Feature Extraction Method of Laser Scanning Point Cloud Based on Morphological Gradient

2018 ◽  
Vol 55 (5) ◽  
pp. 051203
Author(s):  
邓博文 Deng Bowen ◽  
王召巴 Wang Zhaoba ◽  
金永 Jin Yong ◽  
陈友兴 Chen Youxing ◽  
吴其洲 Wu Qizhou ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Point Cloud ◽  
Laser Scanning ◽  
Feature Extraction Method ◽  
Morphological Gradient

scholarly journals Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256665
Author(s):  
Muhammad Rabani Mohd Romlay ◽  
Azhar Mohd Ibrahim ◽  
Siti Fauziah Toha ◽  
Philippe De Wilde ◽  
Ibrahim Venkat
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Object Recognition ◽  
Extraction Method ◽  
Point Cloud ◽  
Lidar Data ◽  
Low Density ◽  
Feature Extraction Method ◽  
Depth Measurement ◽  
Computing Capacity

Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required. To achieve reliable feature extraction from a sparse LiDAR point cloud, this paper proposes a novel Clustered Extraction and Centroid Based Clustered Extraction Method (CE-CBCE) method for feature extraction followed by a convolutional neural network (CNN) object classifier. The integration of the CE-CBCE and CNN methods enable us to utilize lightweight actuated LiDAR input and provides low computing means of classification while maintaining accurate detection. Based on genuine LiDAR data, the final result shows reliable accuracy of 97% through the method proposed.

scholarly journals Photovoltaic Power Station Electromagnetic Environment Complexity Evaluation Utilizing Logarithmic Morphological Gradient Spectrum

2021 ◽  
Vol 9 ◽  
Author(s):  
Hua-chen Xi ◽  
Bing Li ◽  
Wen-hui Mai ◽  
Xiong Xu ◽  
Ya Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Electromagnetic Environment ◽  
Power Station ◽  
Feature Extraction Method ◽  
Morphological Gradient ◽  
Photovoltaic Power ◽  
Morphological Theory ◽  
Extraction Scheme ◽  
The Impact

In this paper, a feature extraction method for evaluating the complexity of the Electromagnetic Environment (EME) of the photovoltaic power station is presented by using logarithmic morphological gradient spectrum (LMGS) based on the mathematical morphological theory. We use LMGS to evaluate electromagnetic environment signals. We also explored the impact of structure element (SE) on the MS, MGS, and LMGS. Three types of SE, mean the line SE, square SE and diamond SE, are utilized and compared for computing the LMGS. EME signals with four complexity degrees are simulated to evaluate the effectiveness of the presented method. The experimental results have shown that the feature extraction scheme proposed in this paper is a reasonable method to classify the complexity of EME.

Sign in / Sign up

Export Citation Format

Share Document