Results 21 to 30 of about 2,004,297 (387)
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [PDF]
International Conference on 3D Vision, 2016 Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used ...
F. Milletarì +2 more
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Contextual Convolutional Neural Networks [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the standard convolution, which is the core component of convolutional neural networks. CoConv is implicitly equipped with the capability of incorporating contextual information while maintaining a similar number of parameters and computational cost ...
Radu Tudor Ionescu +2 more
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A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much
Zewen Li +4 more
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Parallel accelerator design for convolutional neural networks based on FPGA
Dianzi Jishu Yingyong, 2021 In recent years, convolutional neural network plays an increasingly important role in many fields. However, power consumption and speed are the main factors limiting its application.
Wang Ting, Chen Binyue, Zhang Fuhai
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Quantum convolutional neural networks [PDF]
Nature Physics, 2019 12 pages, 11 figures. v2: New application to optimizing quantum error correction codes, added sample complexity analysis, more details for experimental realizations, and other minor ...
Soonwon Choi +3 more
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Biomolecules, 2021 Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to
Kaisa Liimatainen +3 more
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Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring [PDF]
Computer Vision and Pattern Recognition, 2016 Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision problem as blurs arise not only from multiple object motions but also from camera shake, scene depth variation.
Seungjun Nah +2 more
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De Computis, 2023 Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition.
Mohammad Mustafa Taye
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PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes [PDF]
Robotics: Science and Systems, 2017 Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work,
Yu Xiang +3 more
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Deep Convolutional Neural Network for Inverse Problems in Imaging [PDF]
IEEE Transactions on Image Processing, 2016 In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades.
Kyong Hwan Jin +3 more
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