Grey level to RGB using YCbCr color space Technique

Reddy, R. Meenakshi; Raju, K. Prudvi; Kumar, L. Ravi; Kumar, M. Jogendra

doi:10.5120/ijca2016911180

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…We also worked in the greyscale system, which allows users to obtain a single parameter of the colour intensity based on the combination of RGB colour space values, facilitating and simplifying colour analysis. Conversion of RGB to greyscale space can be done by three methods with different algorithms [25,26]—method of luminosity (Equation (1)), method of average (Equation (2)) and method of lightness (Equation (3)): I = 0.21R + 0.72G + 0.07B, I = (R + G + B)/3, I = (max (R, G, B) + min (R, G, B))/2.…”

Section: Methodsmentioning

confidence: 99%

Estimation of PM10 Levels and Sources in Air Quality Networks by Digital Analysis of Smartphone Camera Images Taken from Samples Deposited on Filters

Carretero-Peña

Blázquez

Gil

2019

Sensors

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This paper explores the performance of smartphone cameras as low-cost and easily accessible tools to provide information about the levels and origin of particulate matter (PM) in ambient air. We tested the concept by digital analysis of the images of daily PM10 (particles with diameters 10 µm and smaller) samples captured on glass fibre filters by high-volume aerosol samplers at urban and rural locations belonging to the air quality monitoring network of Extremadura (Spain) for one year. The images were taken by placing the filters inside a box designed to maintain controlled and reproducible light conditions. Digital image analysis was carried out by a mobile colour-sensing application using red, green, blue/hue, saturation, value/hue, saturation, luminance (RGB/HSV/HSL) parameters, that were processed through statistical procedures, directly or transformed to greyscale. The results of the study show that digital image analysis of the filters can roughly estimate the concentration of PM10 within an air quality network, based on a significant linear correlation between the concentration of PM10 measured by an official gravimetric method and the colour parameters of the filters’ images, with better results in the case of the saturation parameter (SHSV). The methodology based on digital analysis can discriminate urban and rural sampling locations affected by different local particle-emitting sources and is also able to identify the presence of remote sources such as Saharan dust outbreaks in both urban and rural locations. The proposed methodology can be considered as a useful complement to the aerosol sampling equipment of air quality network field units for a quick estimation of PM10 in the ambient air, through a simple, accessible and low-cost procedure, with further miniaturization potential.

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Section: Methodsmentioning

confidence: 99%

Estimation of PM10 Levels and Sources in Air Quality Networks by Digital Analysis of Smartphone Camera Images Taken from Samples Deposited on Filters

Carretero-Peña

Blázquez

Gil

2019

Sensors

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“…At this time, due to the difference in dimension between SAR and reference MS images, image differencing is performed after matching their dimensions. The reference MS image is matched with the dimension of the SAR image by using the luminosity method and the equation is shown below [36].…”

Section: Fusion Methodsmentioning

confidence: 99%

Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection

Seo

Kim

et al. 2018

IJGI

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In order to overcome the insufficiency of single remote sensing data in change detection, synthetic aperture radar (SAR) and optical image data can be used together for supplementation. However, conventional image fusion methods fail to address the differences in imaging mechanisms and cannot overcome some practical limitations such as usage in change detection or temporal requirement of the optical image. This study proposes a new method to fuse SAR and optical images, which is expected to be visually helpful and minimize the differences between two imaging mechanisms. The algorithm performs the fusion by establishing relationships between SAR and multispectral (MS) images by using a random forest (RF) regression, which creates a fused SAR image containing the surface roughness characteristics of the SAR image and the spectral characteristics of the MS image. The fused SAR image is evaluated by comparing it to those obtained using conventional image fusion methods and the proposed method shows that the spectral qualities and spatial qualities are improved significantly. Furthermore, for verification, other ensemble approaches such as stochastic gradient boosting regression and adaptive boosting regression are compared and overall it is confirmed that the performance of RF regression is superior. Then, change detection between the fused SAR and MS images is performed and compared with the results of change detection between MS images and between SAR images and the result using fused SAR images is similar to the result with MS images and is improved when compared to the result between SAR images. Lastly, the proposed method is confirmed to be applicable to change detection.

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Human detection techniques for real time surveillance: a comprehensive survey

Ansari

Singh

2020

Multimed Tools Appl

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Grey level to RGB using YCbCr color space Technique

Cited by 6 publications

References 3 publications

Estimation of PM10 Levels and Sources in Air Quality Networks by Digital Analysis of Smartphone Camera Images Taken from Samples Deposited on Filters

Estimation of PM10 Levels and Sources in Air Quality Networks by Digital Analysis of Smartphone Camera Images Taken from Samples Deposited on Filters

Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection

Human detection techniques for real time surveillance: a comprehensive survey

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