RGB Method Research Articles

RGB color camera for dynamical measurements of high temperature distribution on a surface of the heated solid

In this report we describe a fast 3-color method of the measurement of temperature distributions on a surface of a heated solid using a RGB color camera with a high frame rate (100 images per second). Statistical error the RGB method is not high, and do not exceed around 5.5% which is surprising taking in to account the number of the measurements at each pixel. Comparison of the results of the temperature measurements on a tungsten plate heated by infra-red laser radiation and conducted with this technique and those obtained with the acousto-optical tunable filter technique demonstrate that error of the temperature measured by 3-color method is only two times as high as that of the tandem acousto-optic filter technique method.

Research on Waste Plastics Classification Method Based on Multi-Scale Feature Fusion.

Microplastic particles produced by non-degradable waste plastic bottles have a critical impact on the environment. Reasonable recycling is a premise that protects the environment and improves economic benefits. In this paper, a multi-scale feature fusion method for RGB and hyperspectral images based on Segmenting Objects by Locations (RHFF-SOLOv1) is proposed, which uses multi-sensor fusion technology to improve the accuracy of identifying transparent polyethylene terephthalate (PET) bottles, blue PET bottles, and transparent polypropylene (PP) bottles on a black conveyor belt. A line-scan camera and near-infrared (NIR) hyperspectral camera covering the spectral range from 935.9 nm to 1722.5 nm are used to obtain RGB and hyperspectral images synchronously. Moreover, we propose a hyperspectral feature band selection method that effectively reduces the dimensionality and selects the bands from 1087.6 nm to 1285.1 nm as the features of the hyperspectral image. The results show that the proposed fusion method improves the accuracy of plastic bottle classification compared with the SOLOv1 method, and the overall accuracy is 95.55%. Finally, compared with other space-spectral fusion methods, RHFF-SOLOv1 is superior to most of them and achieves the best (97.5%) accuracy in blue bottle classification.

RGB colorimetric method based detection of oxytocin in food samples using cysteamine functionalized gold nanoparticles

In the present investigation, a colorimetric method has been demonstrated for detection of oxytocin using Cysteamine functionalized gold nanoparticles (Cys-AuNP). The analyte oxytocin which finds its use in medical field is purposefully injected in fruits and vegetables for their faster growth. Also, it is used in the milk letdown in cattles. The complexity of segregating oxytocin induced fruit and vegetables from naturally grown ones led to development of the method. The choice of spherical gold nanoparticles for development of sensor was influenced from its unique optical properties. Moreover, functionalization with cysteamine increased its sensitivity towards oxytocin detection. The method was coupled with RGB colorimetric technique which exhibited excellent linear correlation between Oxytocin concentration and RGB color values (R2 = 0.96). The RGB method used measured the intensity of color and a relation was established between measured RGB values and concentration of analyte (Oxytocin) present in the sample. This formed the basis for generation of simple colorimetric card that can be used to relate the color with amount present in the sample. More significantly, the Cys-AuNP shows excellent selectivity towards other coexisting substances present in the sample. This method with good precision (RSD>15%) offers suitability for onsite application without need for complex instrumentation.

Real Time Object Detection Using Deep Learning

Abstract: Visually impaired people have difficulty moving safely and independently, which interferes with normal indoor and outdoor work and social activities. Similarly, they have a hard time identifying the basics of the environment. This paper presents a model for detecting the brightness and key colors of real-time images using the RGB method with an external camera and identifying basic objects and face recognition from human datasets.[2]. Object detection is a department of pc imaginative and prescient that appears for times of lexical entities in photographs and videos. The gadget makes use of the ESP-32 Cam's digital digicam to continuously seize severa frames, which can be sooner or later converted to audio segments. In this project, we use the You Only Look Once V3 (YOLO v3)algorithm, which runs thru a version of a really complex Convolutional Neural Network structure with OpenCV. Then with the aid of using the usage of Google Text to Speech, we convert the photo to textual content and afterwards textual content - to - speech for the visually impaired individual. Thus, the Visually Impaired individual receives the place of the gadgets withinside the digital digicam's view through audio. Distance calculation is aided with the aid of using an ultrasonic sensor. By the usage of The amassed consequences show that the proposed prototype is a hit in presenting visually impaired customers with the cappotential to realise surprising settings the usage of a user-pleasant machine that integrates this unique item detection Model

DEVELOPING DESIGN OF AUTOMATIC EGG QUALITY DETECTOR USING ROI AND RGB TEMPLATE METHODS

The eggs of purebred chickens always become the major choice for satisfying people’s needs for nutrients. The way to process them is easy, and the prices are relatively cheap for all people. Since the eggs of purebred chickens are in high demand, a device that can detect their qualities more correctly and accurately is needed, compared to the conventional method. Objective: An automatic egg quality detector was developed through a camera module of pixy cmucam5 by employing ROI and template RGB methods. The implementation of the ROI method could optimize the system performance, thereby accelerating the process of egg quality detection carried out by the preprocessor of the pixy camera. ROI analyzed the egg image only on the desired pixel area, not the whole pixel. As a result, the ROI method yielded a device and system that could detect the egg quality within the average speed of detection time -/+ 2ms per egg out of 4 trial categories. Meanwhile, the template RGB method was able to improve and ease the ability of the pixy camera module in detecting the egg quality. When the RGB index of the egg was < 234, then the egg was of good quality. Contrarily, when the RGB index of the egg was > 234, the egg quality was in a bad category. The contribution of this paper is to develop a low-cost automatic egg sorting and detection system with the advantages of flexibility in device placement, due to the use of camera modules and object detection accuracy, as well as better response time than using color sensors. Accordingly, the template RGB method produced a device and system which could detect egg quality with average accuracy of 90 % and average error of 10% out of 4 trial categories. And, the results of the trials upon the detector system indicated success in detecting the egg quality.

Multicolor fluorescent probe for visual point-of-care detection of water via a smartphone

The application of multicolor fluorescent probes in visual detection of targets has attracted considerable interest. Herein, a multicolor fluorescent probe (G/R-CDs) was fabricated by combining green carbon dots (G-CDs) with red carbon dots (R-CDs). As water content increased, the fluorescence of R-CDs at 683 nm was quenched significantly, resulting in the decrease of red fluorescence. To improve the visualization effect, G-CDs were introduced. The fluorescence peak of G-CDs was red shifted from 500 nm to 535 nm, and the fluorescence intensity was basically unchanged with addition of water. Therefore, G-CDs and R-CDs served as reference signal and response signal, respectively. The fluorescence intensity ratio (F535/F683) showed a good linear relationship with water content in the ranges of 3%-30% and 40%-60% with a low detection limit of 1.2%. More importantly, G-CDs also acted as a toner to enrich the fluorescence color of G/R-CDs. Under the toning of G-CDs, G/R-CDs presented a variety of fluorescent colors such as pink, orange, gray and green towards different water contents. Based on the rich color changes, the visual analysis was achieved by the naked eye. In addition, a smartphone was used to read out the RGB values for establishing the RGB method. The method was successfully applied for the detection of water in medical alcohol and commercial liquor, showing good practicability in visual point-of-care detection of water.

An Action Recognition Method for Rgb and Infrared Videos Based on Knowledge Distillation

An Action Recognition Method for Rgb and Infrared Videos Based on Knowledge Distillation

A quinoline-benzothiazole-based chemosensor coupled with a smartphone for the rapid detection of In3+ ions.

A newly designed quinoline-benzothiazole probe 2-((Z)-((E)-benzo[d]thiazol-2(3H)-ylidenehydrazono)methyl)quinolin-8-ol (L) was synthesized by reacting 8-hydroxyquinoline-2-carbaldehyde with 2-hydrazinobenzothiazole and structurally characterized by various spectroscopic techniques. The sensing ability of probe L was studied with various cations using colorimetry, test paper strips, a red-green-blue (RGB) model and UV-visible spectrophotometry in DMSO : H2O (3 : 7, v/v). The pale yellow colour of L turns into orange on contact with In3+ ions, whereas other tested metal ions did not show any change in colour. The probe L exhibits an absorbance band at 360 nm due to ligand-to-ligand charge transfer (LLCT); upon interaction with In3+ ions, it exhibits a band at 445 nm due to ligand-to-metal charge transfer (LMCT). The probe L binds In3+ in a 2 : 1 ratio with an association constant of 8.1 × 105 M-1 and this is established using the Job's and Benesi-Hildebrand (B-H) methods. The probe L can work in the pH range of 4-8 without interfering with other competing ions. It can be used to detect quantities as low as 2.3 ppb and 85 ppb by spectrophotometry and RGB, respectively. The binding mechanism was studied by 1H NMR titration, ESI mass and FT-IR spectral analysis and well supported by theoretical studies. Overall, probe L demonstrates promising potential for the detection of In3+ ions in the semi-aqueous phase and this is its first report as a colorimetric chromogenic probe.

Microstructural pore analysis using SEM and ImageJ on the absorption of treated coconut shell aggregate

Six treatments on coconut shell aggregate were studied. Through scanning electron microscopy and ImageJ software, the microstructural analysis on coconut shell aggregate has been studied. Sizes of ImageJ used the total area under study, total pore area from both 8-bits and RGB methods, and the pore area % were determined. Results on water absorption from treatments adopted are concurrence to the presence of the pores in the treated coconut shell aggregates. It is possible to bring down the 0% water absorption of coconut shell from its natural range from 20 to 25% by proper treatment. The order of performance improved by the six materials used for treating the coconut shells are acetic acid, sago flour, slaked lime, ferrous sulfate, polyvinyl alcohol, and cornflour. Combining both performance and cost among these, then the two best treatment materials to treat coconut shells are the use of sago flour and slaked lime.

Abnormal thermal quenching behavior and optical properties of a novel apatite-type NaCa3Bi(PO4)3F:Sm3+ orange-red-emitting phosphor for w-LED applications

Novel apatite-type NaCa3Bi(PO4)3F:xSm3+ (0.01 ≤ x ≤ 0.30) orange-red-light phosphors were synthesized through the solid-state method at high temperature. The crystal structure, energy band structure, density of state, phase purity, particle morphology, photoluminescence properties, thermostability, and luminescence decay of the phosphors were comprehensively characterized. When λex = 404 nm, the optimal NaCa3Bi(PO4)3F:0.05 S m3+ phosphor showed the orange-red emission (597 nm). The NaCa3Bi(PO4)3F:Sm3+ phosphors exhibited abnormal thermal quenching properties as their emission intensity increased by about 2.57% from 300 to 380 K. Their intensity at 440 K was still 1.01-fold stronger than that at room temperature. The abnormal thermal quenching mechanisms were well explained via the coordinate configuration scheme. The thermal activation energy (Ea) was calculated to be 0.79 eV. The color purity of all the phosphors reached 99.9%. Ultimately, a white light-emitting diode (w-LED) was fabricated based on the tri-color RGB method. The color rendering index and the chromaticity coordinates of the fabricated w-LED were 89 and (0.310, 0.319), respectively. Thus, these high thermostability NaCa3Bi(PO4)3F:Sm3+ orange-red phosphors can be potentially used in w-LED applications.

A Preliminary Damage Assessment Using Dual Path Synthetic Aperture Radar Analysis for the M 6.4 Petrinja Earthquake (2020), Croatia

On 29 December 2020, an earthquake with a magnitude of M 6.4 hit the central part of Croatia. The earthquake resulted in casualties and damaged buildings in the town of Petrinja (~6 km away from the epicenter) and surrounding areas. This study aims to characterize ground displacement and to estimate the location of damaged areas following the Petrinja earthquake using six synthetic aperture radar (SAR) images (C-band) acquired from both ascending and descending orbits of the Sentinel-1 mission. Phase information from both the ascending (Sentinel-1A) and descending (Sentinel-1B) datasets, acquired from SAR interferometry (InSAR), is used for estimation of ground displacement. For damage mapping, we use histogram information along with the RGB method to visualize the affected areas. In sparsely damaged areas, we also propose a method based on multivariate alteration detection (MAD) and naive Bayes (NB), in which pre-seismic and co-seismic coherence maps and geocoded intensity maps are the main independent variables, together with elevation and displacement maps. For training, approximately 70% of the data are employed and the rest of the data are used for validation. The results show that, despite the limitations of C-band SAR images in densely vegetated areas, the overall accuracy of MAD+NB is ~68% compared with the results from the Copernicus Emergency Management Service (CEMS).

Classification of color features in butterflies using the Support Vector Machine (SVM)

Research in digital images is expanding widely and includes several sectors. One sector currently being carried out research is in insects; specifically, butterflies are used as a dataset. A total of 890 types of butterflies divided into ten classes were used as a dataset and classified based on color. Ten types of butterflies include Danaus plexippus, Heliconius charitonius, Heliconius erato, Junonia coenia, Lycaena phlaeas, Nymphalis antiopa, Papilio cresphontes, Pieris rapae, Vanessa atalanta, Vanessa cardui. The process of extracting color features on butterfly wings uses the RGB method to become HSV color space with color quantization (CQ). The purpose of adding CQ is that the computation process is carried out faster without reducing the image's information. In the color feature extraction process, the image is converted into 3-pixel sizes and normalized. The process of normalizing the dataset has the aim that the value ranges in the dataset have the same value. The 890 butterfly dataset was classified using the Support Vector Machine (SVM) method. Based on this research process, the accuracy of the 256x160 pixel size is 72%, the 420x315 pixel is 75%, and the 768x576 pixel is 75%. The test results on a system with a 768x576 pixel get the highest results with a precision value of 74.6%, a recall of 72%, and an f-measure of 73.2%
 Keywords—image processing; classification; butterflies; color features; features extraction

Klasifikasi Curah Hujan Menggunakan Neuro-Fuzzy System Melalui Citra Radar Cuaca

Rainfall intensity can be measured one of them through the reading of the reflectivity of raindrops on the weather radar. Reflectivity values are represented through colors in the visualization of two-dimensional radar images. Based on several approaches to the classification of weather conditions through radar data that has been successfully carried out, a system is designed to classify rainfall according to weather conditions in an area by utilizing weather radar imagery.The system implementation is carried out in several stages, namely pre-processing, feature extraction and labeling, and classification. Pre-processing is done to visualize radar data from Yogyakarta Climatology Station into a two-dimensional image. After capturing features using the RGB and HSV methods and labeling the rain class, classification is performed using the Neuro-fuzzy algorithm with the Adaptive Neuro-fuzzy Inference System (ANFIS) architecture. The results showed that the Neuro-fuzzy System algorithm was able to classify rainfall better on the RGB feature with an accuracy of 85.02% and a precision of 86.19%, while for the HSV feature the accuracy was 82.68%, 86.67% precision.

Design of Intelligent Assistant System for Billiards Hit Training

Image processing technology has increasing important applications in sports in recent years, such as result prediction, technical analysis, and foul determination. For most billiards beginners, how to hit the billiard ball into the ball belt is not a simple matter. In order to assist the beginners in billiards training, this article proposes an assisted hitting system. The system is divided into three parts: bag detection, billiard detection, and hitting suggestions. In the ball belt detection, double-peak histogram threshold segmentation, edge detection and Hough transform are used to detect the edge line, and then the ball belt position is detected. In billiard ball detection, the normalized RGB method is used to extract the foreground, the Hough transform circle detection method is used to detect the center and radius of the sphere, and the neural network is used to determine the white ball. In the shot suggestion, the force and angle of the shot are given by the dynamic formula, and the path simulation of the ball in the background is given. Experiments show that the recognition method is effective and the path simulation is consistent with reality, which can assist beginners in the training of billiards to a large extent.

Fully Automatic Multi-Object Articulated Motion Tracking

Fully automatic tracking of articulated motion in real-time with a monocular RGB camera is a challenging problem which is essential for many virtual reality (VR) and human-computer interaction applications. In this paper, we present an algorithm for multiple articulated objects tracking based on monocular RGB image sequence. Our algorithm can be directly employed in practical applications as it is fully automatic, real-time, and temporally stable. It consists of the following stages: dynamic objects counting, objects specific 3D skeletons generation, initial 3D poses estimation, and 3D skeleton fitting which fits each 3D skeleton to the corresponding 2D body-parts locations. In the skeleton fitting stage, the 3D pose of every object is estimated by maximizing an objective function that combines a skeleton fitting term with motion and pose priors. To illustrate the importance of our algorithm for practical applications, we present competitive results for real-time tracking of multiple humans. Our algorithm detects objects that enter or leave the scene, and dynamically generates or deletes their 3D skeletons. This makes our monocular RGB method optimal for real-time applications. We show that our algorithm is applicable for tracking multiple objects in outdoor scenes, community videos, and low-quality videos captured with mobile-phone cameras. Keywords: Multi-object motion tracking, Articulated motion capture, Deep learning, Anthropometric data, 3D pose estimation. DOI: 10.7176/CEIS/12-1-01 Publication date: March 31 st 2021

The influence of elastomeric ligatures pigmentation on smile aesthetics during orthodontic treatment.

ABSTRACTAim: To assess the influence of elastomeric ligatures, subjected to a previous in vitro pigmentation process using different substances, on smile aesthetics during orthodontic treatment, from the perception of students and professionals. Methods: Eight elastomeric ligatures of five commercial brands (3M/Unitek, American Orthodontics, Morelli, Ortho Technology, and Orthometric) (n=8) were immersed in coffee, Coca-Cola, and red wine for one minute per day, for 28 days; and another group of ligatures was immersed in artificial saliva. All samples were photographed and subsequently analyzed using the Adobe Photoshop software, by the RGB method. Afterwards, the pigmented ligatures were inserted in a patient wearing orthodontic brackets, and zoomed photographs of the smile were taken and presented to 40 evaluators, who filled in a satisfaction scale sheet to express their opinion on the smile aesthetics of each photograph. The color data were subjected to analysis of variance (ANOVA) and Tukey tests. Results: The substance with the highest pigmentation potential was coffee (p< 0.05) followed by red wine (p< 0.05). Comparison among the brands used in this study showed that American Orthodontics and Orthometric had the lowest degree of pigmentation when immersed in coffee and red wine (p< 0.05), respectively. However, the brand that showed the highest level of satisfaction among the evaluators was Ortho Technology. Conclusions: The presence of pigmented elastomeric ligatures affected smile aesthetics, when compared with the control group.

Influence of surface object movement parameters on the hydroacoustic RGB classification method

This article presents the influence of floating objects motion parameters on the hydroacoustic RGB classification method. The data analysis was obtained through in-situ measurements by the IGLOO sensor nodes. This hardware was engineered in the Polish Naval Academy as part of the European Defense Agency’s (EDA) project ‘SIRAMIS’, realized between 2012 and 2015. The purpose of the analysis of the data obtained is the classification of identified underwater or surface objects in order that several extracted RGB parameters could be calculated and sent in real time using a low-rate information underwater communication modem.The analysis used the RGB method which is based on a one-third-octave spectrum, and raw and normalized data of one object. Moreover, the distinctive feature was selected based on the results of the calculations, and discussed depending on the position (distance) of the detected object relative to the detector.The important question of the data analysis is whether the distance of the examined object from the measurement sensor has an impact on the resolution in the RGB method, known as “acoustical colours“.

A novel color calibration method of multi-spectral camera based on normalized RGB color model

True-color image of Mars is important for mineral analysis. The working light source of the Mars exploration multi-spectral camera is different from the laboratory calibration light source, so that the commonly used RGB calibration method has a large color difference in correcting the on-orbit images. After analyzing the influence of light source lightness and relative spectral distribution on the camera output values, their influences are considered separately in the calibration. A new color calibration method in the luminance-independent chromaticity space is proposed. The RGB color components of multi-spectral camera are normalized and converted to chromaticity values, and the polynomial calibration model in chromaticity space is compared with the RGB method. The experimental results showed that the average value of CIE DE2000 color differences after color calibration was 4.32, which was 0.96 less than that of RGB method. When the calibration matrix was used to correct the outdoor scene images, the corrected color difference gain because of the change of imaging light source was reduced by 0.54 compared to the RGB method. The color calibration method and the calibration data in this paper provide theoretical support for the color correction of the Mars multi-spectral camera on-orbit images.

Colorimetric Method for the Determination of Proteins Using Immobilized Microbial Protease and a Smartphone Camera

The determination of proteins is very important in industry, medicine, analytical chemistry, and biotechnology. A novel colorimetric method using magnetic microparticles as a carrier of immobilized microbial protease on surface was developed and smartphone camera was employed for detection. Folin and Ciocalteu’s phenol reagent was selected as the chromogen and casein from bovine milk was used as the substrate. Casein was cleaved by immobilized enzyme to tyrosine, which together with chromogen spontaneously generated a blue color measurable by a spectrophotometer or photographic detection by a smartphone camera. The photographs were processed by a computer and selected points were transformed into RGB numeric values. A reference spectrophotometric method provided results that provide a Michaelis–Menten dependence with the Michaelis–Menten constant equal to 0.11 mmol/l and a limit of detection equal to 41 µg/ml. The novel method using spectrophotometric detection shows the same Michaelis–Menten dependence with a Michaelis–Menten constant equal to 0.11 mmol/l with a limit of detection of 4.27 µg/ml. The RGB method using a smartphone for the determination of proteins was demonstrated to be an alternative to the standard spectrophotometer. The limit of detection was 242 µg/ml, which is sufficient for protein determination in real samples. Easy processing, good analytical results, and low demands on equipment make this method a suitable tool for a wide range of applications in industry, analytical chemistry, medicine, biotechnology, and biosensors.

Quantification of photoelastic fringe orders using polarized light camera and continuous loading

Quantification of fringe orders is important in visualizing full-field stress in digital photoelasticity. Various methods have been developed to improve the calculated accuracy of fringe orders, such as the phase shifting, carrier, and RGB methods. However, in these methods, fringe orders are calculated using the photoelastic patterns under a certain load but not the patterns in the whole time series; this impedes quantification of the fringe orders in dynamic photoelasticity. In this study, a polarized light camera was employed to simultaneously capture the photoelastic patterns of the dark and bright fields under circular polariscope. Based on the light intensity difference of these patterns in the entire loading process, a method of quantifying fringe orders is proposed. Full-field fringe orders in a disk fabricated by 3D printer and subjected to three compressive loads were calculated using this method. Its good accuracy, improved by removing the background light and choosing a suitable capture frame rate, was verified by comparing the fringe orders calculated by the traditional and proposed methods. Furthermore, the determination of the full-field fringe orders in complex structures indicated that the complexity of tested models had no influence on the calculated accuracy.