Kyushu University Academic Staff Educational and Research Activities Database
List of Papers
Shoko Miyauchi Last modified date:2024.03.04

Assistant Professor / Real World Robotics / Department of Advanced Information Technology / Faculty of Information Science and Electrical Engineering


Papers
1. Shoko Miyauchi, Ken'ichi Morooka, Ryo Kurazume, Isomorphic Mesh Generation from Point Clouds with Multilayer Perceptrons, IEEE Transactions on Visualization and Computer Graphics, 10.1109/TVCG.2024.3367855., 2024.02, A novel neural network called the isomorphic mesh generator (iMG) is proposed to generate isomorphic meshes from point clouds containing noise and missing parts. Isomorphic meshes of arbitrary objects exhibit a unified mesh structure, despite objects belonging to different classes. This unified representation enables various modern deep neural networks (DNNs) to easily handle surface models without requiring additional pre-processing. Additionally, the unified mesh structure of isomorphic meshes enables the application of the same process to all isomorphic meshes, unlike general mesh models, where processes need to be tailored depending on their mesh structures. Therefore, the use of isomorphic meshes can ensure efficient memory usage and reduce calculation time. Apart from the point cloud of the target object used as input for the iMG, point clouds and mesh models need not be prepared in advance as training data because the iMG is a data-free method. Furthermore, the iMG outputs an isomorphic mesh obtained by mapping a reference mesh to a given input point cloud. To stably estimate the mapping function, a step-by-step mapping strategy is introduced. This strategy enables flexible deformation while simultaneously maintaining the structure of the reference mesh. Simulations and experiments conducted using a mobile phone have confirmed that the iMG reliably generates isomorphic meshes of given objects, even when the input point cloud includes noise and missing parts..
2. Kurazume, Tomoki Hiramatsu, Masaya Kamei, Daiji Inoue, Akihiro Kawamura, Shoko Miyauchi, and Qi An, Development of AR training systems for Humanitude dementia care, 10.1080/01691864.2021.2017342, 2022.01.
3. Junichi Inokuchi, Fumio Kinoshita, Yoshinao Oda, Masatoshi Eto, Ryo Kurazume, Ken'ichi Morooka, Jun Mutaguchi, Satoshi Kobayashi, Shoko Miyauchi, Aiko Umehara, Artificial intelligence for segmentation of bladder tumor cystoscopic images performed by U-Net with dilated convolution, 10.1089/end.2021.0483, 2022.01.
4. Fumiaki Ichihashi, Akira Koyama, Tetsuya Akashi, Shoko Miyauchi, Ken'ichi Morooka, Hajime Hojo, Hisahiro Einaga, Yoshio Takahashi, Toshiaki Tanigaki, Hiroyuki Shinada, Yasukazu Murakami, Automatic electron hologram acquisition of catalyst nanoparticles using particle detection with image processing and machine learning, Applied Physics Letters, 10.1063/5.0074231, 120, 6, 1-6, 2022.02, To enable better statistical analysis of catalyst nanoparticles by high-resolution electron holography, we improved the particle detection accuracy of our previously developed automated hologram acquisition system by using an image classifier trained with machine learning. The detection accuracy of 83% was achieved with the small training data of just 232 images showing nanoparticles by utilizing transfer learning based on VGG16 to train the image classifier. Although the construction of training data generally requires much effort, the time needed to select the training data candidates was significantly shortened by utilizing a pattern matching technique. Experimental results showed that the high-resolution hologram acquisition efficiency was improved by factors of about 100 and 6 compared to a scan method and a pattern-matching-only method, respectively..
5. Ken’ichi Morooka, Ryota Matsubara, Shoko Miyauchi, Takaichi Fukuda, Takeshi Sugii, Ryo Kurazume, Ancient pelvis reconstruction from collapsed component bones using statistical shape models, Machine Vision and Applications, 59-69, 2019.02.
6. Shoko Miyauchi, Ken'ichi Morooka, Tokuo Tsuji, Yasushi Miyagi, Takaichi Fukuda, Ryo Kurazume, Fast modified Self-organizing Deformable Model: Geometrical feature-preserving mapping of organ models onto target surfaces with various shapes and topologies, Computer Methods and Programs in Biomedicine, 157, 237-250, 2018.01.