Measurement of correlated color temperature from RGB images by deep regression model

Çatalbaş, Mehmet Cem; Kobav, Matej Bernard

doi:10.1016/j.measurement.2022.111053

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…CNN, which are variants of multilayer perceptrons, mimic the functionality of the human visual cortex and are well suited for image processing tasks. The adaptability of CNNs have been proven in study [13] to handle large scale image datasets, delivering results that confirm their capability in processing not only static two-dimensional images but also dynamic three-dimensional video data. This attribute of CNNs, when used in a plant monitoring domain for thermal imagery use cases, indicates a promising approach for accurately forecasting plant health indicators by interpreting the thermal patterns that are indicative of a plant physiological state.…”

Section: Related Work and Research Gapsmentioning

confidence: 75%

“…Another study, related to the utilization of CNN based regression models, has proven to be an effective approach for estimating correlated color temperature (CCT) from RGB images [13]. CNN, which are variants of multilayer perceptrons, mimic the functionality of the human visual cortex and are well suited for image processing tasks.…”

Section: Related Work and Research Gapsmentioning

confidence: 99%

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Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Kempelis,

Polaka,

Romanovs

et al. 2024

Future Internet

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Urban agriculture presents unique challenges, particularly in the context of microclimate monitoring, which is increasingly important in food production. This paper explores the application of convolutional neural networks (CNNs) to forecast key sensor measurements from thermal images within this context. This research focuses on using thermal images to forecast sensor measurements of relative air humidity, soil moisture, and light intensity, which are integral to plant health and productivity in urban farming environments. The results indicate a higher accuracy in forecasting relative air humidity and soil moisture levels, with Mean Absolute Percentage Errors (MAPEs) within the range of 10–12%. These findings correlate with the strong dependency of these parameters on thermal patterns, which are effectively extracted by the CNNs. In contrast, the forecasting of light intensity proved to be more challenging, yielding lower accuracy. The reduced performance is likely due to the more complex and variable factors that affect light in urban environments. The insights gained from the higher predictive accuracy for relative air humidity and soil moisture may inform targeted interventions for urban farming practices, while the lower accuracy in light intensity forecasting highlights the need for further research into the integration of additional data sources or hybrid modeling approaches. The conclusion suggests that the integration of these technologies can significantly enhance the predictive maintenance of plant health, leading to more sustainable and efficient urban farming practices. However, the study also acknowledges the challenges in implementing these technologies in urban agricultural models.

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Section: Related Work and Research Gapsmentioning

confidence: 75%

Section: Related Work and Research Gapsmentioning

confidence: 99%

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Kempelis,

Polaka,

Romanovs

et al. 2024

Future Internet

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show abstract

“…From the mixed SPDs, the CCT and SWR were calculated. The CCT was calculated using McCamy's CCT formula [25][26]. SWR is calculated by dividing the integral in the short wavelength (380 to 480 nm) region by the integral in the visible light spectrum (380 to 780 nm) region.…”

Section: Calculate Cct and Swr By Mixing Ratiomentioning

confidence: 99%

The Control Method for Wavelength-Based CCT of Natural Light Using Warm/Cool White LED

Yang-Soo Kim,

Seung-Taek Oh,

Jae-Hyun Lim

2023

Proc. eng. technol. innov.

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Reproducing circadian patterns of natural light through lighting requires technology that can control correlated color temperature (CCT) and short wavelength ratio (SWR) simultaneously. This study proposes a method for controlling wavelength-based CCT of natural light using LED light sources. First, the spectral power distribution (SPD) of each channel of the test lighting (two-channel LED lighting with warm white and cool white) is identified through actual measurement. Next, CCT and SWR are calculated based on the additive mixing of SPD using the mixing ratio from the measured SPD. Finally, the regression equations for mixing ratio-CCT and mixing ratio-SWR are derived through regression analysis. These equations are then utilized to implement a wavelength-based CCT control algorithm. For performance and evaluation purposes, natural light reproduction experiments were conducted, achieving a mean error of 94.5K for CCT and 1.5% for SWR.

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“…Mehmet Cem Catalbas & Matej Bernard Kobav [21] suggested an evaluation of correlated colour temperature (CCT) using a deep regression model from RGB images. Owing to the deep regression model, CCT values for the surroundings were estimated nearer to the results of the spectroradiometer measurement.…”

Section: Related Workmentioning

confidence: 99%

Guided Co-operative Waderhunt Neural Network for Measuring the Temperature of Visible Heat Source

2023

IJIES

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In this article, a novel optimization-assisted deep learning (OptiA-DL) strategy is proposed to effectively measure the temperature of visible heat sources. Initially, the image is acquired from the heat source and filtered using gelatin paper. The guided co-operative trilateral filter (GCTF) is used for image smoothening and HSV (Hue, Saturation, Value) or RGB (Red, Green, Blue) structure for colour transformation. Then, an improved Gaussian mixture model (IG-MM) is applied to segment the image into various portions. Accordingly, the compact split attention-based convolutional waderhunt autoencoder network (CSAtt-CWAN) is used for temperature prediction. In CSAtt-CWAN, the parameters are tuned using the waderhunt optimization algorithm (WHOA). The proposed OptiA-DL strategy is implemented in the Python platform through the candle flame dataset and assessed the performance in terms of different evaluation measures. Moreover, a proposed OptiA-DL's performance is compared with existing methods to determine its efficiency in temperature measurement. The minimum mean square error (MSE) value obtained by the proposed OptiA-DL strategy is 0.076, which is lower than the existing methods for measuring the temperature of the visible heat source.

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Measurement of correlated color temperature from RGB images by deep regression model

Cited by 5 publications

References 30 publications

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems

The Control Method for Wavelength-Based CCT of Natural Light Using Warm/Cool White LED

Guided Co-operative Waderhunt Neural Network for Measuring the Temperature of Visible Heat Source

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