Less Is More but More Is Different: Distinction Between High Resolution and Low Resolution Psychobiography

2019 ◽  
pp. 195-207
Author(s):  
Ágnes Bálint
Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour

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.


Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke

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.


2006 ◽  
Vol 2 (14) ◽  
pp. 169-194
Author(s):  
Ana I. Gómez de Castro ◽  
Martin A. Barstow

AbstractThe scientific program is presented as well a the abstracts of the contributions. An extended account is published in “The Ultraviolet Universe: stars from birth to death” (Ed. Gómez de Castro) published by the Editorial Complutense de Madrid (UCM), that can be accessed by electronic format through the website of the Network for UV Astronomy (www.ucm.es/info/nuva).There are five telescopes currently in orbit that have a UV capability of some description. At the moment, only FUSE provides any medium- to high-resolution spectroscopic capability. GALEX, the XMM UV-Optical Telescope (UVOT) and the Swift. UVOT mainly delivers broad-band imaging, but with some low-resolution spectroscopy using grisms. The primary UV spectroscopic capability of HST was lost when the Space Telescope Imaging Spectrograph failed in 2004, but UV imaging is still available with the HST-WFPC2 and HST-ACS instruments.With the expected limited lifetime of sl FUSE, UV spectroscopy will be effectively unavailable in the short-term future. Even if a servicing mission of HST does go ahead, to install COS and repair STIS, the availability of high-resolution spectroscopy well into the next decade will not have been addressed. Therefore, it is important to develop new missions to complement and follow on from the legacy of FUSE and HST, as well as the smaller imaging/low resolution spectroscopy facilities. This contribution presents an outline of the UV projects, some of which are already approved for flight, while others are still at the proposal/study stage of their development.This contribution outlines the main results from Joint Discussion 04 held during the IAU General Assembly in Prague, August 2006, concerning the rationale behind the needs of the astronomical community, in particular the stellar astrophysics community, for new UV instrumentation. Recent results from UV observations were presented and future science goals were laid out. These goals will lay the framework for future mission planning.


Author(s):  
Stephanie C. Herring ◽  
Nikolaos Christidis ◽  
Andrew Hoell ◽  
James P. Kossin ◽  
Carl J. Schreck ◽  
...  

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.


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

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.


2021 ◽  
pp. 1-15
Author(s):  
Yongjie Chu ◽  
Touqeer Ahmad ◽  
Lindu Zhao

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.


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