 1769
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Author Francesco Ciompi; A. Palaioroutas; M. Loeve; Oriol Pujol; Petia Radeva; H. Tiddens; M. de Bruijne
Title Lung Tissue Classification in Severe Advanced Cystic Fibrosis from CT Scans Type Conference Article
Year 2011 Publication In MICCAI 2011 4th International Workshop on Pulmonary Image Analysis Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (down) Toronto, Canada
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 PIA
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPL2011 Serial 1798
Permanent link to this record
 
 
Author Marc Bolaños; R. Mestre; Estefania Talavera; Xavier Giro; Petia Radeva
Title Visual Summary of Egocentric Photostreams by Representative Keyframes Type Conference Article
Year 2015 Publication IEEE International Conference on Multimedia and Expo ICMEW2015 Abbreviated Journal
Volume Issue Pages 1-6
Keywords egocentric; lifelogging; summarization; keyframes
Abstract Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries.
Address (down) Torino; italy; July 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 978-1-4799-7079-7 Edition
ISSN ISBN 978-1-4799-7079-7 Medium
Area Expedition Conference ICME
Notes MILAB Approved no
Call Number Admin @ si @ BMT2015 Serial 2638
Permanent link to this record
 
 
Author G. de Oliveira; A. Cartas; Marc Bolaños; Mariella Dimiccoli; Xavier Giro; Petia Radeva
Title LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task Type Conference Article
Year 2016 Publication 12th NTCIR Conference on Evaluation of Information Access Technologies Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.
Address (down) Tokyo; Japan; June 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 NTCIR
Notes MILAB; Approved no
Call Number Admin @ si @OCB2016 Serial 2789
Permanent link to this record
 
 
Author Mohammad Rouhani; E. Boyer; Angel Sappa
Title Non-Rigid Registration meets Surface Reconstruction Type Conference Article
Year 2014 Publication International Conference on 3D Vision Abbreviated Journal
Volume Issue Pages 617-624
Keywords
Abstract Non rigid registration is an important task in computer vision with many applications in shape and motion modeling. A fundamental step of the registration is the data association between the source and the target sets. Such association proves difficult in practice, due to the discrete nature of the information and its corruption by various types of noise, e.g. outliers and missing data. In this paper we investigate the benefit of the implicit representations for the non-rigid registration of 3D point clouds. First, the target points are described with small quadratic patches that are blended through partition of unity weighting. Then, the discrete association between the source and the target can be replaced by a continuous distance field induced by the interface. By combining this distance field with a proper deformation term, the registration energy can be expressed in a linear least square form that is easy and fast to solve. This significantly eases the registration by avoiding direct association between points. Moreover, a hierarchical approach can be easily implemented by employing coarse-to-fine representations. Experimental results are provided for point clouds from multi-view data sets. The qualitative and quantitative comparisons show the outperformance and robustness of our framework. %in presence of noise and outliers.
Address (down) Tokyo; Japan; December 2014
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 3DV
Notes ADAS; 600.055; 600.076 Approved no
Call Number Admin @ si @ RBS2014 Serial 2534
Permanent link to this record