scholarly journals Increasing the probability of recognizing a target using a two-dimensional image from the sensor of the visible range of the onboard complex of information support for search and rescue operations in the Arctic

2020 ◽  
Vol 30 (3) ◽  
pp. 21-27
Author(s):  
A. S. Zhdanov ◽  
S. A. Matveev ◽  
Yu. V. Petrov ◽  
S. A. Rudyka ◽  
S. Yu. Strakhov ◽  
...  
Keyword(s):  
Object Recognition ◽  
Target Recognition ◽  
Recognition Performance ◽  
Search And Rescue ◽  
Information Support ◽  
The Arctic ◽  
Visible Range ◽  
Image Brightness ◽  
Television Camera ◽  
Constant Color

The article addresses the task of improving target recognition in onboard information support system for search and rescue operations in the Arctic region. One of the tasks performed by the complex is recognition of objects in twodimensional camera images, which suffer from the loss of the image brightness, being formed by constant brightness principle, with its direct impact on the probability of target recognition. To preserve the brightness of the image, the authors propose to process the primary signals of the camera according to the principle of constant color brightness. The proposed processing can increase the probability of correct target recognition. The paper analyzes the principles of encoding the primary signals of the television camera. For the object recognition problem, the cascades were trained based on the cascade classifier using the principles of constant brightness and constant color brightness. The output of the trained cascades has confirmed that the processing of the primary signals of a television camera based on the principle of constant brightness improves target recognition and therefore will increase the object recognition performance of the complex under development.

scholarly journals The Northwest Passage in the Arctic: A Brief Assessment of the Relevant Marine Transportation System and Current Availability of Search and Rescue Services

Logistics ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 23
Author(s):  
Rebecca Sheehan ◽  
Dimitrios Dalaklis ◽  
Anastasia Christodoulou ◽  
Megan Drewniak ◽  
Peter Raneri ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
Transportation System ◽  
Search And Rescue ◽  
Maritime Transportation ◽  
The Arctic ◽  
Marine Transportation ◽  
Northwest Passage ◽  
Traffic Routing ◽  
Brief Assessment

The analysis in hand provides a brief assessment of the United States’ and Canada’s marine transportation system and relevant search and rescue (SAR) support in relation to the Northwest Passage, with the purpose of examining to what extent these countries’ relevant infrastructure resources are able to meet the expected growth of shipping operations and business activities in the Arctic. Through an extensive literature review, this assessment will specifically describe the most important influences upon the maritime transportation system, with the issue of certain geographical details and the capabilities of existing ports standing out. Additionally, vessel activity trends and vessel traffic routing measure initiatives will be examined. Furthermore, the SAR infrastructure details and means to render assistance to people in distress along the Northwest Passage will be discussed. The reality remains that port characteristics are limited and vessel traffic routing measure initiatives and upgrades to SAR assets are commendable but slow-paced. It is true that both the United States and Canada are taking proper measures to build up infrastructure needs, but they both may run out of time to put adequate infrastructure in place to deal effectively with the changing environment.

scholarly journals Autonomous ships for container shipping in the Arctic routes

2021 ◽  
Author(s):  
Ziaul Haque Munim ◽  
Rana Saha ◽  
Halvor Schøyen ◽  
Adolf K. Y. Ng ◽  
Theo E. Notteboom
Keyword(s):  
Environmental Protection ◽  
Search And Rescue ◽  
The Arctic ◽  
Multi Criteria Decision Making ◽  
Container Shipping ◽  
Foreseeable Future ◽  
Arctic Shipping ◽  
Control Centre ◽  
Autonomous Ship ◽  
Best Worst Method

AbstractThis study investigates the competitiveness of various autonomous ship categories for container shipping in the Arctic route. We propose a multi-criteria decision-making (MCDM) framework using four ship categories as alternatives and eight criteria for competitiveness evaluation. We analyse collected data using the Best–Worst Method (BWM), one of the recently developed MCDM methods. The findings reveal that operating expenses, navigation aspects, and environmental protection are the three most important criteria for deploying autonomous ships in the Arctic route. Among the three investigated autonomous ships alternatives, the semi-autonomous ship operated from a shore control centre (SCC) is prioritized for Arctic shipping in the foreseeable future, when benchmarked against the conventional ship. The SCC-controlled semi-autonomous ship alternative is competitive in the majority of the considered criteria including operating expenses, capital expenses, navigation, ship-shore and ship–ship communication, search and rescue, and environmental protection.

Face Perception in Face Transplant Patients

2019 ◽  
Vol 35 (05) ◽  
pp. 525-533
Author(s):  
Evrim Gülbetekin ◽  
Seda Bayraktar ◽  
Özlenen Özkan ◽  
Hilmi Uysal ◽  
Ömer Özkan
Keyword(s):  
Face Recognition ◽  
Object Recognition ◽  
Face Processing ◽  
Recognition Performance ◽  
Discrimination Performance ◽  
Object Discrimination ◽  
Face Transplantation ◽  
Transplant Patients ◽  
Facial Surgery ◽  
The Face

AbstractThe authors tested face discrimination, face recognition, object discrimination, and object recognition in two face transplantation patients (FTPs) who had facial injury since infancy, a patient who had a facial surgery due to a recent wound, and two control subjects. In Experiment 1, the authors showed them original faces and morphed forms of those faces and asked them to rate the similarity between the two. In Experiment 2, they showed old, new, and implicit faces and asked whether they recognized them or not. In Experiment 3, they showed them original objects and morphed forms of those objects and asked them to rate the similarity between the two. In Experiment 4, they showed old, new, and implicit objects and asked whether they recognized them or not. Object discrimination and object recognition performance did not differ between the FTPs and the controls. However, the face discrimination performance of FTP2 and face recognition performance of the FTP1 were poorer than that of the controls were. Therefore, the authors concluded that the structure of the face might affect face processing.

scholarly journals Spectral albedo of seasonal snow during intensive melt period at Sodankylä, beyond the Arctic Circle

2013 ◽  
Vol 13 (7) ◽  
pp. 3793-3810 ◽  
Author(s):  
O. Meinander ◽  
S. Kazadzis ◽  
A. Arola ◽  
A. Riihelä ◽  
P. Räisänen ◽  
...  
Keyword(s):  
Spectral Irradiance ◽  
The Arctic ◽  
Visible Range ◽  
Air Masses ◽  
Clear Sky ◽  
Effective Grain Size ◽  
European Arctic ◽  
Small Absorption ◽  
High Concentrations ◽  
Double Monochromator

Abstract. We have measured spectral albedo, as well as ancillary parameters, of seasonal European Arctic snow at Sodankylä, Finland (67°22' N, 26°39' E). The springtime intensive melt period was observed during the Snow Reflectance Transition Experiment (SNORTEX) in April 2009. The upwelling and downwelling spectral irradiance, measured at 290–550 nm with a double monochromator spectroradiometer, revealed albedo values of ~0.5–0.7 for the ultraviolet and visible range, both under clear sky and variable cloudiness. During the most intensive snowmelt period of four days, albedo decreased from 0.65 to 0.45 at 330 nm, and from 0.72 to 0.53 at 450 nm. In the literature, the UV and VIS albedo for clean snow are ~0.97–0.99, consistent with the extremely small absorption coefficient of ice in this spectral region. Our low albedo values were supported by two independent simultaneous broadband albedo measurements, and simulated albedo data. We explain the low albedo values to be due to (i) large snow grain sizes up to ~3 mm in diameter; (ii) meltwater surrounding the grains and increasing the effective grain size; (iii) absorption caused by impurities in the snow, with concentration of elemental carbon (black carbon) in snow of 87 ppb, and organic carbon 2894 ppb, at the time of albedo measurements. The high concentrations of carbon, detected by the thermal–optical method, were due to air masses originating from the Kola Peninsula, Russia, where mining and refining industries are located.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals ONBOARD COMPLEX OF INFORMATION SUPPORT OF SEARCH AND RESCUE OPERATIONS IN ARCTIC

2019 ◽  
pp. 30-37 ◽  
Author(s):  
S. А. Matveev ◽  
S. А. Rudyka ◽  
Yu. V. Petrov ◽  
А. S. Zhdanov
Keyword(s):  
Search And Rescue ◽  
Information Support

The article discusses a variant of the all‑season and all‑weather complex for providing search and rescue operations conducted with the help of aircraft in the Arctic. The creation of such a complex is associated with the active development of the Arctic zones of the Russian Federation and the difficulties that are characteristic of these regions of the country: extreme climatic conditions, underdeveloped ground and aviation infrastructure, etc. A promising direction is the development of hardware‑software complexes of «improved vision», including various sensors: television and infrared cameras, radar stations, lidars. The article defines the main functional tasks for such complexes, analyzes foreign and domestic analogues of onboard complexes of information support for search and rescue operations. The absence of the complexes providing simultaneous solution of all the noted functional tasks was revealed, in connection with which a new onboard complex was proposed. In this work, the components of the proposed complex  are  considered:  a  laser‑television  module,  radar  stations  for  forward  vision  and  sensing  of  the  underlying  surface; methods of visualization of complex multi‑spectral images of the environment, as well as the formation of messages and signals about the danger are presented.

scholarly journals A SAR Target Recognition Method via Combination of Multilevel Deep Features

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Junhua Wang ◽  
Yuan Jiang
Keyword(s):  
Target Recognition ◽  
Recognition Performance ◽  
Superior Performance ◽  
Recognition Method ◽  
Sar Images ◽  
Noise Interference ◽  
Feature Sets ◽  
Weighted Fusion ◽  
Standard Operating Condition ◽  
Sar Target Recognition

For the problem of synthetic aperture radar (SAR) image target recognition, a method via combination of multilevel deep features is proposed. The residual network (ResNet) is used to learn the multilevel deep features of SAR images. Based on the similarity measure, the multilevel deep features are clustered and several feature sets are obtained. Then, each feature set is characterized and classified by the joint sparse representation (JSR), and the corresponding output result is obtained. Finally, the results of different feature sets are combined using the weighted fusion to obtain the target recognition results. The proposed method in this paper can effectively combine the advantages of ResNet and JSR in feature extraction and classification and improve the overall recognition performance. Experiments and analysis are carried out on the MSTAR dataset with rich samples. The results show that the proposed method can achieve superior performance for 10 types of target samples under the standard operating condition (SOC), noise interference, and occlusion conditions, which verifies its effectiveness.

Image Quality and Target Recognition

1967 ◽  
Vol 9 (1) ◽  
pp. 5-32 ◽  
Author(s):  
Corwin A. Bennett ◽  
Samuel H. Winterstein ◽  
Robert E. Kent
Keyword(s):  
Grain Size ◽  
Image Quality ◽  
Modulation Transfer Function ◽  
Target Recognition ◽  
Recognition Performance ◽  
Aerial Photographs ◽  
Modulation Transfer ◽  
Task Definition ◽  
Subject Selection ◽  
Target Characteristics

The terminology and literature in the area of image quality and target recognition are reviewed. An experiment in which subjects recognized strategic and tactical targets in aerial photographs with controlled image degradations is described. Some findings are: Recognition performance is only moderate for representative conditions. There are wide differences among target types in the recognizability. Knowledge of a target's presence (briefing) greatly aids recognition. Better resolution means better performance. Enlarging the image such that a line of resolution subtends more than three minutes of arc hinders recognition. Grain size should be kept below 20 seconds of arc. It is suggested that the eventual application of the modulation transfer function approach to measurement of image quality and target characteristics will enable a quantitative subsuming of various quality-size relationships. More attention needs to be paid in recognition research to suitable task definition, target description, and subject selection.

Target recognition performance following whole-views, part-views, and both-vlews training

2017 ◽  
pp. 135-141
Author(s):  
Sehchang Hah ◽  
Deborah A. Reisweber ◽  
Jose A. Picart ◽  
Harry Zwick
Keyword(s):  
Target Recognition ◽  
Recognition Performance
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