Results 1 to 10 of about 148,198 (270)
Sparse 3D convolutional neural networks [PDF]
Procedings of the British Machine Vision Conference 2015, 2015 We have implemented a convolutional neural network designed for processing sparse three-dimensional input data. The world we live in is three dimensional so there are a large number of potential applications including 3D object recognition and analysis ...
Graham, Ben
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Polarimetric SAR image classification using 3D generative adversarial network [PDF]
MATEC Web of Conferences, 2021 In this paper, a new architecture of three-dimensional deep convolutional generative adversarial network(3D-DCGAN) is specially defined to solve the unstable training problem of GAN and make full use of the information involved in polarimetric data ...
Liu Lu, Feng Guobao
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3D convolutional neural networks for stalled brain capillary detection [PDF]
Computers in Biology and Medicine, 2022 Adequate blood supply is critical for normal brain function. Brain vasculature dysfunctions such as stalled blood flow in cerebral capillaries are associated with cognitive decline and pathogenesis in Alzheimer's disease. Recent advances in imaging technology enabled generation of high-quality 3D images that can be used to visualize stalled blood ...
Roman Solovyev +2 more
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3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts [PDF]
Sensors, 2021 Deep learning methods have been successfully applied to image processing, mainly using 2D vision sensors. Recently, the rise of depth cameras and other similar 3D sensors has opened the field for new perception techniques. Nevertheless, 3D convolutional neural networks perform slightly worse than other 3D deep learning methods, and even worse than ...
Ibon Merino +3 more
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MixFormer: A Self-Attentive Convolutional Network for 3D Mesh Object Recognition
Algorithms, 2023 3D mesh as a complex data structure can provide effective shape representation for 3D objects, but due to the irregularity and disorder of the mesh data, it is difficult for convolutional neural networks to be directly applied to 3D mesh data processing.
Lingfeng Huang, Jieyu Zhao, Yu Chen
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IEEE Access, 2021 3D data can be instrumental to the computer vision field as it provides insightful information about the full 3D models' geometry. Recently, with easy access to both computational power and huge 3D databases, it is feasible to apply convolutional neural ...
Ilyass Ouazzani Taybi +2 more
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Advances in Multimedia, 2022 In order to improve the classification effect of the 3D CAD model, this paper combines the knowledge recognition algorithm of convolutional neural network to construct the 3D CAD model classification model.
Weiwei Wang, Dandan Sun
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Applied Sciences, 2022 Three-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA).
Kun Ruan +9 more
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International Journal of Advanced Robotic Systems, 2022 Nowadays, multi-sensor fusion is a popular tool for feature recognition and object detection. Integrating various sensors allows us to obtain reliable information about the environment.
Vinicio Rosas-Cervantes +3 more
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Frontiers in Neuroinformatics, 2022 ObjectiveTo explore the feasibility of a deep learning three-dimensional (3D) V-Net convolutional neural network to construct high-resolution computed tomography (HRCT)-based auditory ossicle structure recognition and segmentation models.MethodsThe ...
Xing-Rui Wang +10 more
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