Predict the Performance of Visual Surveillance by EEG Spectral Band Advantage Activity: Modeling-Based Occipital Alpha Waves Advantage Activity

2017 ◽  
pp. 215-221 ◽  
Author(s):  
Deqian Zhang ◽  
Wenjiao Cheng ◽  
Hezhi Yang
Keyword(s):  
Spectral Band ◽  
Visual Surveillance ◽  
Activity Modeling ◽  
Alpha Waves

Great Expectations: Perceptual Challenges of Visual Surveillance in Lifeguarding

2012 ◽  
Author(s):  
Lyndsey K. Lanagan-Leitzel ◽  
Emily Skow ◽  
Cathleen M. Moore
Keyword(s):  
Visual Surveillance ◽  
Great Expectations

Variational Method for Estimating the Dipole Moment in the Second Excited State of Fluorescein Molecule from its Electronic UV Absorption Spectra

2019 ◽  
Vol 70 (10) ◽  
pp. 3538-3544
Author(s):  
Alina Costina Luca ◽  
Ana Cezarina Morosanu ◽  
Irina Macovei ◽  
Dan Gheorghe Dimitriu ◽  
Dana Ortansa Dorohoi ◽  
...  
Keyword(s):  
Dipole Moment ◽  
Excited State ◽  
Variational Method ◽  
Electronic State ◽  
Excited Electronic State ◽  
Spectral Band ◽  
Uv Absorption ◽  
Optical Parameters ◽  
Electrical Permittivity ◽  
Fluorescein Molecule

Electro-optical parameters of fluorescein molecule in the second excited electronic state and information on the interactions with solvents were obtained from a solvatochromic study. Parameters of the solvents such as the refractive index, electrical permittivity and Kamlet-Taft parameters (hydrogen bond acidity and basicity) were related with the experimentally recorded shifts of UV absorption spectral band of fluorescein dissolved in several solvents. Through a variational method, the electric dipole moment and polarizability in excited state of fluorescein molecule were estimated. The calculus requires some parameters of the fluorescein molecule in the ground electronic state, which were determined through a quantum-mechanical study.

A Fast Method for Defogging of Outdoor Visual Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Tannistha Pal
Keyword(s):  
Intelligent Vehicles ◽  
Visual Surveillance ◽  
Qualitative Assessment ◽  
Computational Time ◽  
Data Sets ◽  
Fast Method ◽  
Time Data ◽  
Image Defogging ◽  
Clear Vision ◽  
Dark Channel

Images captured in severe atmospheric catastrophe especially in fog critically degrade the quality of an image and thereby reduces the visibility of an image which in turn affects several computer vision applications like visual surveillance detection, intelligent vehicles, remote sensing, etc. Thus acquiring clear vision is the prime requirement of any image. In the last few years, many approaches have been made towards solving this problem. In this article, a comparative analysis has been made on different existing image defogging algorithms and then a technique has been proposed for image defogging based on dark channel prior strategy. Experimental results show that the proposed method shows efficient results by significantly improving the visual effects of images in foggy weather. Also computational time of the existing techniques are much higher which has been overcame in this paper by using the proposed method. Qualitative assessment evaluation is performed on both benchmark and real time data sets for determining theefficacy of the technique used. Finally, the whole work is concluded with its relative advantages and shortcomings.

Evaluation of the expediency of using thermal i magery data for decryption exogenous processes and vegetation

2018 ◽  
Vol 933 (3) ◽  
pp. 52-62
Author(s):  
V.S. Tikunov ◽  
I.A. Rylskiy ◽  
S.B. Lukatzkiy
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Data Extraction ◽  
Spectral Band ◽  
Information Provision ◽  
Lidar Data ◽  
Thermal Imagery ◽  
Scanning Lidar ◽  
Field Works ◽  
Object Features

Innovative methods of aerial surveys changed approaches to information provision of projecting dramatically in last years. Nowadays there are several methods pretending to be the most efficient for collecting geospatial data intended for projecting – airborne laser scanning (LIDAR) data, RGB aerial imagery (forming 3D pointclouds) and orthoimages. Thermal imagery is one of the additional methods that can be used for projecting. LIDAR data is precise, it allows us to measure relief even under the vegetation, or to collect laser re-flections from wires, metal constructions and poles. Precision and completeness of the DEM, produced from LIDAR data, allows to define relief microforms. Airborne imagery (visual spectrum) is very widespread and can be easily depicted. Thermal images are more strange and less widespread, they use different way of image forming, and spectral features of ob-jects can vary in specific ways. Either way, the additional spectral band can be useful for achieving additional spatial data and different object features, it can minimize field works. Here different aspects of thermal imagery are described in comparison with RGB (visual) images, LIDAR data and GIS layers. The attempt to estimate the feasibility of thermal imag-es for new data extraction is made.

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

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