Relevant bibliographies by topics / Three-dimensional convolution

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"

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McCutchen, C. W. "Convolution relation within the three-dimensional diffraction image." Journal of the Optical Society of America A 8, no. 6 (June 1, 1991): 868. http://dx.doi.org/10.1364/josaa.8.000868.

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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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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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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 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.
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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 (July 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 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.
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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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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 (August 17, 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 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.
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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 (December 5, 2013): 5296. http://dx.doi.org/10.1364/ol.38.005296.

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Li, Qiang, Qi Wang, and Xuelong Li. "Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution." Remote Sensing 12, no. 10 (May 21, 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 (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.
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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 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.
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Dissertations / Theses on the topic "Three-dimensional convolution"

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Fong, Chun Kin. "A freeform modeling system based on convolution surfaces from sketched silhouette curves /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20FONG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 63-68). Also available in electronic version. Access restricted to campus users.
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Чапалюк, Богдан Володимирович. "Системи автоматичної медичної комп’ютерної дiагностики з використанням методiв штучного iнтелекту." Doctoral thesis, Київ, 2020. https://ela.kpi.ua/handle/123456789/39677.

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Мета даного дисертацiйного дослiдження полягає в детальному розглядi, розробцi та удосконаленнi систем автоматичної комп’ютерної дiагностики раку легень використовуючи методи штучного iнтелекту, зокрема застосовуючи та удосконалюючи останнi досягнення в областi глибинного навчання. Для дiагностування раку легенiв в сучасних медичних закладах використовують комп’ютерну томографiю, що представляє собою тривимiрне зображення легенiв пацiєнта, отримане за допомогою рентгенiвського променю, що пошарово та поступово проходить через тканини людського тiла в рiзних напрямках, з рiзних кутiв та положень. Такий вид зображень використовується в роботi для аналiзу присутностi пухлини в легенях за допомогою згорткових нейронних мереж. Однак, такi особливi данi накладають свої складностi в розробцi систем медичного комп’ютерного дiагностування, оскiльки при роботi з ними необхiдно враховувати їхню тривимiрну природу та вiдповiднi просторовi зв’язки. Тому, в дисертацiйному дослiдженнi розглядається три основнi пiдходи для роботи з такими даними: 1. Використання двовимiрної згорткової нейронної мережi. Для кожного шару КТ знiмка застосовується згорткова нейронна мережа. Виходи мережi для кожного шару знiмку об’єднуються та фiнальний висновок робиться на основi правил навчання за набором зразкiв. 2. Використання тривимiрних згорткових нейронних мереж, якi враховують тривимiрну природу вхiдних даних та можуть вiднайти кориснi патерни використовуючи всi три просторовi вiсi. Часто, такi системи роздiляють задачу на декiлька етапiв, кожен з яких використовує тривимiрну згорткову нейронну мережу налаштовану пiд конкретну пiдзадачу. 3. Використання комбiнованої структури двовимiрної згорткової та рекурентної нейронних мереж. В такому пiдходi двовимiрну згорткову нейронну мережу використовують для представлення вхiдних даних в менш мiрному просторi шляхом навчання многовиду меншої розмiрностi. Завдяки цьому на кожному шарi КТ зображення будуть видiлятися тiльки найбiльш важливi високорiвневi ознаки. Отриманi ознаки обробляються двонаправленою рекурентною нейронною мережею з вентильним вузлом (англ. bidirectional gated recurrent neural network), яка навчається складним нелiнiйним функцiям, що описують просторовi залежностi та вплив мiж ними. Вихiд рекурентної мережi повертає ймовiрнiсть наявностi пухлини на знiмку. В рамках даного дисертацiйного дослiдження проводиться аналiз та виконується експерименти для кожного пiдходу, а отриманi результати порiвнюються з роботами iнших авторiв. Експерименти показують, що найбiльш точними є системи побудованi iз декiлькох тривимiрних згорткових нейронних мереж (одна мережа сегментує потенцiйнi проблемнi регiони, iнша класифiкує присутнiсть в таких регiонах пухлини). Однак, такi системи мають дуже великi обчислювальнi вимоги, через те що використовують операцiю тривимiрної згортки, вимоги до обчислювальної потужностi якої ростуть кубiчно зi збiльшенням розмiрностi вхiдного зображення. В такому випадку, запропонована архiтектура рекурентної згорткової нейронної мережi дозволяє отримати точнiсть роботи системи на достатньо високому рiвнi, в той же час використовуючи значно менш вимогливу до обчислювальних потужностей та пам’ятi операцiю двовимiрної згортки. Наукова новизна отриманих результатiв дисертацiї полягає в запропонованому здобувачем методi побудови комбiнованої структури системи комп’ютерної дiагностики, що полягає в поєднаннi двовимiрної згорткової та двонаправленої рекурентної нейронної мережi LSTM. На вiдмiну вiд iнших рiшень, така система враховує просторовi зв’язки мiж рiзними шарами знiмку комп’ютерної томографiї шляхом використання двонаправленої рекурентної нейронної мережi, на входi якої використовують високорiвневi ознаки сформованi за допомогою двовимiрної згорткової нейронної мережi. Високорiвневi ознаки будуються для кожного шару знiмку пацiєнта. За результатами експериментiв така архiтектура нейронної мережi змогла досягти значення AUC ROC на рiвнi 83%, що трохи нижче у порiвнянi з системами тривимiрних згорткових нейронних мереж, що показують значення AUC ROC на рiвнi 90-95%. Однак, отриманi результати є найвищими результатами для рекурентних нейронних мереж, що застосовуються для побудови систем комп’ютерної дiагностики раку легенiв. Також, запропонована архiтектура має вищу швидкодiю, що досягається шляхом використання операцiї двовимiрної згортки замiсть операцiї тривимiрної згортки, вимоги якої до обчислювальної потужностi та пам’ятi ростуть квадратично з розмiром вхiдних даних, а не кубiчно. Для ефективного навчання комбiнованої структури згорткової рекурентної нейронної мережi був запропонований механiзм м’якої уваги, що надав можливiсть нейроннiй мережi отримати iнформацiю про локацiю пухлини пiд час навчання. Згiдно проведених експериментiв, такий пiдхiд допомiг покращити показники метрики AUC ROC бiльш нiж на 8%. Практичне значення отриманих результатiв полягає в розширенi та удосконаленi iснуючих методiв побудови систем комп’ютерної дiагностики. Запропонована комбiнована структура згорткової нейронної мережi та двонаправленої рекурентної мережi дозволяє отримати достатньо високу точнiсть роботи системи та пiдвищує точнiсть роботи системи у порiвнянi з використанням звичайних рекурентних нейронних мереж. Також, така система вiдзначається використанням меншої кiлькостi ресурсiв чим у тривимiрної згорткової нейронної мережi. Проведенi експерименти та аналiз iснуючих методiв систем комп’ютерної дiагностики дозволив сформулювати необхiднi вимоги та пiдходи, якi потрiбно використовувати в залежностi вiд прiоритету швидкодiї чи точностi роботи системи. Запропонований механiзм м’якої уваги дозволяє значно пiдвищити ефективнiсть навчання комбiнованих архiтектур згорткових рекурентних нейронних мереж. Результати дисертацiйного дослiдження впроваджено в НДР за темою “Розроблення та дослiдження методiв обробки, розпiзнавання, захисту та зберiгання медичних зображень в розподiлених комп’ютерних системах” за номером держ реєстрацiї 0117U004267 (тема №2021п, код КВНТД I.1 01.05.02). Також, основнi результати роботи викладенi в 6 друкованих наукових роботах, з них двоє статей в наукових фахових виданнях України, 2 опублiковано в iноземних журналах, що iндексується в Googel Scholar та iнших базах даних, 1-а стаття у виданнi, що входить до Web of Science Core Collection та SCOPUS. Також опублiковано одну роботу в тезах доповiдей мiжнародної наукової конференцiї.
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Ma, Guohua 1970. "Topological consistency in skelatal modeling with convolution surfaces for conceptual design." Thesis, 2007. http://hdl.handle.net/2152/3619.

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This dissertation describes a new topology analysis tool for a skeletal based geometric modeling system for conceptual design. Skeletal modeling is an approach to creating solid models in which the engineer designs with lower dimensional primitives such as points, lines, and triangles. The skeleton is then "skinned over" to create the surfaces of the three-dimensional object. In this research, convolution surfaces are used to provide the flesh to the skeleton. Convolution surfaces are generated by convolving a kernel function with a geometric field function to create an implicit surface. Certain properties of convolution surfaces make them attractive for skeletal modeling, including: (1) providing analytic solutions for various geometry primitives (including points, line segments, and triangles); (2) generating smooth surfaces; and (3) providing well-behaved blending. We assume that engineering designers expect the topology of a skeletal model to be identical to that of the underlying skeleton. However, the topology of convolution surfaces can change arbitrarily, making it difficult to predict the topology of the generated surface from knowledge of the topology of the skeleton. To address this issue, we apply Morse theory to analyze the topology of convolution surfaces by detecting the critical points of the surfaces. We developed an efficient and intelligent algorithm to find the critical points (CPs) by analyzing the skeleton. The critical points provide valuable information about the topology of the convolution surfaces. By tracking the CPs, we know where and what kind of topology changes happen when the threshold value reaches the critical value at the CP. Topology matching is done in two steps: (1) global topology is tested by comparing the Betti numbers (number of component, loops, and voids) of the skeleton and the generated convolution surfaces; (2) with matched Betti numbers, local topology is tested by comparing the location of each loop and void area between the skeleton and surfaces. If the topology does not match, appropriate heuristics for determining parameter values of the convolution surfaces are applied to force the surface topology to match that of the skeleton. A recommend threshold value is then provided to generate the topology matched convolution surfaces.
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Patel, Arun. "A three-dimensional convolution engine for computing the reciprocal-space Ewald electrostatic energy in molecular dynamics simulations." 2007. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=958083&T=F.

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Yu-HsiungSu and 蘇鈺雄. "A highly effective transient thermal simulator using matrix convolution at Electronic System Level for three-dimensional integrated circuits." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/3q8r97.

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"Generalized Statistical Tolerance Analysis and Three Dimensional Model for Manufacturing Tolerance Transfer in Manufacturing Process Planning." Doctoral diss., 2011. http://hdl.handle.net/2286/R.I.9125.

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abstract: Mostly, manufacturing tolerance charts are used these days for manufacturing tolerance transfer but these have the limitation of being one dimensional only. Some research has been undertaken for the three dimensional geometric tolerances but it is too theoretical and yet to be ready for operator level usage. In this research, a new three dimensional model for tolerance transfer in manufacturing process planning is presented that is user friendly in the sense that it is built upon the Coordinate Measuring Machine (CMM) readings that are readily available in any decent manufacturing facility. This model can take care of datum reference change between non orthogonal datums (squeezed datums), non-linearly oriented datums (twisted datums) etc. Graph theoretic approach based upon ACIS, C++ and MFC is laid out to facilitate its implementation for automation of the model. A totally new approach to determining dimensions and tolerances for the manufacturing process plan is also presented. Secondly, a new statistical model for the statistical tolerance analysis based upon joint probability distribution of the trivariate normal distributed variables is presented. 4-D probability Maps have been developed in which the probability value of a point in space is represented by the size of the marker and the associated color. Points inside the part map represent the pass percentage for parts manufactured. The effect of refinement with form and orientation tolerance is highlighted by calculating the change in pass percentage with the pass percentage for size tolerance only. Delaunay triangulation and ray tracing algorithms have been used to automate the process of identifying the points inside and outside the part map. Proof of concept software has been implemented to demonstrate this model and to determine pass percentages for various cases. The model is further extended to assemblies by employing convolution algorithms on two trivariate statistical distributions to arrive at the statistical distribution of the assembly. Map generated by using Minkowski Sum techniques on the individual part maps is superimposed on the probability point cloud resulting from convolution. Delaunay triangulation and ray tracing algorithms are employed to determine the assembleability percentages for the assembly.
Dissertation/Thesis
Ph.D. Mechanical Engineering 2011
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Pereira, Celso Daniel Santos. "Optical camera communications and machine learning for indoor visible light positioning." Master's thesis, 2021. http://hdl.handle.net/10773/33645.

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In this dissertation, a 3D indoor visible light positioning system based on machine learning and optical camera communications is presented. The system uses LED (light-emitting diode) luminaires as reference points and a rolling shutter complementary metal-oxide semiconductor (CMOS) sensor as the receiver. The LED luminaires are modulated using On-Off Keying (OOK) with unique frequencies. You Only Look Once version 5 (YOLOv5) is used for classification and estimation of the position of each visible LED luminaire in the image. The pose of the receiver is estimated using a perspective-npoint (PnP) problem algorithm. The system is validated using a real-world sized setup containing eight LED luminaires, and achieved an average positioning error of 3.5 cm. The average time to compute the camera pose is approximately 52 ms, which makes it suitable for real-time positioning. To the best of our knowledge, this is the first application of the YOLOv5 algorithm in the field of VLP for indoor environments.
Nesta dissertação, é apresentado um sistema de posicionamento 3D por luz visível, baseado em aprendizagem automática e comunicações com câmara. O sistema foi desenvolvido para espaços interiores e utiliza luminárias LED (díodo emissor de luz) como pontos de referência e um sensor CMOS (complementary metal-oxide semiconductor) como recetor. As luminárias LED são moduladas utilizando OOK (On–Off Keying) com frequências únicas. O algoritmo YOLOv5 (You Only Look Once version 5) é utilizado para classificar e estimar a posição de cada luminária LED visível na imagem. A posição e orientação do recetor é estimada utilizando um algoritmo de geometria projetiva. O sistema foi validado utilizando um setup em tamanho real com 8 luminárias LED, e obteve um erro de posicionamento médio de 3.5 cm. O tempo médio para obter a posição e orientação da câmara é de aproximadamente 52ms, o que torna o sistema adequado para posicionamento em tempo real. Tanto quanto sabemos, esta é a primeira aplicação do algoritmo YOLOv5 para localização por luz visível em espaços interiores.
Mestrado em Engenharia Eletrónica e Telecomunicações
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Books on the topic "Three-dimensional convolution"

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Recent advances in visualizing 3D flow with LIC. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.

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E, Grosch C., and Langley Research Center, eds. Recent advances in visualizing 3D flow with LIC. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.

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R, Johnson Christopher, Ma Kwan-liu, and Institute for Computer Applications in Science and Engineering., eds. Visualizing vector fields using line integral convolution and dye advection. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1996.

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R, Johnson Christopher, Ma Kwan-Liu, and Institute for Computer Applications in Science and Engineering., eds. Visualizing vector fields using line integral convolution and dye advection. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1996.

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Book chapters on the topic "Three-dimensional convolution"

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You, Hoydoo. "Crystal Truncation Rod as a Convolution of Three-Dimensional Bravais Lattice with X-Ray Reflectivity." In Springer Proceedings in Physics, 47–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77144-6_9.

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Skublewska-Paszkowska, Maria, Edyta Lukasik, Bartłomiej Szydlowski, Jakub Smolka, and Pawel Powroznik. "Recognition of Tennis Shots Using Convolutional Neural Networks Based on Three-Dimensional Data." In Advances in Intelligent Systems and Computing, 146–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31964-9_14.

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Zhou, Xiangrong, Takaaki Ito, Ryosuke Takayama, Song Wang, Takeshi Hara, and Hiroshi Fujita. "Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting." In Deep Learning and Data Labeling for Medical Applications, 111–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46976-8_12.

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Fan, Kaipeng, Jifeng Guo, Bo Yang, Lin Wang, Lizhi Peng, Baosheng Li, Jian Zhu, and Ajith Abraham. "A Prognosis Method for Esophageal Squamous Cell Carcinoma Based on CT Image and Three-Dimensional Convolutional Neural Networks." In Advances in Intelligent Systems and Computing, 622–31. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49342-4_60.

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Li, Nianfeng, Yupeng Li, Lina Li, Yan Li, and Zhiguo Xiao. "Three-Dimensional Human Posture Rehabilitation Detection Based on Vibe." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220094.

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The main manifestation of stroke patients is physical disorder, so physical rehabilitation treatment will also become a very important link. In order to solve this problem, we have researched and developed a physical rehabilitation detection system based on VIBE. System using existing large-scale motion capture data set (AMASS) and unpaired, the laboratory environment points 2D note, by convolution neural network training out a preliminary training model, and then will recover limb disorders of the video input into a network testing, according to the result of parameter judge the body recovery degree of the patients. In addition, the attention mechanism is added in the network to better fit the movements of the body to achieve the real effect. Through the analysis of the test results of rehabilitation video, it is proved that this method has a good effect on the detection of limb rehabilitation.
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Zhao, Di. "Mobile GPU Computing Based Filter Bank Convolution for Three-Dimensional Wavelet Transform." In Biometrics, 761–77. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch031.

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Mobile GPU computing, or System on Chip with embedded GPU (SoC GPU), becomes in great demand recently. Since these SoCs are designed for mobile devices with real-time applications such as image processing and video processing, high-efficient implementations of wavelet transform are essential for these chips. In this paper, the author develops two SoC GPU based DWT: signal based parallelization for discrete wavelet transform (sDWT) and coefficient based parallelization for discrete wavelet transform (cDWT), and the author evaluates the performance of three-dimensional wavelet transform on SoC GPU Tegra K1. Computational results show that, SoC GPU based DWT is significantly faster than SoC CPU based DWT. Computational results also show that, sDWT can generally satisfy the requirement of real-time processing (30 frames per second) with the image sizes of 352×288, 480×320, 720×480 and 1280×720, while cDWT can only obtain read-time processing with small image sizes of 352×288 and 480×320.
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Glazer, A. M. "4. Diffraction." In Crystallography: A Very Short Introduction, 64–93. Oxford University Press, 2016. http://dx.doi.org/10.1093/actrade/9780198717591.003.0004.

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In order to understand diffraction from a three-dimensional crystal, a new type of lattice, constructed from the real lattice used so far, is defined. It is the reciprocal lattice proposed by Paul Ewald around 1911. ‘Diffraction’ considers how this lattice is constructed and investigates its uses in understanding diffraction. Reflection intensities and amplitudes are also explained along with Friedel’s Law; the convolution theorem; Fourier Transformation; powder diffraction, which has many uses in industry and in academic research; incommensurate or modulated crystals; and quasicrystals, often seen in metal alloys, which cannot be explained by conventional crystal symmetry ideas. Finally, disorder in crystals is discussed.
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Robinson, Joseph E. "Amplitude And Phase In Map And Image Enhancement." In Computers in Geology - 25 Years of Progress. Oxford University Press, 1994. http://dx.doi.org/10.1093/oso/9780195085938.003.0022.

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Many geographic information systems (GIS) and computer mapping packages for graphic display provide data enhancement routines that utilize spatial filtering and may even include sample filter operators. With care, spatial filtering or convolving a filter with string or gridded data can be very effective in enhancing digital information. Filtering is intended to highlight specific features, remove unwanted noise or restore an image. The filtered output may be smoothed, it may portray features of a specific size, or it may subject data to mathematical operations. Results depend on the form of the filter operator, Fourier analysis of the filter operator produces frequency and phase diagrams that describe the effect the filter will have on the input data. These diagrams indicate how the component frequencies in the input data will be altered by the filter. Phase relates to the position of contained features, and amplitudes are responsible for feature definition. Derivative filtering may alter both phase and amplitude and completely change the appearance of the output. Spatial filtering can be very effective, however the interpreter must exercise great care selecting suitable filters. Fourier analysis can aid in design of a suitable filter and in the resultant evaluation. Examples herein describe some common three-value filters for string data and three-by-three matrix filters for grid and raster data. Digital spatial filters are arrays of values with the same form and interval as the original numeric data set (e.g., Robinson and Treitel, 1969). Filters may be strings of one-dimensional values, two-dimensional gridded data sets, or even multi-dimensional data. Spatial filters to be used with digital data sets can be designed and evaluated in either the spatial or the frequency domain. Similarly, line, map, and image data can be transformed and viewed in both domains. Fourier analysis is the bridge between the two domains (Cooley and Tukey, 1965; Gentleman and Sande, 1966; Robinson and Cohn, 1979; Sondegard, Robinson and Merriam, 1989). The filtering operation can be carried out in either domain by convolution in the spatial domain or by multiplication in the frequency domain. Convolution is the Fourier transform of multiplication.
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Naik, K. Jairam, and Annukriti Soni. "Video Classification Using 3D Convolutional Neural Network." In Advancements in Security and Privacy Initiatives for Multimedia Images, 1–18. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2795-5.ch001.

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Since video includes both temporal and spatial features, it has become a fascinating classification problem. Each frame within a video holds important information called spatial information, as does the context of that frame relative to the frames before it in time called temporal information. Several methods have been invented for video classification, but each one is suffering from its own drawback. One of such method is called convolutional neural networks (CNN) model. It is a category of deep learning neural network model that can turn directly on the underdone inputs. However, such models are recently limited to handling two-dimensional inputs only. This chapter implements a three-dimensional convolutional neural networks (CNN) model for video classification to analyse the classification accuracy gained using the 3D CNN model. The 3D convolutional networks are preferred for video classification since they inherently apply convolutions in the 3D space.
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Dou, Qi, Hao Chen, Jing Qin, and Pheng-Ann Heng. "Automatic lesion detection with three-dimensional convolutional neural networks." In Biomedical Information Technology, 265–93. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-816034-3.00009-2.

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Conference papers on the topic "Three-dimensional convolution"

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Colomb, Tristan, Frédéric Montfort, and Christian Depeursinge. "Small reconstruction distance in convolution formalism." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2008. http://dx.doi.org/10.1364/dh.2008.dma4.

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Jang, Jae-Young, Suk-Pyo Hong, Donghak Shin, and Eun-Soo Kim. "Computational 3D Image Reconstruction of Curved Integral Imaging Using Convolution Property Between Periodic Functions." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/dh.2013.dw2a.15.

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Hubbard, Paul F., Kristin L. Umland, M. C. Pereyra, and Thomas P. Caudell. "Three-dimensional audio localization using wavelet-domain convolution." In Optical Science and Technology, SPIE's 48th Annual Meeting, edited by Michael A. Unser, Akram Aldroubi, and Andrew F. Laine. SPIE, 2003. http://dx.doi.org/10.1117/12.504550.

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Tian, Qianyu, Zonghua Zhang, and Nan Gao. "Three-dimensional fingerprint recognition by using convolution neural network." In International Conference on Optical Instruments and Technology 2017: Optoelectronic Measurement Technology and System, edited by Jigui Zhu, Kexin Xu, Liquan Dong, Hwa-Yaw Tam, and Hai Xiao. SPIE, 2018. http://dx.doi.org/10.1117/12.2294017.

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Suzuki, Motofumi T., and Yoshitomo Yaginuma. "A solid texture analysis based on three-dimensional convolution kernels." In Electronic Imaging 2007, edited by J. Angelo Beraldin, Fabio Remondino, and Mark R. Shortis. SPIE, 2007. http://dx.doi.org/10.1117/12.705028.

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Zou, Hai-Yang, and Jian-Qing Gao. "Three Dimensional Image Reconstruction Based on Normalized Convolution Algorithm of SIFT." In 2017 International Conference on Network and Information Systems for Computers (ICNISC). IEEE, 2017. http://dx.doi.org/10.1109/icnisc.2017.00066.

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Wu, Chengpeng, Yuxiang Xing, Hewei Gao, Li Zhang, Xinbin Li, Shengping Wang, and Weijun Peng. "Information retrieval in x-ray imaging with grating interferometry using convolution neural network." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534270.

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Kamanditya, Bharindra, Randy Pangestu Kuswara, Muhammad Adi Nugroho, and Benyamin Kusumoputro. "Convolution Neural Network for Pose Estimation of Noisy Three-Dimensional Face Images." In 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, 2018. http://dx.doi.org/10.1109/icetas.2018.8629150.

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Lin, Shu-Yen, Kuan-Han Lin, Chun-Kuan Tsai, and Po-Hsiang Tseng. "Reconfigurable MAC Systolic Array Architecture Design for Three-Dimensional Convolution Neural Network." In 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2020. http://dx.doi.org/10.1109/icce-taiwan49838.2020.9258069.

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Li, Sui, Dong Zeng, Zhaoying Bian, and Jianhua Ma. "Low-dose cerebral CT perfusion restoration via non-local convolution neural network: initial study." In The Fifteenth International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, edited by Samuel Matej and Scott D. Metzler. SPIE, 2019. http://dx.doi.org/10.1117/12.2534800.

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