Search

The goal of this study is to process digital images to facilitate color discrimination in dichromatic vision without changing their appearances in trichromatic vision as much as possible. Therefore, we propose a simple and effective image enhancement method using the geometric properties of the RGB color space. Concretely, a novel image representation that clarifies the relationship between dichromatic and trichromatic visions in the RGB color space, and an image enhancement method based on this representation are presented. The effectiveness of the proposed method is verified by experiments using several digital images. [ABSTRACT FROM AUTHOR]

The current study describes the development of simple, low-cost, and high-throughput digital image colorimetric methods to determine the total iron concentration in river sediment using the spot-test reactions of iron with 1,10-phenanthroline and thiocyanate. The colorimetric assay was done on 96-microzone plates, and a flatbed scanner was applied to acquire the images. The proposed methods offered a linear range from 0.2 to 14.0 mg/L, with a detection limit of 0.11 mg/kg for the 1,10-phenanthroline method, and, for the thiocyanate method, the linear range comprises 2.0–10.0 mg/L, with a detection limit of 0.28 mg/kg. It was observed that both proposed digital image colorimetric methods (1,10-phenanthroline and thiocyanate) yielded statistically similar results to the reference procedures at a 95% confidence level. A standard reference material (NIST 8704) also was utilized for accuracy assessment and the results were statistically equivalent to the certified values within the 95% confidence level. The digital image colorimetric methods can be an alternative method for iron determination in sediment samples, allowing fast sample screening at a low cost. [Display omitted] [ABSTRACT FROM AUTHOR]

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedures for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods—whenever available—is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review, the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented. [ABSTRACT FROM AUTHOR]

Objective To study the conversion model of RGB color space to CIE1976 L*a*b* color space with higher accuracy, which provides some value for the fields of computer color matching, color detection, and color reproduction. Methods The method of three-dimensional look-up table method and back propagation (BP) neural network are proposed, and the effect of the model built under the two methods is evaluated under the calculation of CIE1976 L*a*b* and CIE2000 color difference by NCS color card data. Results Under the calculation of CIE1976 L*a*b* color difference, the average color difference under the four interpolation methods of the three-dimensional look-up table is within 3, and the average color difference of the BP neural network algorithm is 1.8720. Under the calculation of CIE2000 color difference, the average color difference of the four interpolation methods of the three-dimensional look-up table drops within 1, and the average color difference of the BP neural network also shows a downward trend, and the specific value is 1.3449. Conclusions According to the result obtained by the research method, the color difference of the tetrahedral interpolation method is the smallest among the four interpolation methods of the three-dimensional look-up table method under both color difference formulas. Whether it is the three-dimensional look-up table method or the BP neural network, the model obtained by the CIE2000 color difference formula is the best. In general, for the two methods, the BP neural network method is more convenient and faster, and the color difference effect is also desirable. [ABSTRACT FROM AUTHOR]

People’s traditional music passed down from generation to generation should be heard by the world, and music tunes, as one of the indispensable basic elements of traditional music, need to be studied more deeply and systematically. In this paper, based on modern network information technology, we use big data visualization technology to analyze and study traditional music tunes. Using the RGB color space principle to understand the visualization technology, the number of research literature related to traditional music tunes in the past 20 years is obtained through literature collation, and the chart way of visualization using Sankey diagrams is analyzed to conclude traditional music tunes research. The data show that most research literature on traditional music tunes features an average of 6 articles per year. The number of ephemeral articles has gradually increased over the decade, with the total effective number of articles being about 7 in 2002 and increasing to about 39 in 2022. The research institutions are mainly concentrated in many universities in Henan, Yunnan, Hunan, Guangdong, and Guangxi, but the main traditional music tunes studied in each place also Have differences. The research on traditional music tunes by universities has a guiding and leading role, and the increase and depth of related research literature are also conducive to the protection and inheritance of traditional music tunes and integration and innovation in each region.

Rose cut flowers are popular in everyday bouquets or for special occasions, and tinting of the flowers by means of color addition increases the flowers economical value and aesthetic appeal. This study evaluated red and white luminescent rose cut flowers, which was achieved by applying six persistent luminescent powders. Solutions of each color were prepared by mixing 6 g of powder and 240 ml of deionized water and sprayed four times around the flower head plus a control. Images were taken of the flowers before ultraviolet blacklight exposure and after exposure to be later analyzed with ImageJ software. Daily measurements were taken including vase weight, average floral diameter, and visual deterioration based on scale of 1 to 4. Overall measurements included mean brightness; red, green, and blue measured values; dominant wavelengths of emitted color; flower diameter change rate; relative water percent; solution uptake rate; and vase solution uptake rate. For luminescent brightness mean without ultraviolet, white rose with blue powder had the greatest value. For luminescent brightness after ultraviolet exposure, white rose with green powder had the greatest value. With ultraviolet exposure, white roses with green powder had the greatest value followed by blue, orange, and white. Red powder on white and red roses experienced little to no luminescence before or after ultraviolet exposure. Mean and mode varied in their calculated dominant wavelengths; therefore, it is recommended to use mean values because more similarities in matching of the powder color and the calculated dominant wavelength were reported. Ultimately, white roses are preferred because they seemed to have greater health and luminescence compared with red roses, and green and blue powder would be recommended for luminescent application for brightness. [ABSTRACT FROM AUTHOR]

This work introduces a novel apparatus for monitoring both color and °Brix of selected fruit juices. These parameters are essential for quality and processing control at concentration and dilution operations. The proposed apparatus performs the red, green, and blue (RGB) image analysis of the fruit juice as it flows through a transparent cell equipped with a webcam and a light source. The viewing cell is inside a box for controlling temperature and for ensuring homogeneous lighting. This apparatus not only measures the RGB color space of the fruit juice online but also allows relating it to the °Brix. The relationship between RGB and °Brix was obtained by measuring the RGB color space of various fruit juice samples, of which the °Brix were previously determined using a digital refractometer. RGB image analysis showed an excellent relationship with °Brix (R2 > 0.99, using an exponential fit), allowing for monitoring reliably this parameter during continuous fruit juice concentration and dilution experiments. These results show the potential of this novel apparatus to substitute conventional instruments such as colorimeters and refractometers for online process monitoring. Furthermore, it has the advantage of not requiring sampling, which reduces measurement errors and saves time. Practical applications: This work introduces a novel apparatus based on RGB image analysis for monitoring simultaneously the color and the °Brix of fruit juices. These parameters are important to control the production and quality of fruit juices, especially at concentration and dilution operations. The apparatus proposed here is a viewing cell where the RGB color space is quantified using the software RGBview® as the fruit juice flows through it. The °Brix are determined from the RGB color space using a suitable fit obtained from a previous calibration with juice samples with known °Brix. This is an alternative to conventional analytical instruments such as colorimeters and refractometers but with the advantage of performing an online measurement. This configuration allows for the reduction of analytical errors associated with sampling and saves time. Moreover, this apparatus has the potential to monitor other physicochemical properties related to the color of fruit juices. [ABSTRACT FROM AUTHOR]

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedures for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods—whenever available—is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review, the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

Rose cut flowers are popular in everyday bouquets or for special occasions, and tinting of the flowers by means of color addition increases the flowers economical value and aesthetic appeal. This study evaluated red and white luminescent rose cut flowers, which was achieved by applying six persistent luminescent powders. Solutions of each color were prepared by mixing 6 g of powder and 240 ml of deionized water and sprayed four times around the flower head plus a control. Images were taken of the flowers before ultraviolet blacklight exposure and after exposure to be later analyzed with ImageJ software. Daily measurements were taken including vase weight, average floral diameter, and visual deterioration based on scale of 1 to 4. Overall measurements included mean brightness; red, green, and blue measured values; dominant wavelengths of emitted color; flower diameter change rate; relative water percent; solution uptake rate; and vase solution uptake rate. For luminescent brightness mean without ultraviolet, white rose with blue powder had the greatest value. For luminescent brightness after ultraviolet exposure, white rose with green powder had the greatest value. With ultraviolet exposure, white roses with green powder had the greatest value followed by blue, orange, and white. Red powder on white and red roses experienced little to no luminescence before or after ultraviolet exposure. Mean and mode varied in their calculated dominant wavelengths; therefore, it is recommended to use mean values because more similarities in matching of the powder color and the calculated dominant wavelength were reported. Ultimately, white roses are preferred because they seemed to have greater health and luminescence compared with red roses, and green and blue powder would be recommended for luminescent application for brightness.

[Objective] The transformation from RGB to Munsell color space is a relevant issue for different tasks, such as the identification of soil taxonomy, organic materials, rock materials, skin types, among others. This research aims to develop alternatives based on feedforward networks and the convolutional neural networks to predict the hue, value, and chroma in the Munsell soil-color charts (MSCCs) from RGB images. [Methodology] We used images of Munsell soil-color charts from 2000 and 2009 versions taken from Millota et al. (2018) to train and test the models. A division of 2856 images in 10% for testing, 20% for validation, and 70% for training was used to build the models. [Results] The best approach was the convolutional neural networks for classification with 93% of total accuracy of hue, value, and chroma combination; it comprises three CNN, one for the hue prediction, another for value prediction, and the last one for chroma prediction. However, the three best models show closeness between the prediction and real values according to the CIEDE2000 distance. The cases classified incorrectly with this approach had a CIEDE2000 average of 0.27 and a standard deviation of 1.06. [Conclusions] The models demonstrated better color recognition in uncontrolled environments than the Transformation of Centore, which is the classical method to transform from RGB to HVC. The results were promising, but the model should be tested with real images at different applications, such as soil real images, to classify the soil color.

Photonic crystal has become a powerful technique for regulating electromagnetic waves due to its excellent features, such as structure designability and dynamic adjustability; in addition, it has great application value. In this paper, we visualize colors on the Ostwald color model and analyze the color differences of each group of color patches via the minimum color difference model. The manipulation law of particle size, temperature, and humidity on the color change of photonic crystal hydrogel is verified by these color analyses. [ABSTRACT FROM AUTHOR]

Bilinear interpolation and use of perceptual color spaces (HSL, HSV, LAB, and LUV) fusion techniques are presented to improve spatial and spectral characteristics of the multispectral image that has a low resolution to match the high spatial resolution of a panchromatic image for different satellites image data (Orbview-3 and Landsat-7) for the same region. The Signal-to-Noise Ratio (SNR) fidelity criterion for achromatic information has been calculated, as well as the mean colorshifting parameters that computed the ratio of chromatic information loss of the RGB compound inside each pixel to evaluate the quality of the fused images. The results showed the superiority of HSL color space to fuse images over the rest of the spaces, as it recorded the highest SNR and the lowest mean color-shifting. The quality of the fused images using Lab color space was not affected by the type of intermediate algorithm used for XYZ space, unlike LUV color space. The XYZ algorithm type affected the fused image results, which recorded the worst consequences. It is noted that the computational time taken increased exponentially with the difference in spatial resolution between the fused images. [ABSTRACT FROM AUTHOR]

Shadow is a malicious phenomenon in aerial image processing algorithms. Locating shadow regions is very helpful in aerial image processing. This paper presents a new optimized algorithm for extracting shadows from a single aerial color image. Most of similar approaches extract shadows by using a suite color space. In this work, two RGB and YCbCr color spaces are combined and a powerful 2-steps ratio map has been created. Furthermore, as a new idea, effect of the blue color of sky is used to improve performance of the proposed ratio map. Candidate shadow and non-shadow regions are separated by applying Otsu's thresholding method. Because of the performance of the ratio map, it is not necessary to analyse the image locally and region by region. Therefore, it leads to decrease computational cost and it can be used in online applications. In order to demonstrate the superior performance of the proposed method, both qualitative and quantitative comparisons of the method with other state-of-the-art techniques are provided. [ABSTRACT FROM AUTHOR]

In this paper, the definition of three-dimensional generalized discrete fuzzy number (3-GDFN) is introduced based on the representation theorem of one-dimensional discrete fuzzy number and the similarity measure definition of two 3-GDFNs is given. Then the concept above mentioned is applied to color image representation and color mathematical morphology (CMM) in RGB space. The basic morphology operators, erosion and dilation, are extended to the CMM by defining the total preorder relation based on similarity measure between two 3-GDFNs instead of general vector sorting methods. The corresponding structuring elements in CMM are variable. Finally, the effectiveness and potential of the theoretical results are verified by comparative experiments. The proposed CMM operators are efficiently used in color image processing.

In this study, we evaluate the relationship between long-term variability of facial hue information in physiological and psychological regions of interests (ROIs) and health condition. Long-term facial hue information, RGB, and L*a*b* color space, were obtained from 13 physiological and psychological ROIs in visible facial images measured for approximately six months. In this study, the evaluation was performed based on four conditions (absolute value of RGB, absolute value of L*a*b*, relative value of RGB, and relative value of L*a*b*). These four long-term variabilities of facial hue information were applied to principal component analysis to clarify the relationship between facial hue information and health condition. The results suggested that long-term variability of chromaticity information around the periorbital region may be related to health conditions. Furthermore, physiological and psychological states related to health condition were estimated with the accuracy of approximately 80% using relative chromaticity information around the periorbital region.

Visual cryptography is a technique that allows visual information to be encrypted in such a way that the decrypted information appears as a visual image. To enhance visual cryptography for color images, the proposed technique utilizes the RGB color space. The process involves several steps, including color decomposition and error diffusion. Color decomposition breaks down the color image into its constituent color channels (red, green, and blue). Each color channel is then treated as a separate grayscale image and undergoes visual cryptography individually. Error diffusion is a technique used in half-toning, where the quantization residual (the difference between the original pixel value and the quantized value) is distributed to neighboring pixels that have not been processed yet. This helps in maintaining the overall visual quality of the decrypted image. In the proposed technique, the image is encrypted using four shares: cyan, magenta, yellow, and a mask. The mask is generated using a random function and consists of half-black and half-white pixels in each block. To review the secret image, all the shares are stacked together. By combining the shares, the original image can be visually decrypted. The addition of the AES algorithm can further enhance the security of the encryption process. Advanced Encryption Standard (AES) is a widely used symmetric encryption algorithm that provides strong cryptographic protection. By incorporating the AES algorithm at an appropriate stage in the visual cryptography process, the security of the encrypted shares can be strengthened. The specific details of where and how the AES algorithm is incorporated would depend on the implementation and design choices of the visual cryptography system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel hue-correction scheme based on the constant-hue plane in the RGB color space for deep-learning-based color-image enhancement. Existing hue-preserving image-enhancement methods cannot be applied to state-of-the-art enhancement methods such as deep-learning-based ones. Our main contributions are a discussion on the enhancement performance and the hue distortion of existing image-enhancement methods as well as the first attempt to make hue correction applicable to any existing image-enhancement methods including deep-learning-based ones. This novel scheme is carried out on the basis of the constant-hue plane in the RGB color space. In simulations, we first evaluated conventional image-enhancement methods in terms of the enhancement performance and hue distortion by using five objective metrics: the maximally saturated color similarity, the hue difference in CIEDE2000, discrete entropy, HIGRADE, and NIQMC. The experimental results show that recent deep-learning-based methods have a higher enhancement performance but cause images to be hue-distorted. In addition, the proposed scheme is demonstrated to be effective for suppressing hue distortion even under the use of deep-learning-based enhancement methods. Furthermore, it allows us not only to correct hue but also to maintain the performance of image-enhancement methods.

Photonic crystal has become a powerful technique for regulating electromagnetic waves due to its excellent features, such as structure designability and dynamic adjustability; in addition, it has great application value. In this paper, we visualize colors on the Ostwald color model and analyze the color differences of each group of color patches via the minimum color difference model. The manipulation law of particle size, temperature, and humidity on the color change of photonic crystal hydrogel is verified by these color analyses.

Image segmentation is a key step in the image analysis, pattern recognition, low-level vision, medical data analysis, objects tracking, recognition task and grasping of things from the field of robotics. Being a problematic and demanding chore in image processing, it governs the eminence of absolute outcomes of image analysis. The method aims to improve color detection using formulations in RGB arrays. First targeted color is selected and identified the desired color location by sliding window techniques. Then threshold has been calculated using the summation of within and between the class variance of the selected color. Proposed method overcomes the limitation of complex, the dearth incorrectness, and steadiness of conventional multilevel thresholding for image segmentation. This work is tested on a different kind of images such as two-dimensional images, low-quality images, complex images, blur images, and medical images. The simulated results designate the maximum accuracy and minimum computational time over other methods. [ABSTRACT FROM AUTHOR]

Capturing leaf color variances over space is important for diagnosing plant nutrient and health status, estimating water availability as well as improving ornamental and tourism values of plants. In this study, leaf color variances of the Eurasian smoke tree, Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net (GELnet) and support vector machine (SVM) algorithms. Results reveal that leaf color varied over space, and the variances were the result of geography due to its effect on solar radiation, temperature, illumination and moisture of the shrub environment, whereas the influence of climate were not obvious. The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables, and demonstrates that both techniques have the potential to estimate leaf color variances of C. coggygria in a shrubbery with a complex geographical environment in the absence of human activity. [ABSTRACT FROM AUTHOR]

Endoscopic imaging systems have been widely used in disease diagnosis and minimally invasive surgery. Practically, specular reflection (a.k.a. highlight) always exists in endoscopic images and significantly affects surgeons’ observation and judgment. Motivated by the fact that the values of the red channel in nonhighlight area of endoscopic images are higher than those of the green and blue ones, this paper proposes an adaptive specular highlight detection method for endoscopic images. Specifically, for each pixel, we design a criterion for specular highlight detection based on the ratio of the red channel to both the green and blue channels. With the designed criteria, we take advantage of image segmentation and then develop an adaptive threshold with respect to the differences between the red channel and the other ones of neighboring pixels. To validate the proposed method, we conduct experiments on clinical data and CVC-ClinicSpec open database. The experimental results demonstrate that the proposed method yields an averaged Precision, Accuracy, and F1-score rate of 88.76%, 99.60% and 72.56%, respectively, and outperforms the state-of-the-art approaches based on color distribution reported for endoscopic highlight detection.

This review systematizes for the first time the here called "Chemometrics‐assisted color histogram‐based analytical systems" under the acronym CACHAS. A comprehensive discussion of the fundamentals, general characteristics, and applications are presented in order to direct practical aspects for developing methods that use color histograms as input data for the construction of chemometric models. For this, analytical information is rapidly acquired using a simple image acquisition device (such as digital camera, webcam, scanner, or smartphone), without need for any image processing, other than extraction of the histograms, which are constructed by counting the frequency distribution of the different color tones (conveniently specified through a color space) of all pixels delimited by a region of interest in a digital image. As a consequence, CACHAS have been increasingly used for qualitative and quantitative purposes in food, agricultural, forensic, biomedical, and microbiological analysis, reinforcing the principles of green analytical chemistry. [ABSTRACT FROM AUTHOR]

Histogram sayısal görüntülerdeki piksellerin dağılımını gösteren önemli bir bilgi temsil yöntemidir. Gri seviyeli görüntülerde tek boyutlu dizi işlenirken, renkli görüntülerde üç boyutlu dizinin analizinin yapılması gereklidir. Dolayısıyla renkli görüntülerde histogram işleminin hesapsal maliyeti yüksektir. Her kanaldan alınan tek boyutlu histogram bilgisinin birleştirilmesi ise ayrıca bir problemdir. Bu çalışmada renkli görüntülerde Kırmızı-Yeşil-Mavi (KYM) renk uzayı kullanılarak tek boyutlu histogram üreten yeni bir teknik geliştirilmiştir. Önerilen yaklaşımda, öncelikle her kanal için Otsu ve Kapur eşikleme yöntemleri kullanılarak eşikler elde edilmiş, akabinde renk uzayı söz konusu eşikler yardımıyla 8 adet prizmaya bölünmüştür. Oluşturulan prizma içerisinde kalan pikseller aynı sınıfa atanarak kümeleme yapılmıştır. İlave olarak aynı sınıfa dâhil olan piksellerin ortalama değeri kullanılarak renk indirgemesi yapılmıştır. Böylece elde edilen görüntülerdeki bilgi kaybı tepe sinyal gürültü oranı (Peak Signal Noise Ratio: PSNR) ölçütü ile değerlendirilmiştir.

Despite a number of methods that have been developed for License Plate Detection (LPD), most of these focus on day images for license plate detection. As a result, license plate detection in night images is still an elusive goal for researchers. This paper presents a new method for LPD based on augmentation and Gradient Vector Flow (GVF) in night and day images. The augmentation involves expanding windows for each pixel in R, G and B color spaces of the input image until the process finds dominant pixels in both night and day license plate images of the respective color spaces. We propose to fuse the dominant pixels in R, G and B color spaces to restore missing pixels. For the results of fusing night and day images, the proposed method explores Gradient Vector Flow (GVF) patterns to eliminate false dominant pixels, which results in candidate pixels. The proposed method explores further GVF arrow patterns to define a unique loop pattern that represents hole in the characters, which gives candidate components. Furthermore, the proposed approach uses a recognition concept to fix the bounding boxes, merging the bounding boxes and eliminating false positives, resulting in text/license plate detection in both night and day images. Experimental results on night images of our dataset and day images of standard license plate datasets, demonstrate that the proposed approach is robust compared to the state-of-the-art methods. To show the effectiveness of the proposed method, we also tested our approach on standard natural scene datasets, namely, ICDAR 2015, MSRA-TD-500, ICDAR 2017-MLT, Total-Text, CTW1500 and MS-COCO datasets, and their results are discussed. [ABSTRACT FROM AUTHOR]

There are color vision barrier-free conversion algorithms that are used to improve the contrast in images for dichromats. The algorithms focus on whether dichromats can distinguish a combination of colors and often use a simulation model of color appearance for dichromats. To adjust the algorithms for individual dichromats, it is necessary to investigate whether the simulation model used is suitable for them in the vicinity of the color discrimination threshold Θ ; however, there have been few such studies. In this paper, we propose a method of judging the suitability of a simulation model for dichromatic color appearance in the vicinity of Θ using psychophysical measurements. In our method, gradually changing colors in the RGB color space are presented (Presentation A) to a dichromat and Θ A is obtained. Moreover, colors that are simulated by a target model are also presented (Presentation B) to the dichromat and Θ B is obtained. The suitability of the simulation model for the dirchromat is quantitatively judged by comparing Θ A and Θ B using statistical methods. An application example of the proposed method is also described to concretely show the procedure of the method. [ABSTRACT FROM AUTHOR]

The use of the RGB color model in colorimetric chemical sensing via imaging techniques is widely prevalent in the literature. However, the lack of specificity in the selection of RGB color space during capture and analysis presents a significant challenge in creating standardised methods for this field and possible discrepancies. In this study, we conducted a comprehensive comparison and contrast of a total of 68 RGB color spaces to evaluate their respective impacts on colorimetric bio/chemical sensing. We explore the impact of dynamic range, sensitivity, and limit of detection, and show that the lack of specificity in RGB color space selection can significantly impact colorimetric chemical sensing by 42–77%. We also explore the impact of underlying RGB comparisons and demonstrate a further 18.3% discrepancy between RGB color spaces. By emphasising the importance of proper RGB color space selection and handling, our findings contribute to a better understanding of this critical area and present valuable opportunities for future research. We further provide valuable insights for creating standardised methods in this field, which can be utilised to avoid discrepancies and ensure accurate and reliable analysis in colorimetric bio/chemical sensing. [Display omitted] • Unspecified RGB color spaces can compromise chemical sensing via imaging techniques. • Sensory characteristics significantly affected by RGB color space selection (42–77%). • Conversion between RGB spaces can lead to further discrepancies (18%). • 68 RGB color spaces compared - proper selection is critical for accurate analysis. • Proposed method herein can contribute to standardised methods/analysis in the field. [ABSTRACT FROM AUTHOR]

The present study describes a new imaging method to acquire rice leaf images under field conditions using a smartphone and modeling approaches to retrieve the leaf chlorophyll (Chl) content from digitized images. Pearson's correlation of image-based color indices of the relative Chl content measured with Soil Plant Analysis Development (SPAD) indicated the suitability of the color models RGB, rgb, and DGCI-rgb. Among the linear regression models, the models based on mean brightness ratio (rgb) alone or in combination with a dark green color index (DGCI-rgb) show a good correlation between the predicted Chl content and relative Chl content. A feed-forward backpropagation-type network was also developed following the optimization of hidden neurons, training, and transfer functions. The predicted Chl contents showed a good correlation with SPAD values. Compared to the linear regression model, the developed artificial neural network model was found to be more efficient in predicting the Chl content, particularly with RGB index. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Fire is one of the most devastating hazards that can cause serious damage to human life, health and property. As smoke is often an initial sign of fire, smoke detection using surveillance cameras is key to providing early alarm in open space environments. In this paper, we propose a new feature extraction method that combines local binary patterns with co-occurrence of texture features in RGB color space to characterize the diverse manifestations of smoke. The proposed RGB color based Local Binary Co-occurrence Patterns (RGB_LBCoP) extracts smoke features from candidate smoke regions which are extracted by Fuzzy C-Means (FCM) algorithm. Subsequently, Support Vector Machine (SVM) is used for training and classification based on these features. The major benefit of the proposed feature descriptor is the ability to incorporate local and global texture properties of smoke along with color information. This property enables the detection of smoke in complex environments and provides insensitivity to illumination changes. For validation, performance of the proposed method is compared with other LBP variants and Gray-level co-occurrence matrix (GLCM). Experimental analysis on publicly available smoke video datasets demonstrates that the proposed algorithm outperforms the other methods by achieving an average True Positive Rate (TPR) of 92.02%. Highlights • A new feature extraction technique is successfully applied for smoke detection. • Combines LBP with co-occurrence of texture features in RGB color space. • The texture features are calculated for each of the three channels of RGB color space. • Combines local and global texture features to detect smoke for complex scenario. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)