scholarly journals City-Scale Distance Sensing via Bispectral Light Extinction in Bad Weather

2020 ◽  
Vol 12 (9) ◽  
pp. 1401
Author(s):  
Dong Zhao ◽  
Yuta Asano ◽  
Lin Gu ◽  
Imari Sato ◽  
Huixin Zhou
Keyword(s):  
Extinction Coefficient ◽  
Near Infrared ◽  
Imaging System ◽  
Weather Conditions ◽  
Light Extinction ◽  
Selection Strategy ◽  
Infrared Wavelengths ◽  
The Difference ◽  
Bad Weather ◽  
Almost All

In this paper, we propose a novel city-scale distance sensing algorithm based on atmosphere optics. The suspended particles, especially in bad weather, would attenuate the light at almost all wavelengths. Observing this fact and starting from the light scattering mechanism, we derive a bispectral distance sensing algorithm by leveraging the difference of extinction coefficient between two specifically selected near infrared wavelengths. The extinction coefficient of the atmosphere is related to both wavelength and meteorological conditions, also known as visibility, such as the fog and haze day. To account for different bad weather conditions, we explicitly introduce visibility into our algorithm by incorporating it into the calculation of extinction coefficient, making our algorithm simple yet effective. To capture the data, we build a bispectral imaging system that is able to take a pair of images with a monochrome camera and two narrow band-pass filters. We also present a wavelength selection strategy that allows us to accurately sense distance regardless of material reflectance and texture. Specifically, this strategy determines two distinct near infrared wavelengths by maximising the extinction coefficient difference while minimizing the influence of building’s reflectance variance. The experiments empirically validate our model and its practical performance on the distance sensing for the city-scale buildings.

United Front: Partisans of the Leningrad Region and their connections with the Central Asian Soviet Republics

2021 ◽  
Vol 2021 (02) ◽  
pp. 214-225
Author(s):  
Sergey Kulik ◽  
Аnatoliy Kashevarov ◽  
Zamira Ishankhodjaeva
Keyword(s):  
World War Ii ◽  
Weather Conditions ◽  
Leningrad Region ◽  
World War ◽  
North West ◽  
The North ◽  
Occupied Territory ◽  
Central Asian ◽  
The Difference ◽  
Almost All

During World War II, representatives of almost all the Soviet Republics fought in partisan detachments in the occupied territory of the Leningrad Region. Among them were many representatives of the Central Asian republics: Kazakhstan, Kyrgyzstan and Uzbekistan. Many Leningrad citizens, including relatives of partisans, had been evacuated to Central Asia by that time. However, representatives of Asian workers’ collectives came to meet with the partisans. The huge distance, the difference in cultures and even completely different weather conditions did not become an obstacle to those patriots-Turkestanis who joined the resistance forces in the North-West of Russia.

scholarly journals Variations of the snow physical parameters and their effects on albedo in Sapporo, Japan

2007 ◽  
Vol 46 ◽  
pp. 375-381 ◽  
Author(s):  
Teruo Aoki ◽  
Hiroki Motoyoshi ◽  
Yuji Kodama ◽  
Teppei J. Yasunari ◽  
Konosuke Sugiura
Keyword(s):  
Grain Size ◽  
Near Infrared ◽  
Mineral Dust ◽  
Radiation Budget ◽  
Radiative Transfer Model ◽  
Physical Parameters ◽  
Transfer Model ◽  
Infrared Wavelengths ◽  
The Difference ◽  
Impurity Models

AbstractContinuous measurements of the radiation budget and meteorological components, along with frequent snow-pit work, were performed in Sapporo, Hokkaido, Japan, during two winters from 2003 to 2005. The measured relationships between broadband albedos and the mass concentration of snow impurities were compared with theoretically predicted relationships calculated using a radiative transfer model for the atmosphere–snow system in which different types (in light absorption) of impurity models based on mineral dust and soot were assumed. The result suggests that the snow in Sapporo was contaminated not only with mineral dust but also with more absorptive soot. A comparison of the measured relationships between broadband albedos and snow grain size for two different layers with the theoretically predicted relationships revealed that the visible albedo contains information about the snow grain size in deeper snow layers (10 cm), and the near-infrared albedo contains only surface information. This is due to the difference in penetration depth of solar radiation into snow between the visible and the near-infrared wavelengths.

scholarly journals Real-Time Vision through Haze Based on Polarization Imaging

2019 ◽  
Vol 9 (1) ◽  
pp. 142 ◽  
Author(s):  
Xinhua Wang ◽  
Jihong Ouyang ◽  
Yi Wei ◽  
Fei Liu ◽  
Guang Zhang
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Detection Efficiency ◽  
Detection System ◽  
Weather Conditions ◽  
Pollutant Emission ◽  
Optical Monitoring ◽  
Stokes Vectors ◽  
Bad Weather ◽  
Pollutant Discharge

Various gases and aerosols in bad weather conditions can cause severe image degradation, which will seriously affect the detection efficiency of optical monitoring stations for high pollutant discharge systems. Thus, penetrating various gases and aerosols to sense and detect the discharge of pollutants plays an important role in the pollutant emission detection system. Against this backdrop, we recommend a real-time optical monitoring system based on the Stokes vectors through analyzing the scattering characteristics and polarization characteristics of both gases and aerosols in the atmosphere. This system is immune to the effects of various gases and aerosols on the target to be detected and achieves the purpose of real-time sensing and detection of high pollutant discharge systems under bad weather conditions. The imaging system is composed of four polarizers with different polarization directions integrated into independent cameras aligned parallel to the optical axis in order to acquire the Stokes vectors from various polarized azimuth images. Our results show that this approach achieves high-contrast and high-definition images in real time without the loss of spatial resolution in comparison with the performance of conventional imaging techniques.

scholarly journals Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

2017 ◽  
Vol 71 (1) ◽  
pp. 241-256 ◽  
Author(s):  
Riccardo Polvara ◽  
Sanjay Sharma ◽  
Jian Wan ◽  
Andrew Manning ◽  
Robert Sutton
Keyword(s):  
Autonomous Navigation ◽  
Optimal Solution ◽  
Weather Conditions ◽  
Research Area ◽  
Unmanned Surface Vehicles ◽  
Planning Methods ◽  
The Difference ◽  
Optimisation Methods ◽  
Intelligent Methods ◽  
Almost All

The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.

scholarly journals An easy to construct sub-micron resolution imaging system

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Lakhi Sharma ◽  
A. Roy ◽  
S. Panja ◽  
S. De
Keyword(s):  
Near Infrared ◽  
Imaging System ◽  
Spherical Aberration ◽  
Numerical Aperture ◽  
Field Of View ◽  
Depth Of Field ◽  
Large Ensemble ◽  
Resolution Imaging ◽  
Infrared Wavelengths ◽  
Working Distance

AbstractWe report an easy to construct imaging system that can resolve particles separated by $$\ge $$ ≥ 0.68 $$\upmu $$ μ m with minimum aberrations. Its first photon collecting lens is placed at a distance of 31.6 mm giving wide optical access. The microscope has a Numerical Aperture (NA) of 0.33, which is able to collect signal over 0.36 sr. The diffraction limited objective and magnifier recollects 77% photons into the central disc of the image with a transverse spherical aberration of 0.05 mm and magnification upto 238. The system has a depth of field of 142 $$\upmu $$ μ m and a field of view of 56 $$\upmu $$ μ m which images a large ensemble of atoms. The imaging system gives a diffraction limited performance over visible to near-infrared wavelengths on optimization of the working distance and the distance between the objective and magnifier.

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

Sign in / Sign up

Export Citation Format

Share Document