Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Masanao Matsumoto"'
Publikováno v:
IEEE Access, Vol 12, Pp 127244-127258 (2024)
This paper presents a multi-modal Gaussian process latent variable model with semi-supervised label dequantization. In real-world applications, although user ratings are often attached to the content, they are roughly provided and are limited in numb
Externí odkaz:
https://doaj.org/article/0e6ad5558f8341caafa99dc3c55fcb30
Publikováno v:
IEEE Access, Vol 9, Pp 21810-21822 (2021)
Supervised fractional-order embedding multiview canonical correlation analysis via ordinal label dequantization (SFEMCCA-OLD) for image interest estimation is presented in this paper. SFEMCCA-OLD is a CCA method that realizes accurate integration of
Externí odkaz:
https://doaj.org/article/45c1bff0450d4aad92d5c920ab32e6fb
Autor:
Masanao Matsumoto, Nobuyuki Takeuchi
Publikováno v:
Nippon Laser Igakkaishi. 42:2-7
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7de422b1c20ce1c8935883ec2b8c6966
https://doi.org/10.2530/jslsm.jslsm-42_0001
https://doi.org/10.2530/jslsm.jslsm-42_0001
Publikováno v:
ICASSP
This paper presents multi-modal label dequantized Gaussian process latent variable model (mLDGP) for ordinal label estimation. mLDGP is constructed based on a probabilistic generative model via Gaussian process and realizes accurate calculation of co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd6276fbfb3a21187cf4ce20985f4d51
https://doi.org/10.1109/icassp39728.2021.9415090
https://doi.org/10.1109/icassp39728.2021.9415090
Publikováno v:
GCCE
This paper presents an estimation method of user-specific interests for images. The proposed method computes a projection which maximizes the correlation between “eye gaze data which are collected while watching images” and “visual and text fea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1e1cf1155dbd2e1b3de9a7db2576320
https://doi.org/10.1109/gcce46687.2019.9015464
https://doi.org/10.1109/gcce46687.2019.9015464
Publikováno v:
ICCE-TW
This paper presents a Convolutional Sparse Coding (CSC)-based anomalous event detection method in surveillance videos. The proposed method derives new features from reconstruction errors and sparse coefficient maps obtained by CSC, and the anomalous
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::71754f611e5d02d12d56677f9304b78d
https://doi.org/10.1109/icce-tw46550.2019.8991951
https://doi.org/10.1109/icce-tw46550.2019.8991951
Publikováno v:
LifeTech
This paper presents a detection method of chronic gastritis from gastric X-ray images. The conventional method cannot detect chronic gastritis accurately since the number of non-gastritis images is overwhelmingly larger than the number of gastritis i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a68b366fd46ec067b71fab6296bc7b7
https://doi.org/10.1109/lifetech.2019.8884074
https://doi.org/10.1109/lifetech.2019.8884074
Publikováno v:
GCCE
A novel method for missing image data estimation is presented in this paper. The proposed method realizes accurate estimation of missing image data by iterating dictionary learning and Convolutional Sparse Coding (CSC). Specifically, our method itera
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::874ff0dc8d487a5df213c048234b58df
https://doi.org/10.1109/gcce.2018.8574839
https://doi.org/10.1109/gcce.2018.8574839
Publikováno v:
Key Engineering Materials. :27-32
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1eef4aef23aa499ab627822f44af5bb4
https://doi.org/10.4028/www.scientific.net/kem.243-244.27
https://doi.org/10.4028/www.scientific.net/kem.243-244.27