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Detecting Nanosheet Objects from Noisy CLSM Images Using Deep Learning Approach
| Content Provider | Semantic Scholar |
|---|---|
| Author | Fujioka, Hiroyuki Sawangphol, Jarupat Anraku, Shinya Miyamoto, Nobuyoshi Kino, Hitoshi Hidaka, Akinori |
| Copyright Year | 2019 |
| Abstract | This paper considers a problem of detecting nanosheets which are moving in colloidal liquid from confocal laser scanning microscopy (CLSM) images. Introducing the deep learning approach, we particularly develop a scheme for constructing the so-called `detection map’ consisting of the brightness value information on the area of nanosheets in CLSM images. Therein, we use an architecture of deep learning network ‘U-net’ and present how to implement such a network. The performance is demonstrated by some experimental studies. |
| Starting Page | 11 |
| Ending Page | 15 |
| Page Count | 5 |
| File Format | PDF HTM / HTML |
| DOI | 10.4028/www.scientific.net/KEM.804.11 |
| Volume Number | 804 |
| Alternate Webpage(s) | https://www.scientific.net/KEM.804.11.pdf |
| Alternate Webpage(s) | https://doi.org/10.4028/www.scientific.net%2FKEM.804.11 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
