Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Shannon M. Hughes"'
Publikováno v:
Signal Processing. 93:541-553
The new field of visual stylometry proposes to apply mathematical and statistical tools to high-resolution images of artworks to produce a quantitative description of each work's style, or of stylistic differences between works. Such quantitative evi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f73e41c42f443ee04125e048e4a4a02
https://doi.org/10.1016/j.sigpro.2012.09.025
https://doi.org/10.1016/j.sigpro.2012.09.025
Autor:
Ingrid Daubechies, Marco F. Duarte, Shannon M. Hughes, Andrei Brasoveanu, Anila Anitha, Matthias Alfeld, Koen Janssens, Joris Dik
Publikováno v:
Signal processing
This paper describes our methods for repairing and restoring images of hidden paintings (paintings that have been painted over and are now covered by a new surface painting) that have been obtained via noninvasive X-ray fluorescence imaging of their
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::462749501492a72f8212c250e393602f
https://doi.org/10.1016/j.sigpro.2012.09.027
https://doi.org/10.1016/j.sigpro.2012.09.027
Autor:
Eric O. Postma, James Z. Wang, Eugene Brevdo, Shannon M. Hughes, Igor Berezhnoy, Jia Li, Ella Hendriks, Chris R. Johnson, Ingrid Daubechies
Publikováno v:
Ieee Signal Processing Magazine, 25(4), 37-48. IEEE
A survey of the literature reveals that image processing tools aimed at supplementing the art historian's toolbox are currently in the earliest stages of development. To jump-start the development of such methods, the Van Gogh and Kroller-Muller muse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d51c33e4b560711ba99c568a5e8effd7
https://doi.org/10.1109/msp.2008.923513
https://doi.org/10.1109/msp.2008.923513
Performing signal processing tasks on compressive measurements of data has received great attention in recent years. In this paper, we extend previous work on compressive dictionary learning by showing that more general random projections may be used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbc51bf0f69f678d12d10f7ce7415b76
http://arxiv.org/abs/1504.01169
http://arxiv.org/abs/1504.01169
Publikováno v:
ICASSP
There has been growing interest in performing signal processing tasks directly on compressive measurements, e.g. low-dimensional linear measurements of signals taken with Gaussian random vectors. In this paper, we present a highly efficient algorithm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4415f7549c46d82f4a6a5f8a5e4b8b83
https://doi.org/10.1109/icassp.2014.6854016
https://doi.org/10.1109/icassp.2014.6854016
Publikováno v:
ICIP
Compressive sensing allows us to recover signals that are linearly sparse in some basis from a smaller number of measurements than traditionally required. However, it has been shown that many classes of images or video can be more efficiently modeled
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9ed81126735dbf3ebfff421182067f65
https://doi.org/10.1109/icip.2013.6738102
https://doi.org/10.1109/icip.2013.6738102
Publikováno v:
ICASSP
Dictionary learning algorithms design a dictionary that is specifically tailored to enable sparse representation of a given set of training signals. In turn, the increased sparsity of the signals with respect to this dictionary enables significantly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a4940d2c736124149d1fa2e40f31f00
https://doi.org/10.1109/icassp.2013.6638709
https://doi.org/10.1109/icassp.2013.6638709
Autor:
Shannon M. Hughes, Yevgen Matviychuk
Publikováno v:
ICASSP
Solving inverse problems in signal processing often involves making prior assumptions about the signal being reconstructed. Here the appropriateness of the chosen model greatly determines the quality of the final result. Recently it has been proposed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8d7f48895fea00ad378103a6f07f0b9
https://doi.org/10.1109/icassp.2013.6638831
https://doi.org/10.1109/icassp.2013.6638831
Autor:
Hanchao Qi, Shannon M. Hughes
Publikováno v:
ICIP
Algorithms that can efficiently recover principal components of high-dimensional data from compressive sensing measurements (e.g. low-dimensional random projections) of it have been an important topic of recent interest in the literature. In this pap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d4e39d076022abc7e19ff5909dff84e
https://doi.org/10.1109/icip.2012.6467015
https://doi.org/10.1109/icip.2012.6467015
Autor:
Hanchao Qi, Shannon M. Hughes
Publikováno v:
ICASSP
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal's inner products with Gaussian random vectors. The number of measurements needed is based on the sparsity of the s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1761efbb99c79e640727219018e865b2
https://doi.org/10.1109/icassp.2011.5947214
https://doi.org/10.1109/icassp.2011.5947214