Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Ivo Everts"'
Autor:
Ivo Everts, Thomas P Mast, Colin Berry, Pim A.L. Tonino, Frederik M. Zimmermann, Marcel van 't Veer, William F. Fearon, Bernard De Bruyne, Barry Hennigan, Nils P. Johnson, Daniel T. Johnson, Keith G. Oldroyd, Nico H.J. Pijls
Publikováno v:
EuroIntervention
EuroIntervention, 17(1), 51-58. EuroPCR
EuroIntervention, 17(1), 51-58. EuroPCR
Background: It would be ideal for a non-hyperaemic index to predict fractional flow reserve (FFR) more accurately, given FFR's extensive validation in a multitude of clinical settings. Aims: The aim of this study was to derive a novel non-hyperaemic
Publikováno v:
IEEE Transactions on Image Processing, 23(4), 1569-1580. Institute of Electrical and Electronics Engineers Inc.
This paper considers the recognition of realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, the
Publikováno v:
IEEE Transactions on Image Processing, 23(12), 5698-5706. Institute of Electrical and Electronics Engineers Inc.
Many computer vision applications, including image classification, matching, and retrieval use global image representations, such as the Fisher vector, to encode a set of local image patches. To describe these patches, many local descriptors have bee
Publikováno v:
2015 IEEE International Conference on Image Processing: ICIP 2015: proceedings: 27-30 September 2015, Québec City, Canada, 3846-3850
STARTPAGE=3846;ENDPAGE=3850;TITLE=2015 IEEE International Conference on Image Processing: ICIP 2015: proceedings: 27-30 September 2015, Québec City, Canada
ICIP
STARTPAGE=3846;ENDPAGE=3850;TITLE=2015 IEEE International Conference on Image Processing: ICIP 2015: proceedings: 27-30 September 2015, Québec City, Canada
ICIP
We propose a patch-specific metric learning method to improve matching performance of local descriptors. Existing methodologies typically focus on invariance, by completely considering, or completely disregarding all variations. We propose a metric l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd556250d6328075ad757b961d7ff147
https://dare.uva.nl/personal/pure/en/publications/perpatch-metric-learning-for-robust-image-matching(a430af3c-90ca-449e-8c9b-cebb76380f73).html
https://dare.uva.nl/personal/pure/en/publications/perpatch-metric-learning-for-robust-image-matching(a430af3c-90ca-449e-8c9b-cebb76380f73).html
Publikováno v:
Proceedings: 2013 IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2013 : 23-28 June 2013, Portland, Oregon, USA, 2850-2857
STARTPAGE=2850;ENDPAGE=2857;TITLE=Proceedings: 2013 IEEE Conference on Computer Vision and Pattern Recognition
CVPR
STARTPAGE=2850;ENDPAGE=2857;TITLE=Proceedings: 2013 IEEE Conference on Computer Vision and Pattern Recognition
CVPR
This paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cd70273db6fae22104f7a5fdb67c52a
https://dare.uva.nl/personal/pure/en/publications/evaluation-of-color-stips-for-human-action-recognition(6d353b01-fe42-479d-a3f9-b1c3742912a7).html
https://dare.uva.nl/personal/pure/en/publications/evaluation-of-color-stips-for-human-action-recognition(6d353b01-fe42-479d-a3f9-b1c3742912a7).html