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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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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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BCNPCL @ bcnpcl @ RTV1998a |
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198 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
3D Dynamic Model of the Coronary Tree. |
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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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BCNPCL @ bcnpcl @ RTV1998b |
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200 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Philippe Dosch; Ernest Valveny |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Report on the Second Symbol Recognition Contest |
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Book Chapter |
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2006 |
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Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 381–397 |
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DAG |
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DAG @ dag @ DoV2006 |
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691 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Philippe Dosch; Josep Llados |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Vectorial Signatures for Symbol Discrimination |
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Miscellaneous |
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2003 |
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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 ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Philippe Dosch; Josep Llados |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Vectorial Signatures for Symbol Discrimination |
Type |
Miscellaneous |
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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 |
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Springer-Verlag |
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DAG |
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DAG @ dag @ DoL2004 |
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461 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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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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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 ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno |
![goto web page (via DOI) doi](img/doi.gif)
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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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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 |
Approved |
no |
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Admin @ si @ FPT2017 |
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2980 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pierluigi Casale |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Social Environment Description from Data Collected with a Wearable Device |
Type |
Miscellaneous |
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Year |
2008 |
Publication |
CVC Technical Report #124 |
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Barcelona, Spain |
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no |
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Admin @ si @ Cas2008 |
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1151 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pierluigi Casale |
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Title |
Approximate Ensemble Methods for Physical Activity Recognition Applications |
Type |
Book Whole |
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Year |
2011 |
Publication |
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 |
Approved |
no |
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Call Number |
Admin @ si @ Cas2011 |
Serial |
1837 |
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Permanent link to this record |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
![goto web page url](img/www.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Face-to-face social activity detection using data collected with a wearable device |
Type |
Conference Article |
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Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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56–63 |
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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 |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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Notes |
MILAB;HuPBA |
Approved |
no |
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BCNPCL @ bcnpcl @ CPR2009b |
Serial |
1206 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Embedding Random Projections in Regularized Gradient Boosting Machines |
Type |
Conference Article |
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2010 |
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Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
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44–53 |
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Barcelona (Spain) |
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SUEMA |
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MILAB;HUPBA |
Approved |
no |
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BCNPCL @ bcnpcl @ CPR2010c |
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1466 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Classyfing Agitation in Sedated ICU Patients |
Type |
Conference Article |
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2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
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19–20 |
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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. |
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Girona |
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MICCAT |
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MILAB;HUPBA |
Approved |
no |
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BCNPCL @ bcnpcl @ COR2010 |
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1467 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Personalization and User Verification in Wearable Systems using Biometric Walking Patterns |
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Journal Article |
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2012 |
Publication |
Personal and Ubiquitous Computing |
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PUC |
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16 |
Issue |
5 |
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563-580 |
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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. |
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Springer-Verlag |
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1617-4909 |
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MILAB;HuPBA |
Approved |
no |
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Admin @ si @ CPR2012 |
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1706 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Human Activity Recognition from Accelerometer Data using a Wearable Device |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
289-296 |
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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%. |
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Las Palmas de Gran Canaria. Spain |
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Springer Berlin Heidelberg |
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Editor |
Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
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IbPRIA |
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Notes |
MILAB;HuPBA |
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no |
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Call Number |
Admin @ si @ CPR2011a |
Serial |
1735 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Approximate Convex Hulls Family for One-Class Cassification |
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Conference Article |
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Year |
2011 |
Publication |
10th International Workshop on Multiple Classifier Systems |
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Volume |
6713 |
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Pages |
106-115 |
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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. |
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Napoli, Italy |
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Springer Berlin Heidelberg |
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Carlo Sansone; Josef Kittler; Fabio Roli |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21556-8 |
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Conference |
MCS |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ CPR2011b |
Serial |
1761 |
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