Red Green Blue Color Space Research Articles

Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition

In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using the gray-centered red-green-blue (RGB) color space. The developed method presents a multi-feature selection process, which eliminates the effect of halation and shadows in apple images. By exploring all the features of the image, including halation and shadows, in the gray-centered RGB color space, the proposed algorithm, which is a generalization of K-means clustering algorithm, provides an efficient target segmentation result. The proposed method is tested on 240 apple images. It offered an average accuracy rate of 98.79%, a recall rate of 99.91%, an F1 measure of 99.35%, a false positive rate of 0.04%, and a false negative rate of 1.18%. Compared with the classical segmentation methods and conventional clustering algorithms, as well as the popular deep-learning segmentation algorithms, the proposed method can perform with high efficiency and accuracy to guide robotic harvesting.

A Watershed-Segmentation-Based Improved Algorithm for Extracting Cultivated Land Boundaries

To accurately extract cultivated land boundaries based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm was proposed herein based on a combination of pre- and post-improvement procedures. Image contrast enhancement was used as the pre-improvement, while the color distance of the Commission Internationale de l´Eclairage (CIE) color space, including the Lab and Luv, was used as the regional similarity measure for region merging as the post-improvement. Furthermore, the area relative error criterion (δA), the pixel quantity error criterion (δP), and the consistency criterion (Khat) were used for evaluating the image segmentation accuracy. The region merging in Red–Green–Blue (RGB) color space was selected to compare the proposed algorithm by extracting cultivated land boundaries. The validation experiments were performed using a subset of Chinese Gaofen-2 (GF-2) remote sensing image with a coverage area of 0.12 km2. The results showed the following: (1) The contrast-enhanced image exhibited an obvious gain in terms of improving the image segmentation effect and time efficiency using the improved algorithm. The time efficiency increased by 10.31%, 60.00%, and 40.28%, respectively, in the RGB, Lab, and Luv color spaces. (2) The optimal segmentation and merging scale parameters in the RGB, Lab, and Luv color spaces were C for minimum areas of 2000, 1900, and 2000, and D for a color difference of 1000, 40, and 40. (3) The algorithm improved the time efficiency of cultivated land boundary extraction in the Lab and Luv color spaces by 35.16% and 29.58%, respectively, compared to the RGB color space. The extraction accuracy was compared to the RGB color space using the δA, δP, and Khat, that were improved by 76.92%, 62.01%, and 16.83%, respectively, in the Lab color space, while they were 55.79%, 49.67%, and 13.42% in the Luv color space. (4) Through the visual comparison, time efficiency, and segmentation accuracy, the comprehensive extraction effect using the proposed algorithm was obviously better than that of RGB color-based space algorithm. The established accuracy evaluation indicators were also proven to be consistent with the visual evaluation. (5) The proposed method has a satisfying transferability by a wider test area with a coverage area of 1 km2. In addition, the proposed method, based on the image contrast enhancement, was to perform the region merging in the CIE color space according to the simulated immersion watershed segmentation results. It is a useful attempt for the watershed segmentation algorithm to extract cultivated land boundaries, which provides a reference for enhancing the watershed algorithm.

FACEMASK RECOGNITION IN PANDEMIC CAUSES MULTIPLE HEAD CRUCIAL PROBLEM CLASSIFICATION

—A chronicle respiratory system condition like Breathing issues to Asthma patients after covering his/her mouth and nose make a tough challenge. Mainly the physical barrier are to take breath in oxygen, it also pack CO2 which the person exhale. A mask can feel high suffocation and add a compromised Head crucial problem stratification is in general used for the fore handling ahead of the facial acknowledgement & facial several inclination issues and only for this reason an algorithm like recognition of front facial expressions as a input images. However pretentious by Corona virus epidemic, public put on face masks to safe ourselves safety, for that face will protected by mask. However this research paper set up a proposed method in this research paper of combining the face portrait with the High speed - channel of the Hue Saturation Value color channel and grayscale image, and train the Convolution Neural Networks to enhance applications that is HGL method. In line portrait we have to generate the image insert it into Convolution Neural Networks for training. Without any processing insert the original picture of the Red Green Blue color space into the Convolution Neural Networks. Fine grained Net Structured Aggregation: FSA – NET stand for Fine grained Structured Aggregation. It is the method to use to remember the structure of a single Red Green Blue color space image. Since the output of this network is the Euler angle of the head pose, we have chosen a set of thresholds that are most effective for pose classification. The way to solve this issue is to provide help for the study of multi-angle problems. In practically, we can practice for a face detection algorithm that differentiate between wearing a mask or demask. If the facial image with masks, we can recommend this method proposed in this paper, and if it is a normal facial image, we can recommend Fine grained Structured Aggregation or Line Portrait algorithm, etc. The very first step of this algorithm to res

An Efficient Palette Generation Method for Color Image Quantization

This article describes an efficient method to generate a color palette for color image quantization. The method consists of two stages. In the first stage, the initial palette is generated. Initially, the color palette is an empty set. First, the N colors are generated according to the data distribution of the input image in the RGB (Red, Green, Blue) color space. Then, one color is selected from the N colors and this color is added to the initial palette, and the step is repeated until the color number of the initial palette is equal to K. In the second stage, the quantized image is generated using the fast K-means algorithm. There are many sampling rates used in this study. For each sampled pixel, a fast searching method is employed to efficiently determine the closest color in the palette. Experimental results show that the high-quality quantized images can be generated by the proposed method. When the sampling rate equals 0.125, the computation time of the proposed method is less than 0.3 s for all cases.

Commercially available color-catching sheets for magnetic textile solid phase extraction of water-soluble dyes

Magnetic textile solid phase extraction, based on the use of magnetically separable color-catching sheets, was successfully employed for the preconcentration of water-soluble dyes methylene blue and crystal violet from water solutions. The photos of color-catching sheet squares with the adsorbed dye were taken with a mobile phone. The computer-based image analysis of the photos was performed using ImageJ freeware while smartphone-based image analysis employed On Color Measure application. The values of saturation from HSB (hue – saturation – brightness) color space, as well as red channel values from RGB (red – green – blue) color space were proportional to the methylene blue and crystal violet concentration in the analyzed samples. This novel method has a potential to be a useful semiquantitative determination procedure for dye analysis.

Linear discriminant analysis in red sorghum using artificial intelligence

Red Sorghum landraces are predominantly cultivated in southern districts of Tamil Nadu, India and are noted for its anthocyanin (3-deoxyanthocyanin) content, high antioxidant activity, low glycemic index, dietary fiber, astringency, fodder value, high protein, biotic and abiotic stress resistance. In this study, the seeds of 72 sorghum landraces collected from Tamil Nadu were analyzed to measure morpho-colourimetric data using an image analyser. Artificial Intelligence is widely used in genetic research for its uncompromised accuracy and speed. The observed phenotypic data is used to create a statistical database and analysed by linear discriminate analysis (LDA) to identify and discriminate landraces for utilization in breeding projects. The classification is based on sorghum descriptors established by Indian Institute of Millet Research, Hyderabad. The LDA for the trait seed size had discriminated the landraces into three groups with an accuracy of 91.83%. Five groups were observed based on grain color Red Green–Blue colour space Model and CIELAB (Commission International de I’Elcairage Lightness Red/Green values Blue/Yellow values) colour space with accuracy 53.06%. The result showed that discrimination of landraces was possible with great accuracy by image analyser. The image analysis of seeds is found to be more effective, easy, non-destructive and fast method to classify landraces. This new phenomic approach is proved to be an important tool in the early selection of genotypes and facilitates speed breeding in colored sorghum.

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.

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.

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.

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.

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.

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.

Visual saliency object detection using sparse learning

In many applications in order to recognise the relationship between user and computer, the position at which the user looks should be detected. To this end, a salient object should be extracted that is attracted to the attention of the viewer. In this study, a new method is proposed to extract the object saliency map, which is based on learning automata and sparse algorithms. In the proposed method, after decomposition of an image to its superpixels, eight features (namely three features in red–green–blue colour space, coalition, central bias, rotation feature, brightness, and colour difference) are extracted. Then the extracted features are normalised to zero mean and unit variance. In this study, K-means singular-value decomposition is used to integrate the extracted features. The performance of the proposed method is compared with that of 20 other methods by applying four new databases, including MSRA-100, ECSSD, MSRA-10K, and Pascal-S. The obtained results show that the proposed method has a better performance compared to the other methods with regard to the prediction of the salient object.

The Use of Unmanned Aerial Systems to Map Intertidal Sediment

This paper describes a new methodology to map intertidal sediment using a commercially available unmanned aerial system (UAS). A fixed-wing UAS was flown with both thermal and multispectral cameras over three study sites comprising of sandy and muddy areas. Thermal signatures of sediment type were not observable in the recorded data and therefore only the multispectral results were used in the sediment classification. The multispectral camera consisted of a Red–Green–Blue (RGB) camera and four multispectral sensors covering the green, red, red edge and near-infrared bands. Statistically significant correlations (>99%) were noted between the multispectral reflectance and both moisture content and median grain size. The best correlation against median grain size was found with the near-infrared band. Three classification methodologies were tested to split the intertidal area into sand and mud: k-means clustering, artificial neural networks, and the random forest approach. Classification methodologies were tested with nine input subsets of the available data channels, including transforming the RGB colorspace to the Hue–Saturation–Value (HSV) colorspace. The classification approach that gave the best performance, based on the j-index, was when an artificial neural network was utilized with near-infrared reflectance and HSV color as input data. Classification performance ranged from good to excellent, with values of Youden’s j-index ranging from 0.6 to 0.97 depending on flight date and site.

Coloration Characteristic and Population Genetic Analysis of Wild-Captured Giant Tiger Shrimp (Penaeus monodon) from Aceh Timur

Giant tiger shrimp (Penaeus monodon) has become a prime commodity in Indonesia which was produced by aquaculture and capture fisheries activities. Aceh Province, in this case mostly represented by Aceh Timur District, was well-known as the center of wild-captured-adult giant tiger shrimp. Several previous investigations had proved for its high-quality shrimp spawner in producing good eggs in quality and quantity under artificial spawning condition. Two main interesting points of wild giant tiger shrimp from Aceh Timur came from their coloration and population clusters. This report was aimed to provide that information pre-preliminary and highlighted quantitative information of coloration characteristic through RGB (Red Green Blue) and CIE Lab color space data analysis, as well as, 16S rDNA-PCR-RFLP genetic comparison among four population clusters in Aceh Timur Waters. The color analysis resulted in significant differences between wild-captured and pond-cultured giant tiger shrimp which produced R value 0.1524±0.0091 and 0.1268±0.0004, respectively. Total pixel analysis through L* a* b* color space has distinguished detailed differentiation between wild-captured and pond-cultured giant tiger shrimp acquired images. It is known that most of the wild-captured image pixels were concentrated in quadrant I (+a, +b) while pond-cultured in quadrant II (-a, +b) and III (-a, -b).Genotyping of represented samples from 4 population clusters, i.e. Aceh Tamiang, Langsa, Peudawa, and Julok produce 2 haplotype composite, AAA and AAB. Among 4 clusters, it was found that Julok has become the only cluster which has a different haplotype composite ratio (1:1) (D 0.0348, V 0,9501) from the others (4:1)(V 0.9504).

Fusion of RGB and HSV colour space for foggy image quality enhancement

The physical properties of water cause light-prompted degradation of foggy images. The light quickly loses intensity as it goes in the water, depending upon the shading range wavelength. Visible light is consumed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Foggy images with low contrast, are captured due to the degradation effects of the light spectrum. Therefore, valuable information from these images cannot be fully extracted for further processing. In this paper, the authors have proposed a new method to increase contrast and reduce the noise of foggy images using CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm. The proposed method fuses the modification of image histogram into two main colour models, namely, Red–Green–Blue (RGB) and Hue-Saturation-Value (HSV). In the primary stage, the CLAHE is connected just on the red part as in water, red shading is more influenced than the blue or green shading. Furthermore, in the second stage, without influencing the hue, the CLAHE is connected to saturation and value components of the HSV colour model. Finally, enhanced image has been produced using a fusion of output of primary phase and output produced in the second phase. Two parameters, namely, RMSE (Root Mean Squared Error) and the PSNR (Peak Signal to Noise Ratio) have been considered in comparing the experimental results of the proposed system with state-of-the-art work.

The color of melt ponds on Arctic sea ice

Abstract. Pond color, which creates the visual appearance of melt ponds on Arctic sea ice in summer, is quantitatively investigated using a two-stream radiative transfer model for ponded sea ice. The upwelling irradiance from the pond surface is determined and then its spectrum is transformed into RGB (red, green, blue) color space using a colorimetric method. The dependence of pond color on various factors such as water and ice properties and incident solar radiation is investigated. The results reveal that increasing underlying ice thickness Hi enhances both the green and blue intensities of pond color, whereas the red intensity is mostly sensitive to Hi for thin ice (Hi < 1.5 m) and to pond depth Hp for thick ice (Hi > 1.5 m), similar to the behavior of melt-pond albedo. The distribution of the incident solar spectrum F0 with wavelength affects the pond color rather than its intensity. The pond color changes from dark blue to brighter blue with increasing scattering in ice, and the influence of absorption in ice on pond color is limited. The pond color reproduced by the model agrees with field observations for Arctic sea ice in summer, which supports the validity of this study. More importantly, the pond color has been confirmed to contain information about meltwater and underlying ice, and therefore it can be used as an index to retrieve Hi and Hp. Retrievals of Hi for thin ice (Hi < 1 m) agree better with field measurements than retrievals for thick ice, but those of Hp are not good. The analysis of pond color is a new potential method to obtain thin ice thickness in summer, although more validation data and improvements to the radiative transfer model will be needed in future.

Covert Images Using Surface Plasmon‐Mediated Optical Polarization Conversion

AbstractCovert optical signatures are a vital element in anticounterfeiting technologies. Plasmonic surfaces offer a means of manipulating the properties of light including the realization of colored pixels and images. In this work, concealed images with accurate color reproduction using plasmonic pixel arrays are demonstrated. The spectral and spatial control of optical polarization conversion is accomplished by tailoring the interaction of light with surface plasmons through the design and arrangement of surface nanostructures. The latent image is revealed using a polarization‐sensitive optical system, which represents a means for the authentication of security features that can be created using these nanostructured devices. A red–green–blue color space is defined containing a wide gamut of chromaticities, enabling comprehensive full‐color image capability. The device concept extends the functionality of a polarization‐dependent plasmonic response to realize the encoding of a color image in covert form.

Predicting the ripening of papaya fruit with digital imaging and random forests

Papaya grading is performed manually which may lead to misclassifications, resulting in fruit boxes with different maturity stages. The objective is to predict the ripening of the papaya fruit using digital imaging and random forests. A series of physical/chemical analyses are carried out and true maturity stage is derived from pulp firmness measurements. Imaging and image analysis provides hand-crafted color features computed from the peel and random decision forests are implemented to predict ripening stage. More specifically, a total of 114 samples from 57 fruits are used for the experiments, and classified into three stages of maturity. After image acquisition and analysis, twenty-one hand-crafted color features (comprising seven groups) that have low computational cost are extracted and evaluated. Random forests with two datasets (cross-validation and prediction set) are employed for the experiments. Concerning all image features, 94.3% classification performance is obtained over the cross-validation set. The prediction set obtained 94.7% misclassifying only a single sample. For the group comparisons, the normalized mean of the RGB (red, green, blue) color space achieved better performance (78.1%). Essentially, the technique can mature into an industrial application with the right integration framework.

Colorimetric determination of mercury vapor using smartphone camera-based imaging

ABSTRACTThe smartphone camera presents a convenient, portable, and low-cost innovation in colorimetric measurements. In this paper, a smartphone camera was applied for the colorimetric detection of gaseous elemental mercury, an atmospheric pollutant of concern in environment, and workplace monitoring. A cuprous iodide (CuI)/polystyrene composite was used as the recognition element, which exhibited a reddish color in the presence of Hg0. Digital images of the sensing reagent phase were captured by a smartphone camera and were analyzed in red–green–blue color space using ImageJ, an open source image processing program. Parameters for the digital colorimetric sensing including the color values, polymer reagent binder, amount of CuI, exposure time, and Hg0 concentration were investigated and optimized. The linear working range of the sensor was from 61 to 270 μg/m3 Hg0 with a correlation coefficient of 0.978. The limit of detection is 16 μg/m3 Hg0. This method shows the feasibility of applying a smartphone camera for a simple, reliable, and inexpensive colorimetric measurement of Hg0 vapor in a gas mixture such as air.