no code implementations • 12 Sep 2021 • Ryoya Katafuchi, Terumasa Tokunaga
Through extensive experiments on PlantVillage, MVTec AD, and Cloud datasets, we demonstrate that the proposed layer-wise visual attention mechanism consistently boosts anomaly detection performances of an existing CNN model, even on imbalanced datasets.
no code implementations • 29 Nov 2020 • Ryoya Katafuchi, Terumasa Tokunaga
Although supervised image classifiers based on deep learning can be a powerful tool for plant disease diagnosis, they require a huge amount of labeled data.
no code implementations • 26 Aug 2015 • Osamu Hirose, Shotaro Kawaguchi, Terumasa Tokunaga, Yu Toyoshima, Takayuki Teramoto, Sayuri Kuge, Takeshi Ishihara, Yuichi Iino, Ryo Yoshida
Data types to which the method is applicable are characterized as follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to distinguish cells based only on shape and size, (iii) the number of imaged cells ranges in several hundreds, (iv) moves of nearly-located cells are strongly correlated and (v) cells do not divide.