Results 251 to 260 of about 6,781,577 (302)

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

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3D object understanding with 3D Convolutional Neural Networks

Information Sciences, 2016
Feature engineering plays an important role in object understanding. Expressive discriminative features can guarantee the success of object understanding tasks. With remarkable ability of data abstraction, deep hierarchy architecture has the potential to represent objects.
Biao Leng, Yu Liu 0015, Kai Yu, Xiangyang Zhang, Zhang Xiong 0001   +4 more
openaire   +1 more source

3D convolutional neural networks by modal fusion

2017 IEEE International Conference on Image Processing (ICIP), 2017
We propose multi-view and volumetric convolutional neural networks (ConvNets) for 3D shape recognition, which combines surface normal and height fields to capture local geometry and physical size of an object. This strategy helps distinguishing between objects with similar geometries but different sizes.
Yusuke Yoshiyasu, Eiichi Yoshida, Sören Pirk, Leonidas J. Guibas   +3 more
openaire   +2 more sources

Towards 3D convolutional neural networks with meshes

2017 IEEE International Conference on Image Processing (ICIP), 2017
Voxels are an effective approach to 3D mesh and point cloud classification because they build upon mature Convolutional Neural Network concepts. We show however that their cubic increase in dimensionality is unsuitable for more challenging problems such as object detection in a complex point cloud scene. We observe that 3D meshes are analogous to graph
Miguel Domínguez, Felipe Petroski Such, Shagan Sah, Raymond W. Ptucha   +3 more
openaire   +1 more source

Convolutional Neural Networks for 3D Protein Classification

2020
The main goal of this chapter is to develop a system for automatic protein classification. Proteins are classified using CNNs trained on ImageNet, which are tuned using a set of multiview 2D images of 3D protein structures generated by Jmol, which is a 3D molecular graphics program.
Nanni L., Pasquali F., Brahnam S., Lumini A., Axenopoulos A.   +4 more
openaire   +1 more source

A survey of accelerator architectures for 3D convolution neural networks

Journal of Systems Architecture, 2021
Abstract 3D convolution neural networks (CNNs) have shown excellent predictive performance on tasks such as action recognition from videos. Since 3D CNNs have unique characteristics and extremely high compute/memory-overheads, executing them on accelerators designed for 2D CNNs provides sub-optimal performance.
Sparsh Mittal, Vibhu
openaire   +2 more sources

Disparity Filtering with 3D Convolutional Neural Networks

2018 15th Conference on Computer and Robot Vision (CRV), 2018
Stereo matching is an ill-posed problem and hence the disparity maps generated are often inaccurate and noisy. To alleviate the problem, a number of approaches were proposed to output accurate disparity values for selected pixels only. Instead of designing another disparity optimization method for sparse disparity matching, we present a novel disparity
Wendong Mao, Minglun Gong
openaire   +2 more sources

3D Convolutional Neural Network for Action Recognition

2017
Compared with traditional machine learning methods, deep convolutional neural network has more powerful learning ability. The convolutional neural network model of the depth learning algorithm has made remarkable achievements in the field of computer vision.
Junhui Zhang, Li Chen 0011, Jing Tian 0002   +2 more
openaire   +2 more sources

Winograd Algorithm for 3D Convolution Neural Networks

2017
Three-dimensional convolution neural networks (3D CNN) have achieved great success in many computer vision applications, such as video analysis, medical image classification, and human action recognition. However, the efficiency of this model suffers from great computational intensity.
Zelong Wang, Qiang Lan, Hongjun He, Chunyuan Zhang   +3 more
openaire   +2 more sources

Recognizing Chinese Texts with 3D Convolutional Neural Network

2019 IEEE International Conference on Image Processing (ICIP), 2019
In this paper, we propose a deep learning system to localize and recognize Chinese texts in scenes with signage and road marks through 3D convolutional neural network. The proposed system adopts YOLO for detecting target location and exploits 3D convolutional neural network for recognizing the contents.
Kuan-Chou Chen, Guan-Ting Lin, Che-Tsung Lin, Jiun-In Guo   +3 more
openaire   +1 more source