Research on the Extraction Method of Painting Style Features Based on Convolutional Neural Network

2022 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Hua Jiang ◽  
Ting Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Extraction Method

scholarly journals Extraction of Guardrails from MMS Data Using Convolutional Neural Network

2021 ◽  
Vol 15 (3) ◽  
pp. 258-267
Author(s):  
Hiroki Matsumoto ◽  
◽  
Yuma Mori ◽  
Hiroshi Masuda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Asset Management ◽  
Extraction Method ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Method Point ◽  
Mobile Mapping Systems ◽  
Capture Point ◽  
Mapping Systems

Mobile mapping systems can capture point clouds and digital images of roadside objects. Such data are useful for maintenance, asset management, and 3D map creation. In this paper, we discuss methods for extracting guardrails that separate roadways and walkways. Since there are various shape patterns for guardrails in Japan, flexible methods are required for extracting them. We propose a new extraction method based on point processing and a convolutional neural network (CNN). In our method, point clouds and images are segmented into small fragments, and their features are extracted using CNNs for images and point clouds. Then, features from images and point clouds are combined and investigated using whether they are guardrails or not. Based on our experiments, our method could extract guardrails from point clouds with a high success rate.

scholarly journals A new road extraction method using Sentinel-1 SAR images based on the deep fully convolutional neural network

2019 ◽  
Vol 52 (1) ◽  
pp. 572-582 ◽  
Author(s):  
Qianqian Zhang ◽  
Qingling Kong ◽  
Chao Zhang ◽  
Shucheng You ◽  
Hai Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Extraction Method ◽  
Road Extraction ◽  
Sar Images

scholarly journals Octave-band Filtering for Convolutional Neural Network-based Diagnostics for Rotating Machinery

2020 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Namkyoung Lee ◽  
Michael Azarian ◽  
Michael Pecht
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Convolutional Neural Network ◽  
Extraction Method ◽  
Extraction Methods ◽  
Rotating Machinery ◽  
Octave Band ◽  
Feature Extraction Method ◽  
Model Research

The performance of a machine learning model depends on the quality of the features used as input to the model. Research into feature extraction methods for convolutional neural network (CNN)-based diagnostics for rotating machinery remains in a developmental stage. In general, the input to CNN-based diagnostics consists of a spectrogram without significant pre-processing. This paper introduces octave-band filtering as a feature extraction method for preprocessing a spectrogram prior to use with CNN. This method is an adaptation of a feature extraction method originally developed for speech recognition. The method developed for diagnosis of machinery faults differs from filtering methods applied to speech recognition in its use of octave bands, to which weighting has been applied that is optimal for machinery diagnosis. Through a case study, the effectiveness of octave-band filtering is demonstrated. The method not only improves the accuracy of the CNN-based diagnostics but also reduces the size of the CNN.

Dense Disparity Map Extraction Method Based on Improved Convolutional Neural Network

2018 ◽  
Vol 55 (12) ◽  
pp. 121004
Author(s):  
黄东振 Huang Dongzhen ◽  
赵沁 Zhao Qin ◽  
刘华巍 Liu Huawei ◽  
李宝清 Li Baoqing ◽  
袁晓兵 Yuan Xiaobing
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Extraction Method ◽  
Disparity Map
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