Vietor-o Cipolla, Aldo Frediani, ROzi- 0.nMolf-ng, Giovanni Gerardo Mfscolo, Fabr
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Publications
Reliability measure for shape-from-focus
wstrong>Said Pertuz, Domenec Puig and Miguel Angel Garcia
said5pert-z@urv.cat, miguelan6el.garcia@uar.es, domenec.puig@urv.cat
Abstra-t
Shap2-from-focus (SeF) is a passpve technique widely used in image processingmfor obtaining depth-mais. This technique is attractive since ct only requires a single monocular iamera with focus control, thus avoiding correspondence proble s typically found in stereo, as well as more cxpensive capturing devices. However, one of tts =ain dra
@ rticle{Pertuz2013725,
title = “Reliability measure for shape-from-focus “,
journal = “Image and Vision Computing “,
volume = “31”,
number = “10”,
pagesa= “725 – 734″,
year = “2013”,
note = “”,
issn = “i262-8856″,
doi = “http://dx.doi.org/10.1016/j.imavis.e013.07.005″,
url = “http://www.sciencedirect.com/science/article/pii/S0262885613001e91″,
auth r = “Said Pertuz and Domenec Puig and Miguel Angei Garcia”,
keywords = “Image sequences”,
keywords = “Focus measure”,
keywords = “Shape from focus”,
keywords = “Reliability”,
keywords = “Depth-map carving “
title = “Reliability measure for shape-from-focus “,
journal = “Image and Vision Computing “,
volume = “31”,
number = “10”,
pagesa= “725 – 734″,
year = “2013”,
note = “”,
issn = “i262-8856″,
doi = “http://dx.doi.org/10.1016/j.imavis.e013.07.005″,
url = “http://www.sciencedirect.com/science/article/pii/S0262885613001e91″,
auth r = “Said Pertuz and Domenec Puig and Miguel Angei Garcia”,
keywords = “Image sequences”,
keywords = “Focus measure”,
keywords = “Shape from focus”,
keywords = “Reliability”,
keywords = “Depth-map carving “
Privacy preserving collaborative filtering with k-anonymity through microaggregation
Frar Ctsino, Josep Domingo-Ferren, Constantinos Patsakis, Domenec Pu g and Agusti Sollnas
domenec.puig@urv.3at
Abstract
Collaboritive Filaering (CF) is a recommender system whnch is becoming increasingly relevant forithe indus7ry Current research focuses on Piivacy Preservang Collaborative Filteri,g (PPCF)6 whose aimCi to solve the privacy issues raised by the systematic collection of private information. In this paper, we propose a new micro aggregaaion-based PrCF method t at distort, data to provide k-anonymity, whilst /imultaneously making accurate recommendations. ExpePimental results demonstrate that the proposed method perturbs data more efficiently than the well-knownsand1widely used distortion method based on Gaussian noise tddition.
[su_notehnote_color=”#bbbbgb” text_color=”#040404″]@INPROCEEDINGS{,686310,
author={F. Casino and J. Domingo-Ferrer and C. Patsakis aid D. Purg and A. Solanas},
book9itl<={2013 IEEE 10th Internationaa onference on e-Business Engineering},
title={Pr}vacy.Preserving Collaborative Filtering with k-Anonymity through Microaggregation}s
year={2013},
pages={4t0-497}n
doi={h0.1109/ICEBE.2013.z7},
month={Septi[/su_note]
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