Relevant bibliographies by topics / Three-dimensional convolution

Contents

  1. Journal articles

Academic literature on the topic 'Three-dimensional convolution'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Three-dimensional convolution.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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 convolution surface, and the human body model of convolution surface is established according to convolution surface. By using the method of binary parameter matching, the accuracy of the matching results is improved, and the three-dimensional skeleton of gymnastic dance movement is reconstructed. The experimental results show that the fitting degree between the proposed method and the actual reconstruction result is 99.8%, and the reconstruction result of this algorithm has high accuracy.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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 performance but also enable the estimation to be robust under severe noise perturbations or point deficiencies. The performance was validated in three different data sets: Synthetic CAD data (ModelNet), RGB-D sensor-based real 3D PCD (S3DIS), and LiDAR sensor-based real 3D PCD that we built and shared.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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 images. In this paper, we proposed a bottleneck-based 3D depthwise separable CNN architecture where the video is disentangled into spatial and temporal information. By applying the basic depthwise convolution concept to each video frame, spatial information can be efficiently learned; for temporal information, the 3D pointwise convolution can be used to learn the linear combination among sequential frames. To validate and train the proposed model, we collected large quantities of datasets, which are video clips of the physical interactions between two objects under different conditions (illumination and angle variations) and the corresponding interaction forces measured by the haptic sensor (as the ground truth). Our experimental results confirmed our hypothesis; when compared with previous models, the proposed model was more accurate and efficient, and although its model size was 10 times smaller, the 3D convolutional neural network architecture exhibited better accuracy. The experiments demonstrate that the proposed model remains robust under different conditions and can successfully estimate the interaction force between objects.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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 (MCM) to extract the potential features by 2D/3D convolution instead of one convolution, which enables the network to more mine spatial features of hyperspectral image. To explore the effective features from 2D unit, we design the local feature fusion to adaptively analyze from all the hierarchical features in 2D units. In 3D unit, we employ spatial and spectral separable 3D convolution to extract spatial and spectral information, which reduces unaffordable memory usage and training time. Extensive evaluations and comparisons on three benchmark datasets demonstrate that the proposed approach achieves superior performance in comparison to existing state-of-the-art methods.
APA, Harvard, Vancouver, ISO, and other styles
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.

Full text
Abstract:
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 discretization technique based on the collocation method. The boundary is discretized with the quadrilateral 8-node biquadratic elements. Generalized boundary functions are approximated with the help of the Goldshteyn’s displacement-stress matched model. The time-stepping scheme can rely on the application of convolution theorem as well as integration theorem. By means of the developed software the following 3d poroelastodynamic problem were numerically treated: a Heaviside-shaped longitudinal load acting on the face of a column.
APA, Harvard, Vancouver, ISO, and other styles
More sources
You might also be interested in the extended bibliographies on the topic 'Three-dimensional convolution' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography