scholarly journals Explaining Extreme Events of 2014 from a Climate Perspective

2015 ◽  
Vol 96 (12) ◽  
pp. S1-S172 ◽  
Author(s):  
Stephanie C. Herring ◽  
Martin P. Hoerling ◽  
James P. Kossin ◽  
Thomas C. Peterson ◽  
Peter A. Stott
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Low Resolution

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2014 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

scholarly journals Explaining Extreme Events of 2016 from a Climate Perspective

2018 ◽  
Vol 99 (1) ◽  
pp. S1-S157 ◽  
Author(s):  
Stephanie C. Herring ◽  
Nikolaos Christidis ◽  
Andrew Hoell ◽  
James P. Kossin ◽  
Carl J. Schreck ◽  
...  
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Low Resolution

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2016 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

scholarly journals Explaining Extreme Events of 2019 from a Climate Perspective

2021 ◽  
Vol 102 (1) ◽  
pp. S1-S116
Author(s):  
Stephanie C. Herring ◽  
Nikolaos Christidis ◽  
Andrew Hoell ◽  
Martin P. Hoerling ◽  
Peter A. Stott
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Low Resolution ◽  
Supplementary Material

AbstractEditors note: For easy download the posted pdf of the Explaining Extreme Events of 2019 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

scholarly journals Explaining Extreme Events of 2017 from a Climate Perspective

2019 ◽  
Vol 100 (1) ◽  
pp. S1-S117 ◽  
Author(s):  
Stephanie C. Herring ◽  
Nikolaos Christidis ◽  
Andrew Hoell ◽  
Martin P. Hoerling ◽  
Peter A. Stott
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Low Resolution ◽  
Supplementary Material

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2016 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

scholarly journals Explaining Extreme Events of 2018 from a Climate Perspective

2020 ◽  
Vol 101 (1) ◽  
pp. S1-S140 ◽  
Author(s):  
Stephanie C. Herring ◽  
Nikolaos Christidis ◽  
Andrew Hoell ◽  
Martin P. Hoerling ◽  
Peter A. Stott
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Low Resolution

Abstract Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2018 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

scholarly journals Explaining Extreme Events of 2015 from a Climate Perspective

2016 ◽  
Vol 97 (12) ◽  
pp. S1-S145 ◽  
Author(s):  
Stephanie C. Herring ◽  
Andrew Hoell ◽  
Martin P. Hoerling ◽  
James P. Kossin ◽  
Carl J. Schreck ◽  
...  
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Low Resolution

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2015 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

Extreme Events in High Resolution CMCC Regional and Global Climate Models

2011 ◽  
Author(s):  
Enrico Scoccimarro ◽  
Silvio Gualdi ◽  
Antonella Sanna ◽  
Edoardo Bucchignani ◽  
Myriam Montesarchio
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

scholarly journals Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

2021 ◽  
Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke
Keyword(s):  
High Resolution ◽  
Digital Image Correlation ◽  
Spatial Resolution ◽  
Digital Image ◽  
Super Resolution ◽  
Image Correlation ◽  
Low Resolution ◽  
Strain Measurements ◽  
Resolution Image ◽  
High Resolution Image

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

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