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Author (up) Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva
Title 3D Vessel Reconstruction from Biplane Angiograms using Snakes. Type Miscellaneous
Year 1998 Publication Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Cleveland, OH
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 MILAB;ADAS Approved no
Call Number BCNPCL @ bcnpcl @ RTV1998a Serial 198
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Author (up) Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva
Title 3D Dynamic Model of the Coronary Tree. Type Miscellaneous
Year 1998 Publication Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Cleveland, OH
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 MILAB;ADAS Approved no
Call Number BCNPCL @ bcnpcl @ RTV1998b Serial 200
Permanent link to this record
 
 
Author (up) Philippe Dosch; Ernest Valveny
Title Report on the Second Symbol Recognition Contest Type Book Chapter
Year 2006 Publication Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 381–397 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
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 DAG Approved no
Call Number DAG @ dag @ DoV2006 Serial 691
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Author (up) Philippe Dosch; Josep Llados
Title Vectorial Signatures for Symbol Discrimination Type Miscellaneous
Year 2003 Publication Proceedings of Fifth IAPR International Workshop on Graphics Recognition, 159–169 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona
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 DAG Approved no
Call Number DAG @ dag @ DoL2003 Serial 373
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Author (up) Philippe Dosch; Josep Llados
Title Vectorial Signatures for Symbol Discrimination Type Miscellaneous
Year 2004 Publication Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:150–161, ISBN: 3–540–22478–5 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Springer-Verlag
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 DAG Approved no
Call Number DAG @ dag @ DoL2004 Serial 461
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Author (up) Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera
Title RGB-D-based Human Motion Recognition with Deep Learning: A Survey Type Journal Article
Year 2018 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 171 Issue Pages 118-139
Keywords Human motion recognition; RGB-D data; Deep learning; Survey
Abstract Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research.
Address
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 @ WLO2018 Serial 3123
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Author (up) Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno
Title Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era" Type Conference Article
Year 2017 Publication Workshop on Big Data Analytics and Machine Learning for Data Communication Networks Abbreviated Journal
Volume Issue Pages 43-48
Keywords mobile networks; call detail records; human mobility
Abstract The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes,
have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users.
By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of “
always connected” terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users’ locations.
Address UCLA; USA; August 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 978-1-4503-5054-9 Medium
Area Expedition Conference ACMW (SIGCOMM)
Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ FPT2017 Serial 2980
Permanent link to this record
 
 
Author (up) Pierluigi Casale
Title Social Environment Description from Data Collected with a Wearable Device Type Miscellaneous
Year 2008 Publication CVC Technical Report #124 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona, Spain
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 @ Cas2008 Serial 1151
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Author (up) Pierluigi Casale
Title Approximate Ensemble Methods for Physical Activity Recognition Applications Type Book Whole
Year 2011 Publication PhD Thesis, Universitat de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The main interest of this thesis focuses on computational methodologies able to
reduce the degree of complexity of learning algorithms and its application to physical
activity recognition.
Random Projections will be used to reduce the computational complexity in Multiple Classifier Systems. A new boosting algorithm and a new one-class classification
methodology have been developed. In both cases, random projections are used for
reducing the dimensionality of the problem and for generating diversity, exploiting in
this way the benefits that ensembles of classifiers provide in terms of performances
and stability. Moreover, the new one-class classification methodology, based on an ensemble strategy able to approximate a multidimensional convex-hull, has been proved
to over-perform state-of-the-art one-class classification methodologies.
The practical focus of the thesis is towards Physical Activity Recognition. A new
hardware platform for wearable computing application has been developed and used
for collecting data of activities of daily living allowing to study the optimal features
set able to successful classify activities.
Based on the classification methodologies developed and the study conducted on
physical activity classification, a machine learning architecture capable to provide a
continuous authentication mechanism for mobile-devices users has been worked out,
as last part of the thesis. The system, based on a personalized classifier, states on
the analysis of the characteristic gait patterns typical of each individual ensuring an
unobtrusive and continuous authentication mechanism
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Oriol Pujol;Petia Radeva
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number Admin @ si @ Cas2011 Serial 1837
Permanent link to this record
 
 
Author (up) Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Face-to-face social activity detection using data collected with a wearable device Type Conference Article
Year 2009 Publication 4th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 5524 Issue Pages 56–63
Keywords
Abstract In this work the feasibility of building a socially aware badge that learns from user activities is explored. A wearable multisensor device has been prototyped for collecting data about user movements and photos of the environment where the user acts. Using motion data, speaking and other activities have been classified. Images have been analysed in order to complement motion data and help for the detection of social behaviours. A face detector and an activity classifier are both used for detecting if users have a social activity in the time they worn the device. Good results encourage the improvement of the system at both hardware and software level
Address Póvoa de Varzim, Portugal
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 978-3-642-02171-8 Medium
Area Expedition Conference IbPRIA
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ CPR2009b Serial 1206
Permanent link to this record
 
 
Author (up) Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Embedding Random Projections in Regularized Gradient Boosting Machines Type Conference Article
Year 2010 Publication Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Abbreviated Journal
Volume Issue Pages 44–53
Keywords
Abstract
Address Barcelona (Spain)
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 SUEMA
Notes MILAB;HUPBA Approved no
Call Number BCNPCL @ bcnpcl @ CPR2010c Serial 1466
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Author (up) Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Classyfing Agitation in Sedated ICU Patients Type Conference Article
Year 2010 Publication Medical Image Computing in Catalunya: Graduate Student Workshop Abbreviated Journal
Volume Issue Pages 19–20
Keywords
Abstract Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances.
Address Girona
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 MICCAT
Notes MILAB;HUPBA Approved no
Call Number BCNPCL @ bcnpcl @ COR2010 Serial 1467
Permanent link to this record
 
 
Author (up) Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Personalization and User Verification in Wearable Systems using Biometric Walking Patterns Type Journal Article
Year 2012 Publication Personal and Ubiquitous Computing Abbreviated Journal PUC
Volume 16 Issue 5 Pages 563-580
Keywords
Abstract In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.
Address
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1617-4909 ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPR2012 Serial 1706
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Author (up) Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Human Activity Recognition from Accelerometer Data using a Wearable Device Type Conference Article
Year 2011 Publication 5th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 6669 Issue Pages 289-296
Keywords
Abstract Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computationally efficient features and the Random Forest classifier. We obtain very encouraging results with classification accuracy of human activities recognition of up to 94%.
Address Las Palmas de Gran Canaria. Spain
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21256-7 Medium
Area Expedition Conference IbPRIA
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPR2011a Serial 1735
Permanent link to this record
 
 
Author (up) Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Approximate Convex Hulls Family for One-Class Cassification Type Conference Article
Year 2011 Publication 10th International Workshop on Multiple Classifier Systems Abbreviated Journal
Volume 6713 Issue Pages 106-115
Keywords
Abstract In this work, a new method for one-class classification based on the Convex Hull geometric structure is proposed. The new method creates a family of convex hulls able to fit the geometrical shape of the training points. The increased computational cost due to the creation of the convex hull in multiple dimensions is circumvented using random projections. This provides an approximation of the original structure with multiple bi-dimensional views. In the projection planes, a mechanism for noisy points rejection has also been elaborated and evaluated. Results show that the approach performs considerably well with respect to the state the art in one-class classification.
Address Napoli, Italy
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Carlo Sansone; Josef Kittler; Fabio Roli
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21556-8 Medium
Area Expedition Conference MCS
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPR2011b Serial 1761
Permanent link to this record