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Author  |
Pierluigi Casale |
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Title |
Approximate Ensemble Methods for Physical Activity Recognition Applications |
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Year |
2011 |
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PhD Thesis, Universitat de Barcelona-CVC |
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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 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Oriol Pujol;Petia Radeva |
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MILAB |
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Admin @ si @ Cas2011 |
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1837 |
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Author |
Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno |
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Title |
Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era" |
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Conference Article |
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2017 |
Publication |
Workshop on Big Data Analytics and Machine Learning for Data Communication Networks |
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43-48 |
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Keywords |
mobile networks; call detail records; human mobility |
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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. |
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UCLA; USA; August 2017 |
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978-1-4503-5054-9 |
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ACMW (SIGCOMM) |
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HuPBA; no menciona |
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no |
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Admin @ si @ FPT2017 |
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2980 |
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Author |
Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera |
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Title |
RGB-D-based Human Motion Recognition with Deep Learning: A Survey |
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Journal Article |
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2018 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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171 |
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118-139 |
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Keywords |
Human motion recognition; RGB-D data; Deep learning; Survey |
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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. |
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HUPBA; no proj |
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no |
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Admin @ si @ WLO2018 |
Serial |
3123 |
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Author |
Philippe Dosch; Josep Llados |
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Title |
Vectorial Signatures for Symbol Discrimination |
Type |
Miscellaneous |
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2003 |
Publication |
Proceedings of Fifth IAPR International Workshop on Graphics Recognition, 159–169 |
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Barcelona |
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DAG |
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DAG @ dag @ DoL2003 |
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373 |
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Author |
Philippe Dosch; Josep Llados |
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Title |
Vectorial Signatures for Symbol Discrimination |
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Miscellaneous |
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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 |
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Springer-Verlag |
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DAG @ dag @ DoL2004 |
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461 |
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Author |
Philippe Dosch; Ernest Valveny |
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Title |
Report on the Second Symbol Recognition Contest |
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Book Chapter |
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2006 |
Publication |
Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 381–397 |
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DAG @ dag @ DoV2006 |
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691 |
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Author |
Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva |
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Title |
3D Vessel Reconstruction from Biplane Angiograms using Snakes. |
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Miscellaneous |
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1998 |
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Cleveland, OH |
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MILAB;ADAS |
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no |
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BCNPCL @ bcnpcl @ RTV1998a |
Serial |
198 |
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Author |
Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva |
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Title |
3D Dynamic Model of the Coronary Tree. |
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1998 |
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Cleveland, OH |
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MILAB;ADAS |
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BCNPCL @ bcnpcl @ RTV1998b |
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200 |
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Author |
Petia Radeva; Michal Drozdzal; Santiago Segui; Laura Igual; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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Title |
Active labeling: Application to wireless endoscopy analysis |
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Conference Article |
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2012 |
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High Performance Computing and Simulation, International Conference on |
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174-181 |
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Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”. |
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978-1-4673-2359-8 |
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HPCS |
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MILAB; OR;MV |
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Admin @ si @ RDS2012 |
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2152 |
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Author |
Petia Radeva; Maya Dimitrova; Ch. Roumenin; David Rotger; D. Nikolov; Juan J. Villanueva |
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Title |
Integration of Multiple Sensor Modalities in ActiveVessel Cardiology Workstation |
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2004 |
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Varna (Bulgaria) |
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BCNPCL @ bcnpcl @ RDR2004 |
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473 |
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Author |
Petia Radeva; M. Scoccianti |
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Title |
3D Reconstruction of Abdominal Aortic Aneurysm |
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Miscellaneous |
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2000 |
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Elsevier Science B.V., Ed. H.U. Lemke, M.W. Vannier, K. Inamura, A.G.Farman and K.Doi, CARS 2000, pp.1014, ISBN:0–444–50536–9 |
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San Francisco, USA |
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BCNPCL @ bcnpcl @ RaS2000 |
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439 |
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Author |
Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria |
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Bayesian Classification for Inspection of Industrial Products. |
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2002 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RBT2002a |
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285 |
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Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria |
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Real-Time Inspection of cork stoppers using parametric methods in high dimensional spaces. |
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2002 |
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Proceedings of the Fourth IASTED International Conference, Signal and Image Processing: 480–484. |
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BCNPCL @ bcnpcl @ RBT2002b |
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296 |
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Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria |
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Bayesian Classification for Inspection of Industrial Products. |
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2002 |
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BCNPCL @ bcnpcl @ RBT2002c |
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316 |
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Petia Radeva; Judit Martinez; A. Tovar; X. Binefa; Jordi Vitria; Juan J. Villanueva |
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CORKIDENT: an automatic vision system for real-time inspection of natural products. |
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1999 |
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|
| |
ISSN |
|
ISBN |
|
Medium |
|
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| |
Area |
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Expedition |
|
Conference |
|
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| |
Notes |
OR;MILAB;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ RMT1999 |
Serial |
23 |
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| Permanent link to this record |
