Average RGB Value Research Articles

A rapid colorimetric sensing methodology for urinary tract bacterial pathogens as a point-of-care approach using natural anthocyanin loaded nanosilver

Various commercial diagnostic kits have been used to detect uropathogens. However, non-intensive, sensitive, and instantaneous detection of bacteria in urine is still at its infancy. Herein, a facile attempt was carried out to detect the most common uropathogens using anthocyanin-loaded silver nanoparticles as the indicator. The significance of the current research was to apply the crucial sensing ability of eggplant peel anthocyanin to detect each pathogen by exhibiting discrete colors, based on pH of the urine sample. The novelty of this study was the ability of a single indicator compound to distinctively differentiate multiple uropathogens, as monocultures or consortia, with accurate chromacity, simultaneously and instantaneously. Anthocyanin (AC) from eggplant (Solanum melongena) peels acted as a pH responsive sensor in the prepared indicator solution. The solution was used to sense three each of Gram-positive and Gram-negative uropathogens, as both monoculture and consortium. AC was loaded onto silver nanoparticles (AgNP) to stabilize the pigment. An evident color change due to the function of AC was considered as the end-point of detection. The optimized conditions for the study were obtained as: 50 μL indicator solution, 105CFU.mL−1 inoculum, and instantaneous sensing time. Deprotonation and protonation of AC due to ammonia and acids, evolved from the microbes, subsequently varied the pH level in the medium. These resulted in the generation of colors as hues of yellow, orange, green and blue and semi-quantitatively represented using average RGB values and delta E analysis obtained after the indicator solution was added to 24 h bacterial growth. In conclusion, the proposed solution can be a potential alternative to the conventional diagnostic tools as a specific, sensitive, rapid, precise and affordable indicator for point-of-care diagnosis of urinary tract infection.

A computer vision enhanced smart phone platform for microfluidic urine glucometry.

Glucose is an important biomarker for diagnosing and prognosing various diseases, including diabetes and hypoglycemia, which can have severe side effects, symptoms, and even lead to death in patients. As a result, there is a need for quick and economical glucose level measurements to help identify those at potential risk. With the increase in smartphone users, portable smartphone glucose sensors are becoming popular. In this paper, we present a disposable microfluidic glucose sensor that accurately and rapidly quantifies glucose levels in human urine using a combination of colorimetric analysis and computer vision. This glucose sensor implements a disposable microfluidic device based on medical-grade tapes and glucose analysis strips on a glass slide integrated with a custom-made polydimethylsiloxane (PDMS) micropump that accelerates capillary flow, making it economical, convenient, rapid, and equipment-free. After absorbing the target solution, the disposable device is slid into the 3D-printed main chassis and illuminated exclusively with Light Emitting Diode (LED) illumination, which is pivotal to color-sensitive experiments. After collecting images, the images are imported into the algorithm to measure the glucose levels using computer vision and average RGB values measurements. This article illustrates the impressive accuracy and consistency of the glucose sensor in quantifying glucose in sucrose water. This is evidenced by the close agreement between the computer vision method used by the sensor and the traditional method of measuring in the biology field, as well as the small variation observed between different sensor performances. The exponential regression curve used in the study further confirms the strong relationship between glucose concentrations and average RGB values, with an R-square value of 0.997 indicating a high degree of correlation between these variables. The article also emphasizes the potential transferability of the solution described to other types of assays and smartphone-based sensors.

A semi-automated, GIS-based framework for the mapping of supraglacial hydrology

AbstractSupraglacial drainage networks play an integral role in both glacier dynamics and run-off timing, and mapping them provides insight into their role in glacial systems. Here we present a reproducible approach for semi-automated mapping of supraglacial hydrologic features, which complements existing work in automated and manual mapping by providing clear definitions for identification of features. This framework uses a digital terrain model (DTM) to identify potential flow routes on the glacier surface, which are then classified using a set of standardized rules based on the DTM and an orthomosaic. We found that the normalized difference water index calculated from digital imagery was influenced by image brightness and introduce a new approach using average RGB values to correct for this. Using this framework we mapped supraglacial drainage networks at Nàłùdäy and Thores Glacier, Canada. The framework was easier to implement with high-resolution (0.5 m) imagery and DTMs, compared to data with lower resolution (10 m), due to the increased detail in topography and feature boundaries at high-resolution. Lower-resolution data captured larger streams (>2 pixels wide), however, indicating that the framework can still be used at this resolution. Mapping supraglacial hydrology using standardized methods opens possibilities for investigating many questions relating to changes in supraglacial hydrology over time.

Computerized visualization of seeds of Sepa subgenus (Allium L., Alliaceae – an effective tool to assess their quality

The authors presented the results of a study of the morphology of Allium seeds from the subgenus Cepa: Cepa section (Mill.) Prokh. A. fistulosum L., A. altaiсum Pall., A. galanthum Kar. & Kir., A. oschaninii O. Fedtsch., A. pskemense B. Fedtsch.; Schoenoprasum Dum. – A. altyncoliсum, A. ledebourianum, A. oliganthum, A. schoenoprasum L.; Condensatum N. Friesen – A. condensatum, from the biocollection of All-Russian Research Institute of Vegetable Growing – Branch of the Federal State Budgetary «Scientific Institution Federal Scientific Centre for Vegetable Growing») (Moscow region). Seeds were 2.74 – 3.50 mm long and 1.33 – 2.14 mm wide. The morphological characteristics of the seeds can be used as additional taxonomic indicators in the identification and classification of taxa within the Cepa subgenus of the genus Allium. The authors measured the morphometric and optical parameters of the seeds by image analysis using VideoTest-Morphology software developed at Argus Bio Ltd. (St. Petersburg). Seed digital images were obtained using an HP Scanjet 200 digital flatbed scanner, 600 dpi resolution, and JPG file format. Morphometric parameters of seeds were determined, including projection area (cm2), length, width, perimeter, average size (mm), average diameter Fere, factors of roundness, elongation, ellipse, rugosity (relative units), parameters of brightness, tonality, color saturation (relative units). Based on the results of the study, a series of distributions of species was compiled in descending order of each of the characters studied. Within the Cepa section, the seeds of A. pskemense had the maximum linear size, perimeter, and cross-sectional area. Within the Schoenoprasum section, the seeds of A. altyncolium had the maximum length. The maximum width, perimeter, cross-sectional area, and average diameter of Fere seeds were recorded for A. ledebourianum. In the Cepa section, the average RGB value in descending order was as follows: A. pskemense > A. galanthum > A. fistulosum > A. altaiсum > A. oschaninii. In the Schoenoprasum section, this series is as follows. A. schoenoprasum > A. ledebourianum > A. altyncoliсum > A. oliganthum.

Analysis of morphometric and optical parameters of seeds of the subgenus cepa (<i>Allium</i> L., Alliaceae) by digital scanning

The results of the study of seed morphology from the subgenus Cepa: section Cepa (Mill.) Prokh. - Allium fistulosum L., A. altaiсum Pall., A. galanthum Kar. & Kir., A. oschaninii O. Fedtsch., A. pskemense B. Fedtsch.; section Schoenoprasum Dum. - A. altyncoliсum , A. ledebourianum , A. oliganthum , A. schoenoprasum L.; section Condensatum N. Friesen - A. condensatum are presented. Morphological characters of seeds can be used as additional taxonomic indicators in the identification and distinction of taxa within the subgenus Cepa of the genus Allium . The seeds were 2.74-3.50 mm long and 1.33-2.14 mm wide. The morphometric and optical parameters of seeds were measured by analyzing images using software. Digital images of seeds were obtained using an HP Scanjet 200 digital flatbed scanner, 600 dpi resolution, JPG file format. Morphometric and optical parameters of seeds were determined, including projection area (cm 2 ), length, width, perimeter, mean size (mm), average feret diameter, factors of roundness, elongation, ellipse, indentation (relative units), parameters of brightness, tonality, color saturation (relative units). According to the results of the study, a series of distribution of species in descending order for each of the studied traits are formed. Within the Cepa section, A. pskemense seeds had the maximum linear size, perimeter, and cross-sectional area. Among the representatives of Schoenoprasum section, the maximum length of the seeds was found in A. altyncoliсum . Maximum width, perimeter, cross-sectional area, average feret diameter of the seeds were recorded in A. ledebourianum . In the Cepa section, the average RGB value in descending order was as follows: A. pskemense > A. galanthum > A. fistulosum > altaiсum > A. oschaninii . In the Schoenoprasum section this series has the form: A. schoenoprasum > A. ledebourianum > A. altyncoliсum > A. oliganthum .

Klasifikasi Citra Tanaman Perdu Liar Berkhasiat Obat Menggunakan Jaringan Syaraf Tiruan Radial Basis Function

Wild plants or what are usually called weeds are plants that are considered harmful because they grow in unwanted places. But it turns out that some wild plants have many benefits for the health of the human body. Wild plants have many forms of vegetation, one of which is often encountered is shrubs. There are many wild herbaceous plants that are efficacious as medicine. However, most of the people who do not have knowledge about the types of wild shrubs that have medicinal properties. This study aims to implement the Radial Basis Function (RBF) algorithm for the classification of wild herbaceous plant species with medicinal properties by extracting color and texture features. The color feature extraction is based on the average RGB value, while the texture feature extraction uses a Gabor filter with the mean, entropy, and variance parameters of the magnitude image. The result of feature extraction becomes input data which will be managed by the RBF artificial neural network. RBF is a neural network that has three layers that have feedforward properties that can assist in solving classification or pattern recognition problems. Based on the test results, the precision value is 91%, recall is 89% and accuracy is 90%. These results show that the Radial Basis Function (RBF) algorithm with color and texture feature extraction can classify wild shrubs with medicinal properties well.

Classification of rice plant nitrogen nutrient status using k-nearest neighbors (k-NN) with light intensity data

<span>Crop management including the efficient use of nitrogen (N) fertilizer is important to ensure crop productivity. Human error in judging the leaf greenness when using the leaf color chart (LCC) to estimate the rice plant N nutrient status has encouraged numerous researchers to implement a machine-learning algorithm but experienced some issues in calibration and lighting. The datasets are created at 6.00-7.00AM (consistent lighting) and including light intensity, so each dataset contains RGB value and light intensity as inputs, and LCC value as a target. A system consists of a smartphone with an application that prevents user from taking an image if the light intensity is not in 2000-3500 lux, and a computer for preprocessing and classification purposes were developed. The preprocessing included cropping, splitting the rice leaf images, and calculating the average RGB values. A k-NN classifier is implemented and by using a cross-validation method is found k=5 gives the best accuracy of 97,22%. The in-site test of the system also works with an accuracy of 96.40%. </span>

Rancang Bangun Purwarupa Sistem Deteksi Tingkat Kematangan Buah Jeruk Berdasarkan Warna Kulitnya

The process of selecting the level of maturity of an orange fruit can be done electronically, using a color sensor that will detect the color of the orange peel. Orange fruits will be grouped into two levels of maturity, namely unripe/raw and ripe. By using image processing techniques, the image of orange fruit to be selected is searched for components R (Red), G (Green), and B (Blue). If an average RGB value of less than 100 is categorized as mature and if more than 100 is categorized as immature/raw. The test results show that this system can work well as planned.

Development of a New Method for Turbidity Measurement Using Two NIR Digital Cameras.

This paper proposes a new method of using two NIR digital cameras to measure water turbidity accurately and quickly. A measuring device based on an NIR camera and image processing software is designed. Two NIR cameras collect scattered and transmitted images when the NIR light is passing through the turbid solution. The average RGB values of 400 pixels in the central region of the image are obtained and converted into CIE Lab color space values. The water turbidity was measured by the functional relationship between turbidity and the corresponding color components (R, G, B, L, a, b, and grayscale). The results of comparison with a commercial turbidimeter show that this method has a high accuracy for the determination of standard solution with wider linear range and is consistent with the turbidimeter results for the measurement of real samples, which verifies the feasibility of this method.

Circle Hough Transform (CHT) method applied to coin identifier and recognition based on computer vision and android developer

The coin recognition system is a very important role in currency processing systems which is aims to minimize human miscalculation and time to calculate coin values. The coin identifier and recognition to calculate the value of Indonesian coins based on android developer presented in this paper. The two coin parameters are used for coin recognition which are radius and colour of the coins. The Circle Hough Transform (CHT) method with gray scaling and erosion process as pre-processing image used to calculate radius parameters, based on android developer and computer vision. After the radius value is obtained then the average RGB value of the coins is detected to determine the colour parameters. By calculating the diameter and determining the colour, the coins can be recognized and the total value of the coins in the input image can be known. The experimental results show that the system has been obtained indicate that the system successfully recognized coins based on determined algorithm. That is mean the application using computer vision can recognition coin and count the value of coins.

Photovoltage-triggered electrochromic tablet for visualized photoelectrochemical sensing

In this work, we designed an integrated photoelectrochemical (PEC) analytical system with photovoltage-regulated electrochromic tablet for visualized sensing. The electrochromic tablet consisted of electron-injector (EI) part and colorimetric system, which was functionalized with dye-sensitized titanium dioxide and nickel oxide nanosheet film as the recognition element and signal readout, respectively. Under irradiation of home-built white light, the EI part provided sufficient and stable photovoltage and injected the photoexcited electrons to nickel oxide nanosheet through the conductive inner side of indium tin oxide slide. Thus, the different color states of nickel oxide nanosheet were observed, resulting in a detectable signal by naked eyes for visual analysis. More importantly, the color change was identified to be dependent on the photovoltages generated from EI part. Using copper ions as a model analyte, the electrochromic tablet was successfully applied to visually detect copper ions based on its quenching effect on photovoltage due to the formation of exciton trapping. As a result, the copper ions could be simultaneously determined over a wide range through photovoltage record, average RGB value, and naked eyes. Furthermore, the device exhibited considerable stability and could be reused through simple washing steps. The smart tablet PEC system provides a new avenue to design practical PEC sensor in point-of-care diagnosis.

A Low-Cost System Based on Image Analysis for Monitoring the Crystal Growth Process.

Many techniques are used to monitor one or more of the phenomena involved in the crystallization process. One of the challenges in crystal growth monitoring is finding techniques that allow direct interpretation of the data. The present study used a low-cost system, composed of a commercial webcam and a simple white LED (Light Emitting Diode) illuminator, to follow the calcium carbonate crystal growth process. The experiments were followed with focused beam reflectance measurement (FBRM), a common technique for obtaining information about the formation and growth of crystals. The images obtained in real time were treated with the red, blue, and green (RGB) system. The results showed a qualitative response of the system to crystal formation and growth processes, as there was an observed decrease in the signal as the growth process occurred. Control of the crystal growth was managed by increasing the viscosity of the test solution with the addition of monoethylene glycol (MEG) at 30% and 70% in a mass to mass relationship, providing different profiles of the RGB average curves. The decrease in the average RGB value became slower as the concentration of MEG was increased; this reflected a lag in the growth process that was proven by the FBRM.

Application of fluorescent particles as a tracer to detect the membrane surface damage in a pilot scale membrane bioreactor

In this study, a fluorescent silica nano particle is used as the surrogate for challenging test of membrane surface integrity. The particles are functionalized by a fluorescent dying agent so that as an ultraviolet light is imposed a bright fluorescent image from the particles can be taken. If a membrane surface is damaged and has a compromised part larger than the size of surrogate the fluorescent particles would pass through and contained in the permeate. An operator can directly notice whether the membrane surface is damaged or not by detecting a fluorescent image taken from the permeate. Additionally, the size of compromised part is estimated through analysing the fluorescent image in which we surmise the mass of particles included in the permeate by calculating an average RGB value of the image. The pilot scale experiments showed that this method could be applied successfully to determine if a membrane surface had a damaged parts regardless of the test condition. In the testing on the actual damaged area of , the lowest error of estimating the damaged area was -1.32% with the surrogate concentration of 80 mg/L, flux of for 25 minutes of detection. A further study is still going on to increase the lowest detection limit and thus decrease the error of estimation.

적응적 타일링 및 블록 매칭을 통한 포토 모자이크 알고리즘

모자이크란 여러 가지 빛깔의 재료를 조각조각 붙여서 무늬나 영상을 만드는 기법을 말하며, 최근에는 디지털 이미징 기술의 발달로 인하여 사진을 이용하여 영상을 만드는 포토 모자이크 기술들이 활용되고 있다. 본 논문에서는 적응적 타일링 및 블록 매칭을 통하여 포토 모자이크 영상을 만드는 컴퓨터 알고리즘을 제안한다. 제안한 알고리즘은 사진 데이터베이스의 생성 단계와 포토 모자이크 생성 단계로 구성된다. 사진 데이터베이스란 모자이크에 사용되는 사진(또는 타일)을 의미하며, 타일을 <TEX>$4{\times}4$</TEX>로 분할한 후에 각 영역의 RGB 평균값을 특징값으로 저장한다. 포토 모자이크 생성 단계는 입력 영상에 대하여 기 설정된 블록 크기로 분할한 후에 특징을 추출하는 과정, 인접한 블록들 사이의 유사도를 비교하여 병합하는 적응적 타일링 과정, 적응적 타일링을 통해 생성된 블록들을 사진 데이터베이스의 타일들과 유클리드 차이로 유사도를 비교하여 유사한 타일을 찾는 블록 매칭 과정 및 매칭된 타일의 명암값을 해당 블록의 명암값으로 교체하여 영상의 유사도를 높이는 밝기값 조정 과정으로 구성된다. 또한 인접 블록간 타일의 중복성을 최소화하는 기법을 적용하여 영상의 품질도 향상하였다. 제안한 알고리즘의 성능을 분석하기 위하여 안드레아 모자이크 소프트웨어와 비교하였고, 정량적인 분석 및 정성적인 분석에 있어서 제안한 알고리즘이 우수한 것으로 나타났다. Mosaic is to make a big image by gathering lots of small materials having various colors. With advance of digital imaging techniques, photomosaic techniques using photos are widely used. In this paper, we presents an automatic photomosaic algorithm based on adaptive tiling and block matching. The proposed algorithm is composed of two processes: photo database generation and photomosaic generation. Photo database is a set of photos (or tiles) used for mosaic, where a tile is divided into <TEX>$4{\times}4$</TEX> regions and the average RGB value of each region is the feature of the tile. Photomosaic generation is composed of 4 steps: feature extraction, adaptive tiling, block matching, and intensity adjustment. In feature extraction, the feature of each block is calculated after the image is splitted into the preset size of blocks. In adaptive tiling, the blocks having similar similarities are merged. Then, the blocks are compared with tiles in photo database by comparing euclidean distance as a similarity measure in block matching. Finally, in intensity adjustment, the intensity of the matched tile is replaced as that of the block to increase the similarity between the tile and the block. Also, a tile redundancy minimization scheme of adjacent blocks is applied to enhance the quality of mosaic photos. In comparison with Andrea mosaic software, the proposed algorithm outperforms in quantitative and qualitative analysis.

Potential Application of Color and Hyperspectral Images for Estimation of Weight and Ripeness of Oil Palm (Elaeis guineensis Jacq. var. tenera)

The intent of this study was to develop a technique for weight and ripeness estimation of oil palm (Elaeis guieensis Jacq. var. tenera) bunches from hyperspectral and RGB color images. In the experiments, color and hyperspectral images of the bunch were acquired from four different angles, each differing by 90 degrees. Acquired RGB color images were converted to HSI and L*a*b color space. Gray-scale thresholds were used to identify the area of the bunch and the area of space between the fruits. The total number of pixels in the bunch and the space were counted, respectively. In the hyperspectral images, the total number of pixels in the bunch was also counted from an image composed of three wavelengths (560 nm, 680 nm, and 740 nm), while the total number of pixels of space between fruits was obtained at a wavelength of 910 nm. From these sets of data, weight-estimation equations were determined by linear regression (LR) or multiple linear regression (MLR). As a result, the coefficient of determination (R2) of actual weight and estimated weight were at a level of 0.989 and 0.992 for color and hyperspectral images, respectively. Estimation of oil palm bunch ripeness was also tested. Bunches belonging to 4 classes of ripeness (overripe, ripe, underripe, and unripe) were used for this study. Since ripeness estimation from overall data from a bunch was quite difficult, we focused on the difference in colors or reflectivity of the portion concealed and not-concealed with fronds. Euclidean distances between the test sample and the standard 4 classes of ripeness were calculated, and the test sample was classified into the ripeness class that had the shortest distance from the sample. In the classification based on color image, average RGB values of concealed and not-concealed areas were used, while in hyperspectral images the average intensity values of fruits pixels from the concealed area were used. The results of validation experiments with the developed estimation methods indicated acceptable estimation accuracy, and a possibility for practical use to estimate the ripeness of oil palm bunches.