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Relevant bibliographies by topics / Three-dimensional convolution

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Journal articles

Academic literature on the topic 'Three-dimensional convolution'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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Journal articles on the topic "Three-dimensional convolution"

1

McCutchen, C. W. "Convolution relation within the three-dimensional diffraction image." Journal of the Optical Society of America A 8, no. 6 (1991): 868. http://dx.doi.org/10.1364/josaa.8.000868.

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2

Wells, N. H., C. S. Burrus, G. E. Desobry, and A. L. Boyer. "Three-dimensional Fourier convolution with an array processor." Computers in Physics 4, no. 5 (1990): 507. http://dx.doi.org/10.1063/1.168385.

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3

Feng Bowen, 冯博文, 吕晓琪 Lü Xiaoqi, 谷宇 Gu Yu, 李菁 Li Qing, and 刘阳 Liu Yang. "Three-Dimensional Parallel Convolution Neural Network Brain Tumor Segmentation Based on Dilated Convolution." Laser & Optoelectronics Progress 57, no. 14 (2020): 141009. http://dx.doi.org/10.3788/lop57.141009.

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4

Fan, Wenxian, and Yebing Zou. "Three-dimensional Motion Skeleton Reconstruction Algorithm for Gymnastic Dancing Movements." International Journal of Circuits, Systems and Signal Processing 16 (January 7, 2022): 1–5. http://dx.doi.org/10.46300/9106.2022.16.1.

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Aiming at the problem of inaccurate matching results in the traditional three-dimensional reconstruction algorithm of gymnastic skeleton, a three-dimensional motion skeleton reconstruction algorithm of gymnastic dance action is proposed. Taking the center of gravity of the human body as the origin, the position of other nodes in the camera coordinate system relative to the center point of the human skeleton model is calculated, and the human skeleton data collection is completed through action division and posture feature calculation. Polynomial density is introduced into the integration of co

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5

Hyeon, Janghun, Weonsuk Lee, Joo Hyung Kim, and Nakju Doh. "NormNet: Point-wise normal estimation network for three-dimensional point cloud data." International Journal of Advanced Robotic Systems 16, no. 4 (2019): 172988141985753. http://dx.doi.org/10.1177/1729881419857532.

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In this article, a point-wise normal estimation network for three-dimensional point cloud data called NormNet is proposed. We propose the multiscale K-nearest neighbor convolution module for strengthened local feature extraction. With the multiscale K-nearest neighbor convolution module and PointNet-like architecture, we achieved a hybrid of three features: a global feature, a semantic feature from the segmentation network, and a local feature from the multiscale K-nearest neighbor convolution module. Those features, by mutually supporting each other, not only increase the normal estimation pe

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6

Yu Feng, 冯雨, 易本顺 Benshun Yi, 吴晨玥 Chenyue Wu, and 章云港 Yungang Zhang. "Pulmonary Nodule Recognition Based on Three-Dimensional Convolution Neural Network." Acta Optica Sinica 39, no. 6 (2019): 0615006. http://dx.doi.org/10.3788/aos201939.0615006.

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7

Kim, Dongyi, Hyeon Cho, Hochul Shin, Soo-Chul Lim, and Wonjun Hwang. "An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video." Sensors 19, no. 16 (2019): 3579. http://dx.doi.org/10.3390/s19163579.

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Interaction forces are traditionally predicted by a contact type haptic sensor. In this paper, we propose a novel and practical method for inferring the interaction forces between two objects based only on video data—one of the non-contact type camera sensors—without the use of common haptic sensors. In detail, we could predict the interaction force by observing the texture changes of the target object by an external force. For this purpose, our hypothesis is that a three-dimensional (3D) convolutional neural network (CNN) can be made to predict the physical interaction forces from video image

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8

Kou, Shan Shan, Colin J. R. Sheppard, and Jiao Lin. "Calculation of the volumetric diffracted field with a three-dimensional convolution: the three-dimensional angular spectrum method." Optics Letters 38, no. 24 (2013): 5296. http://dx.doi.org/10.1364/ol.38.005296.

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9

Li, Qiang, Qi Wang, and Xuelong Li. "Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution." Remote Sensing 12, no. 10 (2020): 1660. http://dx.doi.org/10.3390/rs12101660.

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Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works. One is to use the typical three-dimensional convolution analysis, resulting in more parameters of the network. The other is not to pay more attention to the mining of hyperspectral image spatial information, when the spectral information can be extracted. To address these issues, in this paper, we propose a mixed convolutional network (MCNet) for hyperspectral image super-resolution. We design a novel mixed convolutional module

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10

Igumnov, L. A., I. V. Vorobtsov, and S. Yu Litvinchuk. "Boundary Element Method with Runge-Kutta Convolution Quadrature for Three-Dimensional Dynamic Poroelasticity." Applied Mechanics and Materials 709 (December 2014): 101–4. http://dx.doi.org/10.4028/www.scientific.net/amm.709.101.

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The paper contains a brief introduction to the state of the art in poroelasticity models, in BIE & BEM methods application to solve dynamic problems in Laplace domain. Convolution Quadrature Method is formulated, as well as Runge-Kutta convolution quadrature modification and scheme with a key based on the highly oscillatory quadrature principles. Several approaches to Laplace transform inversion, including based on traditional Euler stepping scheme and Runge-Kutta stepping schemes, are numerically compared. A BIE system of direct approach in Laplace domain is used together with the discret

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