Geometric feature extraction from point clouds obtained by laser scanning

2005 ◽  
Author(s):  
Suogui Zhang ◽  
John Wootton ◽  
Alex Chisholm
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Point Clouds ◽  
Geometric Feature ◽  
Geometric Feature Extraction

Geometric Feature Extraction of Ship in High-resolution Synthetic Aperture Radar Images

2018 ◽  
Vol 47 (1) ◽  
pp. 110001
Author(s):  
熊伟 XIONG Wei ◽  
徐永力 XU Yong-li ◽  
崔亚奇 CUI Ya-qi ◽  
李岳峰 LI Yue-feng
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Geometric Feature ◽  
Radar Images ◽  
Geometric Feature Extraction ◽  
Aperture Radar

Geometric feature extraction by FTAs for finger‐based biometrics system

2016 ◽  
Vol 6 (3) ◽  
pp. 157-164 ◽  
Author(s):  
Mohd Shahrimie Mohd Asaari ◽  
Shahrel Azmin Suandi ◽  
Bakhtiar Affendi Rosdi
Keyword(s):  
Feature Extraction ◽  
Geometric Feature ◽  
Geometric Feature Extraction

Research of Face Identification Method Based on Geometric Feature Extraction and the Enlargement of Image Interpolation with Scientific Image Materials

2011 ◽  
Vol 63-64 ◽  
pp. 846-849
Author(s):  
Jian Ni ◽  
Yu Duo Li
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Interpolation Method ◽  
Image Interpolation ◽  
Geometric Feature ◽  
Face Identification ◽  
Human Face ◽  
Training Experience ◽  
Geometric Feature Extraction ◽  
Vector Weight

To achieve human face identification, this paper adopts the method of geometric feature extraction and the enlargement of image interpolation on the basis of the completion of face detection. First of all, the input digital image will be normalized to reduce the complexity of the image, and then the feature of human face will be extract. With the feature information extracted, we can construct the feature vector and assign different weights to different feature vector. Weight is interpreted as the EXP obtained after a large amount of training experience is gained. Finally, to get the similarity of picture, the bilinear interpolation method is adopted on the basis of the nearest interpolation. Thus, we will get the results of face identification according to the similarity quality. Through the development and implementation of practical programming, this paper proves the feasibility of such method.

scholarly journals Geometric Feature Extraction by a Multimarked Point Process

2010 ◽  
Vol 32 (9) ◽  
pp. 1597-1609 ◽  
Author(s):  
Florent Lafarge ◽  
Georgy Gimel'farb ◽  
Xavier Descombes
Keyword(s):  
Feature Extraction ◽  
Point Process ◽  
Geometric Feature ◽  
Geometric Feature Extraction

scholarly journals DGANet: A Dilated Graph Attention-Based Network for Local Feature Extraction on 3D Point Clouds

2021 ◽  
Vol 13 (17) ◽  
pp. 3484
Author(s):  
Jie Wan ◽  
Zhong Xie ◽  
Yongyang Xu ◽  
Ziyin Zeng ◽  
Ding Yuan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Long Range ◽  
Point Clouds ◽  
Feature Representation ◽  
Learning Ability ◽  
Geometric Feature ◽  
Cloud Data ◽  
Fine Grained ◽  
3D Point Clouds ◽  
Benchmark Datasets

Feature extraction on point clouds is an essential task when analyzing and processing point clouds of 3D scenes. However, there still remains a challenge to adequately exploit local fine-grained features on point cloud data due to its irregular and unordered structure in a 3D space. To alleviate this problem, a Dilated Graph Attention-based Network (DGANet) with a certain feature for learning ability is proposed. Specifically, we first build a local dilated graph-like region for each input point to establish the long-range spatial correlation towards its corresponding neighbors, which allows the proposed network to access a wider range of geometric information of local points with their long-range dependencies. Moreover, by integrating the dilated graph attention module (DGAM) implemented by a novel offset–attention mechanism, the proposed network promises to highlight the differing ability of each edge of the constructed local graph to uniquely learn the discrepancy feature of geometric attributes between the connected point pairs. Finally, all the learned edge attention features are further aggregated, allowing the most significant geometric feature representation of local regions by the graph–attention pooling to fully extract local detailed features for each point. The validation experiments using two challenging benchmark datasets demonstrate the effectiveness and powerful generation ability of our proposed DGANet in both 3D object classification and segmentation tasks.

scholarly journals PROGRESS ON ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1709-1713
Author(s):  
C. Wang ◽  
Y. Dai ◽  
N. El-Sheimy ◽  
C. Wen ◽  
G. Retscher ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Low Cost ◽  
Point Clouds ◽  
Indoor Positioning ◽  
Benchmark Dataset ◽  
Scanning System ◽  
Indoor Environments ◽  
Building Management ◽  
Common Framework

<p><strong>Abstract.</strong> This paper presents the design of the benchmark dataset on multisensory indoor mapping and position (MIMAP) which is sponsored by ISPRS scientific initiatives. The benchmark dataset including point clouds captured by indoor mobile laser scanning system (IMLS) in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) SLAM-based indoor point cloud generation; (2) automated BIM feature extraction from point clouds, with an emphasis on the elements, such as floors, walls, ceilings, doors, windows, stairs, lamps, switches, air outlets, that are involved in building management and navigation tasks ; and (3) low-cost multisensory indoor positioning, focusing on the smartphone platform solution. MIMAP provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone indoor positioning methods.</p>

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