no code implementations • 4 Apr 2024 • Naoya Sogi, Hiroyuki Oyama, Takashi Shibata, Makoto Terao
The key idea behind the proposed method is an end-to-end approach for determining whether the action plan can complete a given task instead of manually redesigning the conditions.
no code implementations • 31 Mar 2023 • Takumi Kanai, Naoya Sogi, Atsuto Maki, Kazuhiro Fukui
This paper proposes a new method for anomaly detection in time-series data by incorporating the concept of difference subspace into the singular spectrum analysis (SSA).
1 code implementation • 26 Jul 2022 • Tomoki Uchiyama, Naoya Sogi, Satoshi Iizuka, Koichiro Niinuma, Kazuhiro Fukui
The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D temporal-spatial data space and then measure the change degree in the output score.
no code implementations • 8 Nov 2021 • Lincon S. Souza, Naoya Sogi, Bernardo B. Gatto, Takumi Kobayashi, Kazuhiro Fukui
The image set is represented by a low-dimensional input subspace; and this input subspace is matched with reference subspaces by a similarity of their canonical angles, an interpretable and easy to compute metric.
no code implementations • 29 Oct 2019 • Kazuhiro Fukui, Naoya Sogi, Takumi Kobayashi, Jing-Hao Xue, Atsuto Maki
To avoid the difficulty, we first introduce geometrical Fisher discriminant analysis (gFDA) based on a simplified Fisher criterion.
no code implementations • 26 Sep 2019 • Shin-Fang Ch'ng, Naoya Sogi, Pulak Purkait, Tat-Jun Chin, Kazuhiro Fukui
Planar markers are useful in robotics and computer vision for mapping and localisation.
no code implementations • 14 Mar 2019 • Naoya Sogi, Rui Zhu, Jing-Hao Xue, Kazuhiro Fukui
Moreover, to enhance the framework, we introduce a discriminant space that maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected convex cones onto the discriminant space, similar to the Fisher discriminant analysis.
no code implementations • 31 May 2018 • Naoya Sogi, Taku Nakayama, Kazuhiro Fukui
In this paper, we propose a method for image-set classification based on convex cone models, focusing on the effectiveness of convolutional neural network (CNN) features as inputs.