Titlebook: Industrial Competitiveness and Design Evolution; Takahiro Fujimoto,Fumihiko Ikuine Book 2018 Springer Japan KK, part of Springer Nature 20

FLING

無(wú)法解釋

煞費(fèi)苦心

Young Won Park,Takahiro Fujimotom is the example-based approach. The existing example-based approaches often calculate a global pose error to search a single match 3D pose from the source library. This way fails to capture the local deformations of human pose and highly dependent on a large training set. To alleviate these issues,

Connotation

chandel

Fumihiko Ikuine Besides degrading the visual perception of the CSI, this poor quality also significantly affects the performance of 3D model reconstruction. Most of the existing image-enhanced methods, however, focus on processing natural images but not CSI. In this paper, we propose a novel and effective CSI enha

使成波狀

Fumihiko Ikuineon convolutional neural networks (CNNs). There is little to no progress on studying the DG performance of vision transformers (ViTs), which are challenging the supremacy of CNNs on standard benchmarks, often built on i.i.d assumption. This renders the real-world deployment of ViTs doubtful. In this

表否定

Wei Huang,Masanori Yasumoto,Jing-Ming Shiud at image-level or pixel-level. Considering that pixel-level anomaly classification achieves better representation learning in a finer-grained manner, we regard data augmentation transforms as a self-supervised segmentation task from which to extract the critical and representative information from

職業(yè)拳擊手

Jing-Ming Shiu,Masanori Yasumotoections on different angles into a 3D CT image. For minimizing the X-ray induced ionizing radiation, sparse-view CBCT takes fewer projections by a wider-angle interval, but suffers from an inferior CT reconstruction quality. To solve this, the recent solutions mainly resort to synthesizing missing p

CRUMB

Takahiro Fujimoto,Fumihiko Ikuinerojection .ttention UNet, named ., for 3D medical image segmentation, especially for small targets. Considering the large proportion of the background in the 3D feature space, we introduce a projection strategy to project the 3D features into three orthogonal 2D planes to capture the contextual atte

諄諄教誨

Takahiro Fujimoto,Fumihiko Ikuinened promising results in this domain with their effectiveness in learning feature representation. Both local and global features are crucial for medical image classification tasks, particularly for 3D medical image data, however, the receptive field of the convolution kernel limits the global featur