scholarly journals A multi-scale UAV image matching method applied to large-scale landslide reconstruction

2021 ◽  
Vol 18 (3) ◽  
pp. 2274-2287
Author(s):  
Ren Chaofeng ◽  
◽  
Xiaodong Zhi ◽  
Yuchi Pu ◽  
Fuqiang Zhang ◽  
...  
Keyword(s):  
Image Matching ◽  
Large Scale ◽  
Matching Method ◽  
Multi Scale ◽  
Uav Image

Method of Image Matching for Seabed Sonar Images Based on Regions of Interest

2014 ◽  
Vol 532 ◽  
pp. 126-129
Author(s):  
Zhi Gang Zhang ◽  
Hong Yu Bian ◽  
Hui Xu ◽  
Zi Qi Song
Keyword(s):  
Target Detection ◽  
Image Matching ◽  
Large Scale ◽  
Regions Of Interest ◽  
Matching Method ◽  
Aspect Angle ◽  
Sonar Images ◽  
The Stability ◽  
Imaging Sonar ◽  
The Impact

One of the most effective instruments for target detection in turbid waters is imaging sonar. However, the aspect angle of imaging sonar is usually small and that is a sacrifice for high detection precision. To make imaging sonar practical in large scale target detection with wide aspect angle, investigating image matching methods for continuous sonar frames is of great importance. A novel image matching method using local features of SIFT is described in this paper, which mainly focuses on the problem of weak echo signals and the following sonar images mismatch. The correspondence between objects and cast shadow regions is employed to extract regions of interest. Besides, status parameters of underwater vehicle are used to approximate the image transformation. Image segmentation methods are involved to decrease the size of the feature extracting regions and reduce the impact of non-target seabed areas, which improves the stability of this sonar image matching method significantly.

Multiscale Convolutional Descriptor Aggregation for Visual Place Recognition

2020 ◽  
Vol 2020 (10) ◽  
pp. 313-1-313-7
Author(s):  
Raffaele Imbriaco ◽  
Egor Bondarev ◽  
Peter H.N. de With
Keyword(s):  
Image Matching ◽  
Large Scale ◽  
Place Recognition ◽  
Aggregation Method ◽  
Local Descriptors ◽  
Multi Scale ◽  
Vector Construction ◽  
Visual Place Recognition ◽  
Different Sources ◽  
Geometric Verification

Visual place recognition using query and database images from different sources remains a challenging task in computer vision. Our method exploits global descriptors for efficient image matching and local descriptors for geometric verification. We present a novel, multi-scale aggregation method for local convolutional descriptors, using memory vector construction for efficient aggregation. The method enables to find preliminary set of image candidate matches and remove visually similar but erroneous candidates. We deploy the multi-scale aggregation for visual place recognition on 3 large-scale datasets. We obtain a Recall@10 larger than 94% for the Pittsburgh dataset, outperforming other popular convolutional descriptors used in image retrieval and place recognition. Additionally, we provide a comparison for these descriptors on a more challenging dataset containing query and database images obtained from different sources, achieving over 77% Recall@10.

scholarly journals Public Awareness: What Climate Change Scientists Should Consider

2020 ◽  
Vol 12 (20) ◽  
pp. 8369
Author(s):  
Mohammad Rahimi
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Large Scale ◽  
Public Awareness ◽  
Connected Network ◽  
Multi Scale ◽  
Valuable Addition ◽  
Scientific Approach ◽  
Design Solutions ◽  
Mitigate Climate Change

In this Opinion, the importance of public awareness to design solutions to mitigate climate change issues is highlighted. A large-scale acknowledgment of the climate change consequences has great potential to build social momentum. Momentum, in turn, builds motivation and demand, which can be leveraged to develop a multi-scale strategy to tackle the issue. The pursuit of public awareness is a valuable addition to the scientific approach to addressing climate change issues. The Opinion is concluded by providing strategies on how to effectively raise public awareness on climate change-related topics through an integrated, well-connected network of mavens (e.g., scientists) and connectors (e.g., social media influencers).

scholarly journals Multi-Scale Ionospheric Anomalies Monitoring and Spatio-Temporal Analysis during Intense Storm

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 215
Author(s):  
Na Cheng ◽  
Shuli Song ◽  
Wei Li
Keyword(s):  
Magnetic Storm ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Ionospheric Disturbance ◽  
Ionospheric Scintillation ◽  
Small Scale ◽  
Electron Content ◽  
Total Electron ◽  
Multi Scale ◽  
Intense Storm

The ionosphere is a significant component of the geospace environment. Storm-induced ionospheric anomalies severely affect the performance of Global Navigation Satellite System (GNSS) Positioning, Navigation, and Timing (PNT) and human space activities, e.g., the Earth observation, deep space exploration, and space weather monitoring and prediction. In this study, we present and discuss the multi-scale ionospheric anomalies monitoring over China using the GNSS observations from the Crustal Movement Observation Network of China (CMONOC) during the 2015 St. Patrick’s Day storm. Total Electron Content (TEC), Ionospheric Electron Density (IED), and the ionospheric disturbance index are used to monitor the storm-induced ionospheric anomalies. This study finally reveals the occurrence of the large-scale ionospheric storms and small-scale ionospheric scintillation during the storm. The results show that this magnetic storm was accompanied by a positive phase and a negative phase ionospheric storm. At the beginning of the main phase of the magnetic storm, both TEC and IED were significantly enhanced. There was long-duration depletion in the topside ionospheric TEC during the recovery phase of the storm. This study also reveals the response and variations in regional ionosphere scintillation. The Rate of the TEC Index (ROTI) was exploited to investigate the ionospheric scintillation and compared with the temporal dynamics of vertical TEC. The analysis of the ROTI proved these storm-induced TEC depletions, which suppressed the occurrence of the ionospheric scintillation. To improve the spatial resolution for ionospheric anomalies monitoring, the regional Three-Dimensional (3D) ionospheric model is reconstructed by the Computerized Ionospheric Tomography (CIT) technique. The spatial-temporal dynamics of ionospheric anomalies during the severe geomagnetic storm was reflected in detail. The IED varied with latitude and altitude dramatically; the maximum IED decreased, and the area where IEDs were maximum moved southward.

scholarly journals Multi-scale modelling of a large scale shell-and-tube latent heat storage system for direct steam generation power plants

2020 ◽  
Author(s):  
Clément Beust ◽  
Erwin Franquet ◽  
Jean-Pierre Bédécarrats ◽  
Pierre Garcia ◽  
Jérôme Pouvreau ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Storage System ◽  
Steam Generation ◽  
Latent Heat Storage ◽  
Multi Scale ◽  
Direct Steam ◽  
Scale Modelling ◽  
Shell And Tube ◽  
Generation Power

scholarly journals Hybrid Graph Neural Networks for Crowd Counting

2020 ◽  
Vol 34 (07) ◽  
pp. 11693-11700 ◽  
Author(s):  
Ao Luo ◽  
Fan Yang ◽  
Xin Li ◽  
Dong Nie ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Network Architecture ◽  
Message Passing ◽  
Large Scale ◽  
State Of The Art ◽  
Density Variation ◽  
Feature Maps ◽  
Crowd Counting ◽  
Multi Scale ◽  
Crowd Density ◽  
Graph Neural Networks

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges: (i) multi-scale relations capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can capture and distill richer relations between nodes to obtain more powerful representations, providing robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art algorithms by a large margin.

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