Zobrazeno 1 - 10
of 251
pro vyhledávání: '"Takio Kurita"'
Publikováno v:
Intelligent Systems with Applications, Vol 17, Iss , Pp 200187- (2023)
The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for classifying the colony conditions
Externí odkaz:
https://doaj.org/article/1a73c9f0b0fb4a99ae7edac403de6d05
Autor:
Novanto Yudistira, Takio Kurita
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2017, Iss 1, Pp 1-12 (2017)
Abstract Human activity recognition requires both visual and temporal cues, making it challenging to integrate these important modalities. The usual schemes for integration are averaging and fixing the weights of both features for all samples. Howeve
Externí odkaz:
https://doaj.org/article/9388bad5b6fc4c60b2e27a1252a74f5d
Autor:
Tsubasa Hirakawa, Toru Tamaki, Takio Kurita, Bisser Raytchev, Kazufumi Kaneda, Chaohui Wang, Laurent Najman
Publikováno v:
IEEE Access, Vol 5, Pp 13617-13634 (2017)
In this paper, we propose a method for texture image labeling that works with a small number of training images. Our method is based on a tree of shapes and histogram features computed on the tree structure. Labeling results could be obtained by simp
Externí odkaz:
https://doaj.org/article/ed3e7228bf604ce5a7c36b8a9572c2cd
Autor:
Muthu Subash Kavitha, Takio Kurita, Soon-Yong Park, Sung-Il Chien, Jae-Sung Bae, Byeong-Cheol Ahn
Publikováno v:
PLoS ONE, Vol 12, Iss 12, p e0189974 (2017)
Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inad
Externí odkaz:
https://doaj.org/article/2f01cdf4f71b4115ae6db0ba18a79039
Publikováno v:
Journal of Systemics, Cybernetics and Informatics, Vol 9, Iss 6, Pp 63-68 (2011)
Selection of relevant genes that will give higher accuracy for sample classification (for example, to distinguish cancerous from normal tissues) is a common task in most microarray data studies. An evolutionary method based on generalization error bo
Externí odkaz:
https://doaj.org/article/da0413439bb943a89515ebd67a105d9c
Publikováno v:
PLoS ONE, Vol 7, Iss 3, p e32352 (2012)
In time-resolved spectroscopy, composite signal sequences representing energy transfer in fluorescence materials are measured, and the physical characteristics of the materials are analyzed. Each signal sequence is represented by a sum of non-negativ
Externí odkaz:
https://doaj.org/article/ac3c20cf83494341a707cf65c74cdfc2
Publikováno v:
Methods. 214:48-59
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32c0c6c9922328a1117e34180fc4fa46
https://doi.org/10.1016/j.ymeth.2023.04.007
https://doi.org/10.1016/j.ymeth.2023.04.007
Autor:
Yuichiro NOMURA, Takio KURITA
Publikováno v:
IEICE Transactions on Information and Systems. :1759-1768
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b6ccbb3cde57cd2a0f1721bd24164f2
https://doi.org/10.1587/transinf.2022edp7033
https://doi.org/10.1587/transinf.2022edp7033
Weakly-Supervised Action Localization, and Action Recognition Using Global–Local Attention of 3D CNN
Publikováno v:
International Journal of Computer Vision. 130:2349-2363
3D Convolutional Neural Network (3D CNN) captures spatial and temporal information on 3D data such as video sequences. However, due to the convolution and pooling mechanism, the information loss seems unavoidable. To improve the visual explanations a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb9163662d4af7bf77b3f89f564b1fa3
https://doi.org/10.1007/s11263-022-01649-x
https://doi.org/10.1007/s11263-022-01649-x
Autor:
Yuichiro NOMURA, Takio KURITA
Publikováno v:
IEICE Transactions on Information and Systems. :387-395
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c79c779e4f988c771fc7ae5756a19739
https://doi.org/10.1587/transinf.2021edp7127
https://doi.org/10.1587/transinf.2021edp7127