RGB Pixel Values Research Articles

Genetic analysis of pericarp pigmentation variation in Corn Belt dent maize.

The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus.

Performance Comparison of Classical Methods and Neural Networks for Colour Correction.

Colour correction is the process of converting RAW RGB pixel values of digital cameras to a standard colour space such as CIE XYZ. A range of regression methods including linear, polynomial and root-polynomial least-squares have been deployed. However, in recent years, various neural network (NN) models have also started to appear in the literature as an alternative to classical methods. In the first part of this paper, a leading neural network approach is compared and contrasted with regression methods. We find that, although the neural network model supports improved colour correction compared with simple least-squares regression, it performs less well than the more advanced root-polynomial regression. Moreover, the relative improvement afforded by NNs, compared to linear least-squares, is diminished when the regression methods are adapted to minimise a perceptual colour error. Problematically, unlike linear and root-polynomial regressions, the NN approach is tied to a fixed exposure (and when exposure changes, the afforded colour correction can be quite poor). We explore two solutions that make NNs more exposure-invariant. First, we use data augmentation to train the NN for a range of typical exposures and second, we propose a new NN architecture which, by construction, is exposure-invariant. Finally, we look into how the performance of these algorithms is influenced when models are trained and tested on different datasets. As expected, the performance of all methods drops when tested with completely different datasets. However, we noticed that the regression methods still outperform the NNs in terms of colour correction, even though the relative performance of the regression methods does change based on the train and test datasets.

Drying kinetics and image-based identification of drying end point during parboiling of komal chawal

During the process of parboiling, steamed rice goes through a process of drying till a suitable moisture content for storage. This work is based on classifying suitable moisture content of less than 13% for storage. Further, this classification is used for estimating the end point of drying based on images of rice taken out at different time intervals. The drying process is a basic process for food technologists and involves mathematical modeling to determine the process parameters. Optimal drying time calculation to achieve desired moisture content involves the use of mathematical modeling. There are two ways of calculating the end point. One is using already existing drying models, e.g., Page, Newton, etc., with time as a factor; the other is using FF- ANN with one hidden layer with drying time and temperature as a factor; and for theoretical understating of the diffusion process, the analytical solution of Fick's law was studied. The other method involves using extrinsic data like the RGB values and pixel values of images mapped with moisture content classes predicted using machine learning models. This study aims to show how RGB data-based machine learning models for classification gives real time solutions to problems like calculating the final moisture content after drying of parboiled brown rice. Digital images were used as input, pixel values were used as features, and the images were labeled into two categories: A (moisture greater than 13%) and B (less than or equal to 13%). For this purpose, the dataset used consists of the moisture content of samples at different times (0–180 min) and temperatures (40, 50, and 60 °C) of drying in a tray dryer. Semi empirical models were evaluated, and Modified-Page showed the best fitting, The Feed forward Artificial Neural Network was observed to show an accuracy of 0.882 based on the least square error. A convolutional neural network (CNN) was used to map the image pixel data to classify the rice sample based on moisture content with a validation accuracy of 0.79 and a test accuracy of 0.74. Similarly, RGB values-based ML models like CART showed better accuracy of 0.92 approximately. Lastly, a simple calculator for an android application was built in MIT App Inventor by selecting the best fitted model of classification (LR, KNN, NB, or SVM) based on RGB values as the features.

Optimisation of attribution processes using novel pixel-value based colour detection approach

Conventional CNN-based colour detection mechanisms are not optimal due to colour overlap, for example, the CNN model can find it difficult to distinguish shades of red and orange. The proposed approach is aimed at optimizing colour detection in attribution processes using a superior, objective and mathematical approach. The proposed approach allows you to detect a much larger range of shades of colour while increasing colour detection accuracy and reducing the time it takes to predict colour without model training. The proposed approach performs better than the conventional CNN-based supervised machine learning colour detection owing to its enhanced quantifiability, increased degree of quantisation of the colour palette (i.e., an improved colour range that can be detected) and lower the complexity of time. The approach employs fundamental concepts of image processing to isolate pixels that best represents the object’s colour from the region of interest (RoI), following which a mode filter is applied to the RGB pixel values of the pixels lying in the RoI to obtain a single set of [R, G, B] values that best represents the colour of the object. The mapping of this set of RGB values to the name of colour defined in a “Colour Taxonomy” is performed using Euclidean or Manhattan distance, depending upon the application. The accuracy of colour prediction is enhanced and computing time is greatly reduced. The current approach is aimed to be more accurate, more granular in detection (i.e., allows you to detect a much larger range of shades of colour and more time-effective than CNN-based colour detection and manual tagging thus greatly helping optimize attribution processes.

An Aerosol Extinction Coefficient Retrieval Method and Characteristics Analysis of Landscape Images.

Images based on RGB pixel values were used to measure the extinction coefficient of aerosols suspended in an atmospheric state. The pixel values of the object-image depend on the target-object reflection ratio, reflection direction, object type, distances, illumination intensity, atmospheric particle extinction coefficient, and scattering angle between the sun and the optical axes of the camera, among others. Therefore, the imaged intensity cannot directly provide information on the aerosol concentration or aerosol extinction coefficient. This study proposes simple methods to solve this problem, which yield reasonable extinction coefficients at the three effective RGB wavelengths. Aerosol size information was analogized using the RGB Ångström exponent measured at the three wavelengths for clean, dusty, rainy, Asian dust storm, and foggy days. Additionally, long-term measurements over four months showed reasonable values compared with existing PM2.5 measurements and the proposed method yields useful results.

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5–40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

IRIS-GRAPE: An approach for prediction of quality attributes in vineyard grapes inspired by iris biometric recognition

The determination of the period of harvest is essential in the vineyard. For that, attributes of quality such as Total Soluble Solids (TSS) and phenolic compounds are constantly monitored along the maturation. This work proposes a new non-destructive approach for prediction of TSS, total anthocyanins and yellow flavonoids using RGB images, called IRIS-GRAPE. It is inspired by the process of biometric recognition using the iris. In order to validate the proposed approach, a study comparing its performance with the traditional method was performed, using the average of RGB pixel values of the region of interest (ROI) as input variables for a multiple linear regression. The study used two performance evaluation metrics: the correlation coefficient (rP) and the mean square error (MSE). In order to compare the performance differences between the proposed approach, IRIS-GRAPE, and the traditional approach, hypothesis tests were done. The results show that the proposed approach has a superior performance than the traditional method with a confidence level of 95%.

Automatic Classification of Chickpea Varieties Using Computer Vision Techniques

There are about 90 different varieties of chickpeas around the world. In Iran, where this study takes place, there are five species that are the most popular (Adel, Arman, Azad, Bevanij and Hashem), with different properties and prices. However, distinguishing them manually is difficult because they have very similar morphological characteristics. In this research, two different computer vision methods for the classification of the variety of chickpeas are proposed and compared. The images were captured with an industrial camera in Kermanshah, Iran. The first method is based on color and texture features extraction, followed by a selection of the most effective features, and classification with a hybrid of artificial neural networks and particle swarm optimization (ANN-PSO). The second method is not based on an explicit extraction of features; instead, image patches (RGB pixel values) are directly used as input for a three-layered backpropagation ANN. The first method achieved a correct classification rate (CCR) of 97.0%, while the second approach achieved a CCR of 99.3%. These results prove that visual classification of fruit varieties in agriculture can be done in a very precise way using a suitable method. Although both techniques are feasible, the second method is generic and more easily applicable to other types of crops, since it is not based on a set of given features.

Optimized self-adapting contrast enhancement algorithm for wafer contour extraction

The aim is to design a self-adapting contrast enhancement algorithm that can remove the background noise and divide the foreground and background very well. For the self-adapting contrast enhancement algorithm, we looked for all background RGB pixel values, and found the minimum values in the RGB components so that ∂=255/min(Ipixel(R, G, B)). The results were compared to classical contrast enhancement techniques. Five steps to extract the wafer contour, respectively, were the preservation filter, self-adapting contrast enhancement, closing morphological operation, image binarization based on the Otsu method and using the first derivative extract contour. The results of contour extraction were compared with the traditional Canny, Sobel and Scharr algorithms using different parameter values. The experiment results showed that the self-adapting contrast enhancement algorithm could remove the background noise effectively compared to traditional methods. In comparison with the Canny, Sobel and Scharr algorithms, the first derivative after the original image filtering by optimized self-adapting contrast enhancement algorithm was better at extracting wafer contours. Therefore, we concluded that the wafer self-adapting contrast enhancement algorithm method was correct and feasible. The theories in this paper could be applied to image processing in the agricultural, industrial, and military fields.

Quantitative Measurement of the Exposure Response of Digital Cameras

Digital cameras span a large range in price and performance. Consumers often focus mainly on the resolution in pixels when shopping for a camera. Of equal importance is the quality of the optics and the exposure response. Digital cameras generally have a linear exposure response, but the amount of noise and the dynamic range vary. It is difficult to obtain quantitative information on these parameters to make an informed assessment. This work explores and demonstrates first-principles methods to measure the exposure response to make meaningful comparisons between different camera models. It also shows how to make the most of a particular camera by measuring its noise level and dynamic range, to understand the limits of its useable ISO amplification. The methods only require a computer and free software to download images and extract their RGB pixel values. The analysis, based on the RGB values, uses standard spreadsheet software. The procedures are therefore accessible to anyone with a digital camera and computer, and will help to reduce speculation in comparing cameras, and help consumers make an informed decision.

Improving the performance of convolutional neural network for skin image classification using the response of image analysis filters

In this work, we focus in the analysis of dermoscopy images using convolutional neural networks (CNNs). More specifically, we investigate the value of augmenting CNN inputs with the response of mid-level computer vision filters, using the traditional inputting of simple RGB pixel values as baseline. The proposed methodology is applied on two pattern recognition problems with clinical significance: the binary classification of skin lesions in dermoscopy images into “malignant” and “non-malignant” (nevus skin lesions) cases and the four-class, superpixel classification into differential structures that appear in skin lesions. The transfer learning technique is also utilized to compensate for the limited size of the available training image datasets. Results show that filter-based input augmentation using the response of mid-level computer vision filters significantly improves the classification accuracy achieved by the CNN architectures and simplifies the weights of the receptive fields.

Vegetation Image as Bayesian Predictor for Radio Propagation in Complex Environments Using Unscented Transform

Vegetation is considered a complex environment for analysis of scattering and attenuation in radio propagation phenomena. Satellite image processing can improve planning of radio systems with a vegetation attenuation predictor. In this research, the prediction is based on the correlation of more than 56% between RGB pixel values and vegetation attenuation taken from three groups of power measurements at two distinct regions of Brazil: Belo Horizonte, in the southeast region measured at 18 GHz, and Manaus at 24 GHz in the north region. The statistical analysis showed that more than 30% of the attenuation variance was due to the pixel values for each group. Using this linear correlated model between vegetation pixel RGB values and geolocated attenuation values, this work combined the unscented transform (UT) and Bayesian inference to refine the vegetation attenuation distribution. Since the necessary multiplication of Bayes prior and sampling distributions is not easily available in the UT, this paper presents a method that calculates new common sigma points and different new weights for the prior and sampling UT distributions, thus allowing the multiplication and creating the basis for a machine learning predictor tool.

An Enhanced Ant Colony Clustering Method for Color Image Segmentation

Objectives: The aim of segmentation process is to divide the image into homogeneous, self-consistent region or objects. The segmentation algorithms try to make systematic uses of some physically measured image features. Methods/Statistical Analysis: In the earlier research work, the researchers covered only multi resolution, quad tree structure, ant colony optimization and Otsu method for segmentation object in gray scale but not for color images. To overcome these issues, the segmentation for color image is focused on Enhanced Ant Colony Clustering (EACC) method. Findings: The proposed EACC method has three main parts. The first part is used to isolate the components of the given color image including RGB pixel values. The second part finds the clustering center with the help of combination of statistical and artificial selection. The last part implements EACC algorithm to segment on color image. In the existing method of ACO, the processing time takes longer time to segment the object. At the same time, while comparing the threshold value on existing method is lower than current proposed method of EACC. Applications/Improvements: In this research work, the proposed method achieves better segmentation in color image for the data sets Oxford Flowers 17, Weizmann Horse and MSRC dataset. Further, this work may be extended to different type of images such as, multiband or multispectral images, satellite images, etc. Finally, the experimental results are shown through Mat Lab R2013a.

컬러 영상의 RGB 화소 최대차분 기반 보간법을 이용한 정보은닉 기법

Interpolation based information hiding methods are widely used to get information security. Conventional interpolation methods use the neighboring pixel value and simple calculation like average to embed secret bit stream into the image. But these information hiding methods are not appropriate to color images like military images because the characteristics of military images are not considered and these methods are restricted in grayscale images. In this paper, the new information hiding method based on interpolation using RGB pixel values of color image is proposed and the effectiveness is analyzed through experiments.

Cross correlation feature mining for steganalysis of hash based least significant bit substitution video steganography

Recently, video steganography has attracted researchers' attentions for its relative higher capacity than steganographic technique using image or audio as cover. Various image steganographic techniques have been proposed, such as least significant bit (LSB) substitution, LSB matching, etc. In (Dasgupta et al. in Int J Secur Priv Trust Manag 4:1---11, 2012), the authors proposed a hash based least significant bit substitution video steganography in spatial domain, and eight bits of secret message were divided into 3, 3, 2 segments and embedded into the RGB pixel values of cover frame. Besides, a hash function was used to determine the embedding locations. However, the hash function is fragile to attack and the observer can easily figure out the exact embedded positions. Moreover, the embedding of secret message significantly changes the cross correlation feature of consecutive video frames. Based on cross correlation analysis, a video steganalysis technique is proposed in this paper. Theoretical analysis and experimental results all show the effectiveness of the proposed cross correlation based video steganalysis technique.

Visualization of IOL Material-Induced Changes in Retinal Color Stimulus.

Purpose. Different IOL materials, particularly blue-light filtering materials, have different spectral transmittance characteristics. The color stimuli, which influence retinal receptors objectively, have consequently implications for color perception. We report on the quantitative determination of IOL-specific transmittance characteristics and present a method visualizing the resultant changes in color stimulus. Methods. A setup was realized to quantify IOL-absorption in a range of 390–780 nm. To visualize the influence of the different spectral transmittance characteristics an algorithm was developed, which converts RGB-pixel values of images into spectra, which performs the corresponding transmittance correction, reconverts to RGB, and reconstructs the image. IOLs of hydrophobic acrylate and hydrophilic acrylate with a hydrophobic surface in each case with/without blue-light filter were examined. Results. Assessment of the reference images verifies the suitability of the pipeline. Evaluation of the transmittance spectra reveals differences of material- and manufacturer-specifics, which are capable of inducing considerable changes in color perception, particularly in the blue color range and mixed colors involving blue. Conclusions. The developed technique provides an approach for determining IOL-specific transmittance behavior and subsequently its influence on the retinal color stimulus. Problems of altered color perception are occasionally reported after cataract surgery and these become obvious with the visualization procedure developed here.

웹 카메라를 이용한 시설 내 국화생산 광 환경 원격 모니터링

Increase of national family income improved demand of high-quality and year-round horticultural products including chrysanthemum. To meet these demand, farmers have introduced protected facilities, such as greenhouses, of which environmental conditions could be monitored and controlled. Environment management up to three weeks after transplanting is critical for chrysanthemum quality. Artificial lighting and light-blocking screen are especially important for long-day (day period > 13 hours) and short-day (night period > 13 hours) treatments. In this study, a web-camera was installed, and the image was obtained and transmitted to mobile phones to monitor the status of 3-wavelength(RGB) lighting environments. RGB pixel values were used to determine malfunctioning of the lighting lamps, and leaking out and incoming illumination status during short-day and long-day treatment periods. Normal lighting lamps provided RGB pixel values of 240～255. During long-day treatment period, G pixel values were useful to detect abnormal lighting conditions (e.g., leaking). During short-day treatment period, R pixel values were useful to determine incoming light (e.g., sun-light). Results of this study would provide useful information for remote monitoring of light conditions for protected chrysanthemum production under artificial lights.

Image Encryption based on the RGB PIXEL Transposition and Shuffling

Privacy is one of the key issues information Security addresses. Through encryption one can prevent a third party from understanding raw data during signal transmission. The encryption methods for enhancing the security of digital contents has gained high significance in the current era of breach of security and misuse of the confidential information intercepted and misused by the unauthorized parties. This paper sets out to contribute to the general body of knowledge in the area of cryptography application and by developing a cipher algorithm for image encryption of m*n size by shuffling the RGB pixel values. The algorithm ultimately makes it possible for encryption and decryption of the images based on the RGB pixel. The algorithm was implemented using MATLAB.

Content-adaptive color transform for image compression

In this paper, an adaptive color transform for image compression is introduced. In each block of the image, coefficients of the color transform are determined from the previously compressed neighboring blocks using weighted sums of the RGB pixel values, making the transform block-specific. There is no need to transmit or store the transform coefficients because they are estimated from previous blocks. The compression efficiency of the transform is demonstrated using the JPEG image coding scheme. In general, the suggested transformation results in better peak signal-to-noise ratio (PSNR) values for a given compression level.

RGB Image Color Space Transformations

This paper describes a set of pixel accessors that transform RGB pixel values to a different color space. Accessors for the HSI, XYZ, Yuv, YUV, HSV, Lab, Luv, HSL, CMY, and CMYK color spaces are provided here. This paper is accompanied with source code for the pixel accessors and test, test images and parameters, and expected output images.Note: Set() methods are incorrect. Will provide revision by 12.17.2010.