Red Green Blue Color Research Articles

Filter paper-based optical sensor for the highly sensitive assessment of thorium in rock samples

Filter paper-based optical sensors (FPOS) offer rapid and sensitive quantification of analytes. Herein we report, a simple, sensitive, selective and low-cost FPOS for Th(IV) determination. Filter paper strips, impregnated with Morin reagent are reacted with Th(IV) yielding an intense yellow complex whose RGB (red/green/blue) color intensities made the basis of the assessment. A plot of the blue channel color absorbance versus Th(IV) concentration was linear up to 4.0 µg mL− 1 with a detection limit of 0.1 µg mL− 1. The proposed method was accurate, precise and reliable for analyzing complex certified rock samples in excellent compliance with the certified values.

Wide gamut, angle-insensitive structural colors based on deep-subwavelength bilayer media

Abstract Wide gamut and angle-insensitive structural colors are highly desirable for many applications. Herein, a new type of lithography-free, planar bilayer nanostructures for generating structural colors is presented, which is basically composed of a deep-subwavelength, highly absorbing dielectric layer on an opaque metallic substrate. Experimental results show that a galaxy of brilliant structural colors can be generated by our structures, and which can cover ∼50% of the standard red–green–blue color space by adjusting the nanostructure dimensions. The color appearances are robust with respect to the angle of vision. Theoretical partial reflected wave analyses reveal that the structural color effect is attributed to the strong optical asymmetric Fabry–Perot-type (F–P-type) thin-film resonance interference. The versatility of the structural color properties as well as the simplicity of their fabrication processes make this bilayer structures very promising for various applications, such as security marking, information encryption, and color display, etc.

Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and Pand P and K. The r esearchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.

Smartphone‐Based Colorimetric Method to Quantify Iron Concentration and to Determine the Nanoparticle Size from Suspensions of Magnetic Nanoparticles

AbstractAdvanced uses of smartphones are changing lifestyles, and may have a great impact in materials science in the near future. In this work, the use of these devices to develop fast, simple, and cheap methods to characterize magnetic nanoparticle suspensions is tested. A series of dilutions of a wide library of magnetic nanoparticles, composed of iron oxide materials in the range between 3 and 43 nm, with two different shapes and four different coatings is prepared. The colloid color is analyzed using the RGB (red, green, blue) color model. Ratios of these parameters are correlated with the suspension iron concentration and with the nanoparticles average size. A linear relationship between the color (in particular the G/R ratio) and both the colloid iron content and the particles size is found. The link between these parameters allows the development of two new methods to determine either the concentration or the particle size of magnetic nanoparticle suspensions just by acquiring images from suspensions of iron oxide magnetic nanoparticles with a smartphone.

Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection.

Computer-aided detection (CADe) systems play a crucial role in pulmonary nodule detection via chest radiographs (CXRs). A two-stage CADe scheme usually includes nodule candidate detection and false positive reduction. A pure deep learning model, such as faster region convolutional neural network (faster R-CNN), has been successfully applied for nodule candidate detection via computed tomography (CT). The model is yet to achieve a satisfactory performance in CXR, because the size of the CXR is relatively large and the nodule in CXR has been obscured by structures such as ribs. In contrast, the CNN has proved effective for false positive reduction compared to the shallow method. In this paper, we developed a CADe scheme using the balanced CNN with classic candidate detection. First, the scheme applied a multi-segment active shape model to accurately segment pulmonary parenchyma. The grayscale morphological enhancement technique was then used to improve the conspicuity of the nodule structure. Based on the nodule enhancement image, 200 nodule candidates were selected and a region of interest (ROI) was cropped for each. Nodules in CXR exhibit a large variation in density, and rib crossing and vessel tissue usually present similar features to the nodule. Compared to the original ROI image, the nodule enhancement ROI image has potential discriminative features from false positive reduction. In this study, the nodule enhancement ROI image, corresponding segmentation result, and original ROI image were encoded into a red-green-blue (RGB) color image instead of the duplicated original ROI image as input of the CNN (GoogLeNet) for false positive reduction. With the Japanese Society of Radiological Technology database, the CADe scheme achieved high performance of the published literatures (a sensitivity of 91.4 % and 97.1 %, with 2.0 false positives per image (FPs/image) and 5.0 FPs/image, respectively) for nodule cases.

Training Autoencoder using Three Different Reversed Color Models for Anomaly Detection

Autoencoders (AEs) have been applied in several applications such as anomaly detectors and object recognition systems. However, although the recent neural networks have relatively high accuracy but sometimes false detection may occur. This paper introduces AE as an anomaly detector. The proposed AE is trained using both normal and anomalous data based on convolutional neural network with three different color models Hue Saturation Value (HSV), Red Green Blue (RGB), and our own model (TUV). As a result, the trained AE reconstruct the normal images without change, whereas the anomalous image would be reconstructed reversely. The training and testing of the AE in case of RGB, HSV, and TUV color models were demonstrated and Cifar-10 dataset had been used for the evaluation process. It can be noticed that HSV color model has been more effective and achievable as an anomaly detector rather than other color models based on Z- and F-test analyses.

A single sensor based multispectral imaging camera using a narrow spectral band color mosaic integrated on the monochrome CMOS image sensor

A multispectral image camera captures image data within specific wavelength ranges in narrow wavelength bands across the electromagnetic spectrum. Images from a multispectral camera can extract a additional information that the human eye or a normal camera fails to capture and thus may have important applications in precision agriculture, forestry, medicine, and object identification. Conventional multispectral cameras are made up of multiple image sensors each fitted with a narrow passband wavelength filter and optics, which makes them heavy, bulky, power hungry, and very expensive. The multiple optics also create an image co-registration problem. Here, we demonstrate a single sensor based three band multispectral camera using a narrow spectral band red–green–blue color mosaic in a Bayer pattern integrated on a monochrome CMOS sensor. The narrow band color mosaic is made of a hybrid combination of plasmonic color filters and a heterostructured dielectric multilayer. The demonstrated camera technology has reduced cost, weight, size, and power by almost n times (where n is the number of bands) compared to a conventional multispectral camera.

Effective key‐frame extraction approach using TSTBTC–BBA

Video summarisation and key-frame extraction play a vital role in video processing and content-based video analysis. Key-frame extraction facilitates rapid browsing and proficient video ordering in numerous applications such as multimedia, gaming etc. The application of ‘effective key-frame extraction’ can be used in the field of object detection and tracking, and it is found to be helpful in providing some sort of signal to visually impaired persons. In this study, the advantages of Thepade's sorted ternary block truncation coding (TSTBTC) along with binary bat algorithm (BBA) are used to overcome the limitations by extracting an effective key frame from the videos. Static images in the form of frames are extracted from input video database and processed through TSTBTC algorithm for calculating similarity measures amongst two consecutive frames. The input data is processed using four colour spaces, namely red–green–blue colour space, Kekre's LUV colour space, YCbCr colour space, YUV colour space and BBA are used to optimise the threshold value. Furthermore, five similarity measures indices are used to calculate the number of frames, and the result obtained shows that TSTBTC–BBA algorithm deployed in YUV colour space provides better results compared with other existing techniques regarding precision, F-measure and colour spaces.

A Plasmonic Painter's Method of Color Mixing for a Continuous Red-Green-Blue Palette.

The ability of mixing colors with remarkable results had long been exclusive to the talents of master painters. By finely combining colors in different amounts on the palette, intuitively, they obtain smooth gradients with any given color. Creating such smooth color variations through scattering by the structural patterning of a surface, as opposed to color pigments, has long remained a challenge. Here, we borrow from the painter's approach and demonstrate color mixing generated by an optical metasurface. We propose a single-layer plasmonic color pixel and a method for nanophotonic structural color mixing based on the additive red-green-blue (RGB) color model. The color pixels consist of plasmonic nanorod arrays that generate vivid primary colors and enable independent control of color brightness without affecting chromaticity by simply varying geometric in-plane parameters. By interleaving different nanorod arrays, we combine up to three primary colors on a single pixel. Based on this, two- and three-color mixing is demonstrated, enabling the continuous coverage of a plasmonic RGB color gamut and yielding a palette with a virtually unlimited number of colors. With this multiresonant color pixel, we show the photorealistic printing of color and monochrome images at the nanoscale, with ultrasmooth transitions in color and brightness. Our color-mixing approach can be applied to a broad range of scatterer designs and materials and has the potential to be used for multiwavelength color filters and dynamic photorealistic displays.

Flexible, Large‐Area Covert Polarization Display Based on Ultrathin Lossy Nanocolumns on a Metal Film

AbstractCovert polarization displays provide a barrier to the inadvertent viewing of stored optical information. For security and anti‐counterfeiting purposes, access to concealed information without compromising packaging aesthetics is required in certain situations. However, optical conversion with polarized light typically requires sophisticated nanostructures only possible with limited materials, which are not appropriate for application to objects requiring flexibility or conformability, and color selectivity. A flexible, large‐area covert polarization display based on ultrathin lossy nanocolumns with wide color selectivity is presented. Self‐aligned porous nanocolumns (PNCs) fabricated by glancing angle deposition are a facile approach to polarization distinguishable structures. PNCs deposited on metal are designed to switch color in accordance with polarization by ultrathin resonance, which is modeled using the complex effective refractive index. Several combinations of material and thickness are presented to extend color selectivity with the standard red green blue gamut and color palette. As a demonstration, covert polarization display labels are attached to daily objects with curved and wrinkled surfaces, and hidden quick response codes are revealed by polarization adjustment in indoor and outdoor environments. Moreover, a multifunctional water contact detection covert polarization display is demonstrated based on the sensitivity of PNCs to the refractive index of the analyte.

Recognizing Induced Emotions With Only One Feature: A Novel Color Histogram-Based System

Emotions can be evoked in humans by images. Previous reports on Recognition of Emotions induced by Visual Content of images (REVC) mainly focused on numerous features to improve recognition performance. To devise a more robust REVC system, this paper examines the performance of a wide range of classifiers using color histogram as a single feature. Different numbers of color histogram bins in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces are considered in the examination and the overall classification performance is compared across the bin sizes. This investigation shows that features are not the only important factors affecting the performance of REVC systems, but also the type of classifiers and their parameters. This study shows that the HSV color space is better suited than the RGB color space for REVC systems. This paper proposes a new optimization algorithm called Optimizing Parameters of Ensemble RUSboosted Tree (OPERT) to boost the performance of the REVC system. Furthermore, a novel REVC system called Color histogram with Optimized RUSboosted Tree (CORT) is introduced. It is shown that our method is simpler, faster, and more efficient than the state-of-the-art, while providing comparable recognition performance. The robustness of the CORT system is validated over three different image datasets.

Electronic Circuit Failure Detection Using Thermal Image

This inspection of electronic circuits can be performed either by connection with voltage supply and without voltage supply. However, inspections during the working circuit may invite serious harm and damage to the components in the circuit. Usually, the method used is to measure the current and voltage values in the circuit based on the reading reference when the circuit is in normal condition. There is another method that can help check the current circuit condition by using image processing method. In this paper, we presented our study on electronic component failure detection using thermal image processing. The thermal image was collected from linear regulated dc power supply. The observation is focused on the thermal produce by capacitor and voltage regulator IC. The image has been analysed using Red Green Blue (RGB) and Hue Saturation Value (HSV) colour space. The pixel of colour was analysed using mean value. The result showed there is significant different heat produced by the component in normal condition and failure condition. Through the observation, it was found that the high heat was recorded if wrong polarity of the components in the circuit.

Tracking and recognition algorithm for a robot harvesting oscillating apples

Apple fruits on trees tend to swing because of wind or other natural causes, therefore reducing the accuracy of apple picking by robots. To increase the accuracy and to speed up the apple tracking and identifying process, tracking and recognition method combined with an affine transformation was proposed. The method can be divided into three steps. First, the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue (2R-G-B) color feature; after improving the binary image, the apples were recognized with a local parameter adaptive Hough circle transformation method, thus improving the accuracy of recognition and avoiding the long, time-consuming process and excessive fitted circles in traditional Hough circle transformation. The process and results were verified experimentally. Second, the Shi-Tomasi corners detected and extracted from the first frame image were tracked, and the corners with large positive and negative optical flow errors were removed. The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm (RANSAC) to correct the scale of the template image and predict the apple positions. Third, the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm. The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8% and 20% in the absence of template correction and apple position prediction, respectively. In comparison, the running time of our algorithm was 25 ms, and the tracking error was less than 4%. Therefore, test results indicate that speed and efficiency can be greatly improved by using our method, and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits. Keywords: apple picking robot, tracking and recognition algorithm, oscillating apple, Hough transform, pyramid LK optical flow algorithm, affine transform, template matching DOI: 10.25165/j.ijabe.20201305.5520 Citation: Yang Q H, Chen C, Dai J Y, Xun Y, Bao G J. Tracking and recognition algorithm for a robot harvesting oscillating apples. Int J Agric & Biol Eng, 2020; 13(5): 163–170.

Improving Performance of the Multiplexed Colored QR Codes

The vast popularity and useful applications of the QR code were the incentives that encourage the research towards improving its storage capacity and performance. A colored quick response (QR) algorithm is proposed, devised and tested to expand the capacity of the black and white modules to hold colored modules that can fold as many as those available in the 8 bits red-Green-Blue (RGB) color code. Fast Multiplexing Technique (FMT) is established to improve the performance and the storage capacity of the QR color code by multiplexing the black/white QR codes into RGB color shades then folding them into the QR code Modules. Comparative experiments with the classical multiplexing technique -MUX system- proved that FMT has a much better performance, (exponentially faster), while maintaining the capacity multitude to 24 folds that of the classical QR code.

An FPGA-Based Simple RGB-HSI Space Conversion Algorithm for Hardware Image Processing

In this paper, a new, low-complexity, easy-to-implement hardware method for color space conversion between the red-green-blue (RGB) and the hue-saturation-intensity (HSI) color spaces called the simple RGB-HSI space conversion (S-SC) algorithm is proposed, which aims to provide more rapid computing due to the need for fewer operations. In the S-SC algorithm, we reconstruct the model of space conversion between the RGB color space and the HSI color space (RGB-HSI) by inverting the conversion from the HSI color space to the RGB color space of the traditional geometric derivation algorithm. As a result, the nonlinear model-realized RGB-HSI color space conversion by the geometric derivation algorithm is transformed into a linear conversion model, which can avoid complicated calculations such as trigonometric and inverse trigonometric functions in the color space conversion process. The model can effectively reduce the computational complexity of the algorithm and facilitate hardware implementation at the same time. To evaluate the performance of the S-SC algorithm, we first compare the S-SC algorithm with the geometric derivation algorithm from the computational complexity perspective. On this basis, we compare the S-SC algorithm with five other RGB-HSI color space conversion algorithms from the perspectives of error and conversion effect. Finally, we use the field programmable gate array (FPGA) hardware platform to analyze and verify the timing sequence and logical resource consumption and verify the effectiveness of the proposed algorithm with experimental results. We show that the S-SC algorithm achieves good performance in terms of conversion accuracy, logical unit resource occupancy, and output timing.

Application of Seismic Attribute and Spectral Decomposition: Example of Fluvial System During Miocene in Field A, Malay Basin

Stratigraphic channels in field A Malay Basin including those with reservoir potential have been defined seismically. There were two exploration wells in field a however, the wells were abandoned due to low structural relief and volume were below economic criteria. This study discusses how seismic attribute and spectral decomposition give geomorphological information in different stratigraphic levels of Miocene age at Top F, H, I, Group I-50 and I-60. Facies analysis, seismic attribute, spectral decomposition and colour blending were implemented to identify hydrocarbon potential in stratigraphic traps and to enhance seismic resolution. Channel anomalies can be identified in seismic reflections characterized by high-amplitude events with chaotic reflections. Numerous sub-parallel reflection with high amplitude is observed in the upper formation. Several seismic attributes were used from the 3D seismic dataset to identify the geomorphologic features, potential reservoir variations, besides distribution and to extract the anomalies. Physical attributes which are related to amplitude, frequency, phase and their derivative were used including variance, instantaneous frequency, sweetness and spectral decomposition. Sweetness is the combination of lateral continuity and reflection strength. High reflection strength indicates maximum amplitude of the largest peak or through of real trace. This is especially, true when an event is a composite reflection. Amplitude attribute provides better spatial definition of events and bright spots could be enhanced. Instantaneous frequency shows composite frequency of the individual reflections contributing to a particular reflector. This composite is a useful correlation tool. Rapid changes in frequency are sometimes indicator of pinchouts such as oil/gas and water contacts. Low frequency anomalies are observed in zones below hydrocarbon reservoirs. Meanwhile, instantaneous phase is useful in stratigraphic analysis of certain sections because the sharp zero crossings that results may emphasize lateral discontinuities, pinchouts and angularities. The combination of attribute analysis and spectral decomposition aids the interpretation and identification of some channel patterns and channel distribution. Two methods of spectral decomposition are used Short Time Fourier Transform (STFT) and S-transform which focus on spatial and temporal range, respectively. Red Green Blue (RGB) colour blending represents different frequency ranges of low, mid and high frequency bands and display as a single image. This method reveals additional information such as depositional elements, anomaly identification, bed thickness, presence of fluids and help to resolve petrophysical heterogeneity. This study reveals that there are strong evidences of hydrocarbon potential in stratigraphic traps for further exploration within the study area. © Medwell Journals, 2019.

Utilization of color change and image processing to evaluate the Waste Foundry Sand reclamation level

Waste Foundry Sand (WFS) is the by-product of casting industries and is utilized to make molds. Some minerals, such as clay particles or chemical admixtures such as phenolic resins, are used to bond silica sand grains and shape the mold. Preventing environmental impacts such as WFS landfilling, many researches have been conducted to find an appropriate way for reclamation and evaluation of its cleaning. Due to the color changes during this process, image processing is helpful for an estimation of reclamation progress. In this paper, thermal reclamation method and Response Surface Methodology (RSM) are used as the most appropriate way of reclamation and design of experiments, respectively. Samples are heated at different temperatures for a variety of time durations. The color changes of samples are measured using image processing technique by detection of RGB (Red, Green, Blue) color coordinate system parameters changes (ΔRGB). The results of TCLP (Toxicity Characteristic Leaching Procedure) tests reveal that phenolic compounds are removed during the reclamation process, which causes WFS color changes from black to almost white. On this basis, a linear model is developed to predict RGB values relative to time or temperature variations. Results approve that an investigation on ΔRGB values provides a precise estimation of WFS grains reclamation level (less than 10 % error) to guide its reuse within the foundry industries.

Synthesis and Characterization of Novel D-A Type Neutral Blue Electrochromic Polymers Containing Pyrrole[3-c]Pyrrole-1,4-Diketone as the Acceptor Units and the Aromatics Donor Units with Different Planar Structures.

Three soluble conjugated polymers, named BEDPP, FLDPP, and CADPP, were prepared through the Suzuki polymerized reaction, and employed benzene (BE), fluorene (FL), and carbazole (CA) as the donor units, respectively. The electron-deficient molecule 2,5-bis-(2-octyldodecyl)-3,6-bis-(5-bromo-thiophene)-pyrrole[3-c]pyrrole-1,4-diketone(DPP) was introduced and used as the acceptor unit. The properties of these three copolymers were studied by a series of detailed characterization analysis, including X-ray photoelectron spectroscopy (XPS), colorimetry, electrochemical measurements, spectroelectrochemistry, kinetics, quantitative calculation, and thermogravimetric (TG) analysis, etc. The results revealed that BEDPP displayed a blue color in the neutral state and a light brown color in the oxidized state, FLDPP exhibited a cyan color in the neutral state and a gray color in the oxidized state, while CADPP displayed pure blue color in the neutral state and a light gray color in the oxidized state. All these polymers possess narrow optical band gaps lower than 1.80 eV and satisfactory thermal stability. The kinetic characterization showed that the optical contrasts (ΔT%) in the near-infrared region were superior to the visible region. The optical contrasts of BEDPP, FLDPP, and CADPP are 41.32%, 42.39%, and 45.95% in the near-infrared region, respectively, which made them a good application prospect in the near-infrared region. Amid the three polymers, CADPP has the highest coloration efficiency (around about 288 cm2·C−1) and fast switching times (0.77 s in the coloring process and 0.52 s in the bleaching process) in the visible region, and the comprehensive performance of CADPP can be comparable to that of the reported D-A (Donor-Acceptor) type blue color polymers. In general, based on the good performances and the stable neutral blue color, the three polymers had profound theoretical significance for the development of electrochromic material and the completion of the RGB (Red, Green, Blue) color space.

Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features.

Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occurrence matrix (GLCM) methods were used to extract the most significant features for multilayer perceptron (MLP) neural network classification. Haar wavelet transformation was carried out to extract GLCM texture features, and colour features were extracted from RGB (red/green/blue) colour images of prostate tissues. The MANOVA statistical test was performed to select significant features based on F-values and P-values using the R programming language. We obtained an average highest accuracy of 92.7% using level-1 wavelet texture and colour features. The MLP classifier performed well, and our study shows promising results based on multi-feature classification of histological sections of prostate carcinomas.

Chromaticity Measurement Based on the Image Method and Its Application in Water Quality Detection

In order to measure the chromaticity of water and the content of dissolved matter more accurately, effectively, and cheaply, a chromaticity measurement system based on the image method was proposed and applied. The measurement system used the designed acquisition device and image processing software to obtain the Red-Green-Blue (RGB) values of the image and converted the color image from RGB color space to Hue-Saturation-Intensity (HSI) space to separate the chromaticity and brightness. According to the definition of chromaticity, the hue (H), saturation (S) values, and chromaticity of standard chromaticity solution images were fitted by a non-linear surface, and a three-dimensional chromaticity measurement model was established based on the H and S values of water images. For the measurement of a standard chromaticity solution, the proposed method has higher accuracy than spectrophotometry. For actual water sample measurements, there is no significant difference between the results of this method and the spectrophotometer method, which verified the validity of the method. In addition, the system was tried to measure the concentration of ammonia nitrogen, phosphate, and chloride in water with satisfactory results.