Zobrazeno 1 - 5
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pro vyhledávání: '"Vijay Kumar, B G"'
Publikováno v:
ICIP
Vessel segmentation of retinal images is a key diagnostic capability in ophthalmology. Early approaches addressing this problem employed hand-crafted filters to capture vessel structures, accompanied by morphological processing. More recently, deep l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::624319b29371ca1a17d83393f7c759cf
https://doi.org/10.1109/icip.2019.8803762
https://doi.org/10.1109/icip.2019.8803762
Publikováno v:
ICCV
To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples from the same class and maximise the relative distance between samples
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::188a58701957f316c3b53fa8afb6730b
http://arxiv.org/abs/1704.01285
http://arxiv.org/abs/1704.01285
Publikováno v:
ICCV
This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77d9e4a63aa446bb5fb41f059a991bfa
http://arxiv.org/abs/1611.08998
http://arxiv.org/abs/1611.08998
Publikováno v:
CVPR
Scopus-Elsevier
Scopus-Elsevier
Recent innovations in training deep convolutional neural network (ConvNet) models have motivated the design of new methods to automatically learn local image descriptors. The latest deep ConvNets proposed for this task consist of a siamese network th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d578b760657107ac1faa7e4c11153a81
http://arxiv.org/abs/1512.09272
http://arxiv.org/abs/1512.09272
Autor:
Vijay Kumar, B G, Patras, Ioannis
Publikováno v:
2013 10th IEEE International Conference & Workshops on Automatic Face & Gesture Recognition (FG); 2013, p1-8, 8p