RGB Color Space Research Articles

РАЗРАБОТКА МАТЕМАТИЧЕСКОЙ МОДЕЛИ ОПРЕДЕЛЕНИЯ СОСТОЯНИЯ КОПЫТ КРУПНОГО РОГАТОГО СКОТА НА ОСНОВЕ ПРОФИЛЕЙ ТЕРМОГРАММ

Inflammation in the cow hooves leads to a decrease in milk yield and may cause early culling of animals. Early diagnosis of the disease provides a reduction in the cost of treatment, allows maintaining the health and milk production of animals. The use of infrared thermography to determine the temperature of cattle limbs is a fast and non-invasive way to obtain reliable information about the animal’s health. To develop a mathematical model for determining the state of cattle limbs based on the profiles of hoof thermograms, the authors processed the data of 20 cows of the Yaroslavl breed, which are on the 2nd-4th lactation, with a lactation period ranging between months 1 and 7. The data array was formed using a Guide C400M thermal imager in the IR range. Data on the physiological state of cattle were entered into the Matlab R2019b program. For color gradation, the RGB color space was selected. The presented mathematical model for processing the profile of thermograms makes it possible to collect information on the spatial and temporal dynamics of cattle hoof temperatures by thermophysiological categories. When determining the state of the limbs, the areas of spatio-temporal distribution of temperature in the hoof are identified in the form of a transformed segmentation profile of the thermogram into a matrix, where RGB is assigned to the pixels from each fragment of the area, indicating a certain temperature. Exceeding the values R > 230, G > 103, B < 0 symbolizes a temperature above 40°C and means the presence of inflammation in the hoof. The developed algorithm will allow a farm veterinarian to automatically receive information about the state of the hooves of each cow, determine the focus of inflammation, its location, and diagnose the disease.

Edge-Aware Extended Star-Tetrix Transforms for CFA-Sampled Raw Camera Image Compression.

Codecs using spectral-spatial transforms efficiently compress raw camera images captured with a color filter array (CFA-sampled raw images) by changing their RGB color space into a decorrelated color space. This study describes two types of spectral-spatial transform, called extended Star-Tetrix transforms (XSTTs), and their edge-aware versions, called edge-aware XSTTs (EXSTTs), with no extra bits (side information) and little extra complexity. They are obtained by (i) extending the Star-Tetrix transform (STT), which is one of the latest spectral-spatial transforms, to a new version of our previously proposed wavelet-based spectral-spatial transform and a simpler version; (ii) considering that each 2D predict step of the wavelet transform is a combination of two 1D diagonal or horizontal-vertical transforms; (iii) weighting the transforms along the edge directions in the images. Compared with XSTTs, the EXSTTs can decorrelate CFA-sampled raw images well: they reduce the difference in energy between the two green components by about 3.38-30.08 % for high-quality camera images and 8.97-14.47 % for mobile phone images. The experiments on JPEG 2000-based lossless and lossy compression of CFA-sampled raw images show better performance than conventional methods. For high-quality camera images, the XSTTs/EXSTTs produce results equal to or better than the conventional methods: especially for images with many edges, the type-I EXSTT improves them by about 0.03-0.19 bpp in average lossless bitrate and the XSTTs improve them by about 0.16-0.96 dB in average Bjøntegaard delta peak signal-to-noise ratio. For mobile phone images, our previous work perform the best, whereas the XSTTs/EXSTTs show similar trends to the case of high-quality camera images.

Variational Single Nighttime Image Haze Removal With a Gray Haze-Line Prior.

Influenced by glowing effects, nighttime haze removal is a challenging ill-posed task. Existing nighttime dehazing methods usually result in glowing artifacts, color shifts, overexposure, and noise amplification. Thus, through statistical and theoretical analyses, we propose a simple and effective gray haze-line prior (GHLP) to identify accurate hazy feature areas. This prior demonstrates that haze is concentrated on the haze line in the RGB color space and can be accurately projected into the gray component in the Y channel of the YUV color space. Based on this prior, we establish a new unified nighttime haze removal framework and then decompose a nighttime hazy image into color and gray components in the YUV color space. Glowing color correction and haze removal are two important consecutive steps in the nighttime dehazing process. The glowing color correction method is designed to separately remove glow in the color component and enhance illumination in the gray component. After obtaining a refined nighttime hazy image, we propose a new structure-aware variational framework to simultaneously estimate the inverted scene radiance and the transmission in the gray component. This approach can not only recover the high-quality nighttime scene radiance but also preserve the significant structural information and intrinsic color of the scene. Quantitative and qualitative comparisons validate the excellent effectiveness of the proposed nighttime dehazing method against previous state-of-the-art methods. In addition, the proposed approach can be extended to achieve image enhancement for inclement weather scenes, such as sandstorm scenes and extreme daytime hazy scenes.

Brightness and Saturation Enhancement by Using Color Vector on Equi-Hue Plane in RGB Color Space

In order to improve the image quality of an image with reduced visibility, it is necessary to perform image processing such as brightness correction and sharpening. As a method for improving the visibility of this image, unsharp masking by local projection brightness that emphasizes contrast by using brightness that reflects local color changes while preserving hue has been proposed. In this method, however, block noises are occurred in the image by the excessive emphasis. In this study, we proposed a novel sharpening and saturation enhancement method using an Equi-Hue Plane that has the same hue as the input pixels. The effectiveness of the proposed method has been verified through the various experiments.

Series Arc Fault Detection Based on Wavelet Compression Reconstruction Data Enhancement and Deep Residual Network

Series arc fault is the main cause of electrical fire. Because of the complex load types and the randomness of arc fault in low-voltage distribution system, it is difficult to obtain fault data for data-driven machine learning algorithm to achieve higher accuracy. Therefore, an arc fault detection model based on residual network (ResNet) is proposed from the perspective of computer vision, and an appropriate data enhancement method is given. We first analyze the time-domain current signals of different load arc faults by multilayer discrete wavelet analysis. The obtained five-layer discrete wavelet detail values are fused into a matrix, and the coefficient matrix is then converted into a phase space image (RGB color space) using a colormap index. The phase space feature map is made into a classification dataset according to the load and fault type, which is input into ResNet in the form of three channels (RGB) for convolution and classification recognition. Then, aiming at the over-fitting phenomenon of neural network caused by small sample, a data enhancement method based on wavelet compression reconstruction is proposed. Finally, we set up different datasets to compare our method with typical neural network regularization methods. The results show that our method effectively solves the over-fitting phenomenon of deep network ResNet152 and improves the accuracy of ResNet 50/101/152 to 97.91%, 96.30%, and 97.69%, respectively.

Unsupervised Method for Wildfire Flame Segmentation and Detection

In the last decade, there have been many reports on the negative impact of wildfires on various ecosystems. Unfortunately, wildfires have been intensifying as global temperatures, droughts, and other instances of extreme weather events rise around the world. These circumstances are forcing communities to vigorously address the uncontrolled spread of wildfires, where the ultimate goal is the protection of wildlife. At the same time, many disaster prevention and monitoring methods, based on image processing and computer vision, have been developed. In this paper, we present a new unsupervised method based on RGB color space for the early detection of wildfires from still images. From the analysis of existing state-of-the-art methods, it is evident that different methods explore different color spaces for the extraction of flame features. Our motivation was to use only RGB color space and thus eliminate the time-consuming task of color space conversion. The proposed method consists of several new image processing techniques used to efficiently extract flame features. It outperforms the existing methods, where an increase of 3% and 2% is recorded in the F1 score and Matthews correlation coefficient, respectively. Such performance demonstrates the merits of the proposed method for flame segmentation and detection.

Advanced Retinex-Net image enhancement method based on value component processing

When capturing images under low-light lighting conditions, the resulting images often suffer low visibility. Such low-visibility images not only affect the visual effect but also cause many difficulties in practical application. Therefore, image enhancement technology under low-light conditions has always been a challenging problem in image algorithms. Considering that most of the existing image enhancement methods are based on the RGB color space enhancement technology, the correlation among the RGB three primary colors is ignored, which makes the color distortion phenomenon easy to occur when the image is enhanced. To solve the problems of poor image visibility and color deviation under low-light conditions, in this paper an advanced Retinex network enhancement method is proposed. In the method, firstly the low-light RGB image is transformed into HSV color space, the Retinex decomposition network is used to decompose and enhance the value component separately, and thus increasing the resolution of the value component through up-sampling operation; then, for the hue component and saturation component, the nearest neighbor interpolation is used to increase their resolutions, and the enhanced value component is combined to convert back to RGB color space to obtain the initial enhanced image; finally, the wavelet transform image fusion technology is used to fuse with the original low-light image to eliminate the over-enhanced part in the initial enhanced image. The analysis of experimental results shows that the method proposed in this paper has obvious advantages in brightness enhancement and color restoration of low-light images. Especially, comparing with the original Retinex network method, the NIQE value decreases by an average of 19.49%, and the image standard deviation increases by an average of 41.35%. The algorithm proposed in this paper is expected to be effectively used in the fields of security monitoring and biomedicine.

Better Than Reference in Low-Light Image Enhancement: Conditional Re-Enhancement Network.

Low-light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low-light image enhancement method that can be combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model-based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, equals the maximum channel in RGB color space) of the enhanced image can well represent the contrast and brightness enhancement process. Then, a data-driven conditional re-enhancement network (denoted as CRENet) is proposed. The network takes low-light images as input and the enhanced V channel (V channel of the enhanced image) as a condition during testing, and then it can re-enhance the contrast and brightness of the low-light image and at the same time reduce noise and color distortion. In addition, it takes 23 ms to process a color image with the resolution 400*600 on a 1080Ti GPU. Finally, some comparative experiments are implemented to prove the effectiveness of the method. The results show that the method proposed in this paper can significantly improve the quality of the enhanced image, and by combining it with other image contrast enhancement methods, the final enhancement result can even be better than the reference image in contrast and brightness when the contrast and brightness of the reference are not good.

VR Color Picker: Three-Dimensional Color Selection Interfaces

In this paper, we propose a 3D color picker for virtual reality (VR) systems. Using the proposed color picker, the RGB and HSV color spaces are displayed in 3D shape, and a color can be selected through a one-step interaction by pointing at a location inside the color space with a 3D pointing device. This process is very simple compared with the multistep color selection interactions required for existing VR color pickers. Since the interior of the color space is not visible, we also propose three types of user interfaces that show the interior of the color space. These interfaces either cut out a portion of the color space, show cross-sections of the color space, or show discrete samples placed in the color space. We conducted user experiments on the color selection time, color selection accuracy, and usability and proved the usefulness and effectiveness of the proposed VR color picker.

Plant leaf images computerized segmenation

In this paper the comparison of RGB, HSV and CIELab color spaces is considered in view of diseased leaf images segmentation by color thresholding method. In such tasks HSV and CIELab outperform RGB. Thresholding method based upon HSV or CIELab color spaces can be applied to measuring leaves total area, diseased and healthy surfaces area, as well as dataset composing in machine learning.

Assessing grass water use efficiency through smartphone imaging and ImageJ analysis

Overwatering and underwatering grass are widespread issues with environmental and financial consequences. Current approaches to assessing grass water use efficiency (WUE) are inaccessible to the public. We developed an accessible method to assess grass WUE: combining smartphone imaging with open access color unmixing analysis. Images were converted from the RGB color space to the CIELAB color space using ImageJ—an open access, user-friendly software—to correct for uneven lighting without compromising image detail. We obtained parameters a* (unmixes green-to-red vector) and b* (unmixes blue-to-yellow vector). We hypothesized that WUE can be accurately determined from grass color and growth, which can be analyzed using CIELAB color unmixing. We tested how nine watering levels (100–900 mL every 4–5 days) affected Stenotaphrum secundatum (St. Augustine grass) over one month in Orange County, Southern California. Color was quantified using a*:b* ratios, growth was tracked using grass area, and pigment composition was analyzed using plot profiles. We analyzed whole samples in the uncontrolled real-world environment, individual leaves in a controlled homemade imaging box, and extracted pigments before and after paper chromatography. Results were clustered using Gaussian finite mixture models, implemented by R package ‘mclust’. Overall trends for grass coverage, a*:b* ratios, and pigment composition were consistent with real-world observations. Cluster analyses were consistent across image types, identified an ideal watering range (600–700 mL), and differentiated between underwatered and overwatered grass. Our hypothesis was supported. Our method can be applied in automated irrigation systems or apps, providing grass WUE assessment for regular consumer use.

Compressive Sampling of Color Retinal Image Using Spread Spectrum Fourier Sampling and Total Variant

In medical imaging, the application of retinal images demands a lot of retinal photos to analyze and requires efficient compression techniques for retinal image storage. Retinal images must meet stringent quality requirements for clinical data to be accurate and dependable. This paper proposes a compressive sampling (CS) framework for color retinal image (CRI) compression, which relies on spread spectrum Fourier sampling (SSFS) and total variant (TV)-based reconstruction method with three loops of RGB color space, referred to as RGB-TV. In CS, two procedures are performed, i.e., compression and CS reconstruction. In compression steps, SFFS is performed to get a compressed signal from the original CRI with a high compression ratio (CR). While in CS reconstruction, TV-norm and TV proximal operator are exploited for problem optimization to recover original CRI from a compressed signal. In addition, signal-to-noise ratio (SNR), structural similarity (SSIM), and reconstruction time are investigated for the performance metrics of the proposed RGB-TV. The computer simulation result shows that the proposed RGB-TV with a set of CRI of size 512 by 512 pixels can compress until CR = 10 which obtains mean SNR of 22 dB, SSIM 0.84, and reconstruction time of 2.2 seconds.

FilterNet: Self-Supervised Learning for High-Resolution Photo Enhancement

We propose an end-to-end model for high-resolution photographic enhancement in a self-supervised learning approach. Bad quality photos are created from an unlabelled dataset of good quality images. Our deep network model is presented with pairs of bad and good images, and learns the parameters of 6 photographic filters that improve the bad photos by trying to make them similar to the high-quality references. Custom rendering layers apply the photographic filters and compute their derivatives during the forward training pass, so loss attribution can be performed during the backward pass. Our experiments confirm that loss functions based on feature-extraction networks achieve better quality than pixel-comparison metrics. To mimic professional editing applications, our filters are based on curve mapping and alpha blending, and they are rendered using a linear RGB colorspace for mathematical accuracy. At inference time, the custom rendering layers are removed so the model’s output is just the set of filter parameters that best improve the input image. We achieve high-resolution results by applying the predicted filters to the photo captured by the user, even though training and prediction take place using downscaled thumbnails. Our approach has been validated in a professional mobile application.

RESEARCH ON VISUAL NAVIGATION PATH DETECTION METHOD FOR DENSE PLUM GROVE

Aiming at the field management of plum grove in Inner Mongolia of China, taking the dense planting plum groves in Bikeqi town of Hohhot City as the research object, this paper proposed a visual navigation path detection algorithm for plum grove. By processing the video image information of plum grove, comparing RGB and HSV color space model, HSV color model was selected to separate the plant and background in V channel. Homomorphic filtering was used to highlight the region of interest in the image, Otsu was selected to segment the image, the intersection of plum trunk and ground was extracted as feature points, and the least square method was used to fit the navigation path. Through the comparative analysis of detection rate under different detection conditions in one day, the verification test of route accuracy was carried out. The experimental results show that: for dense planting plum grove, the average path detection accuracy of the algorithm is 70% and 73.3% under the condition of front light and weak light, respectively. The detection accuracy and real-time meet the requirements of plum grove field management, and the navigation baseline can be generated more accurately, which provides a preliminary basis for the realization of mechanical vision navigation in plum grove field management.

Detection of Ripeness of Manggosteen Fruit Using Hsv Color Space Transformation Method

Rapid advances in innovation in the PC field are increasingly making applications and exploration of image handling strategies created. Image processing has an important role in various fields. Image processing applications are concerned with image processing with regard to color transformation. In this case, the method of transforming the color space as part of the image processing helps in detecting the colors in the image and processing them. Color space is a mathematical model that describes the color represented in the number model. HSV color space is a color space composed of 3 components, namely Hue, Saturation, Value. Hue is related to purity (color pattern), Saturation is related to the saturation of the color, Value expresses the brightness level of the color. In this study, based on the results of testing using mangosteen fruit imagery to detect the type of ripeness by transforming RGB color space into HSV color space conducted using sample data as much as 60 fruit data consisting of 45 training samples consisting of 15 images of mangosteen fruit in each type of ripeness, and 15 test samples consisting of 5 samples of ripe mangosteen fruit, 5 samples of half-cooked mangosteen fruit and 5 samples of raw mangosteen fruit obtained success rate accuracy of 86.6%, so it can be concluded that the HSV Color Space Transformation method can be used to detect the ripeness of mangosteen fruit.

Image analysis of real-time classification of cherry fruit from colour features

An image analysis algorithm for the classification of cherries in real time by processing their digitalized colour images was developed, and tested. A set of five digitalized images of colour pattern, corresponding to five colour classes defined for commercial cherries, was characterized. The algorithm performs the segmentation of the cheery image by rejecting the pixels of the background and keeping the image features corresponding to the coloured area of the fruit. A histogram analysis was carried out for the RGB and HSV colour spaces, where the Red and Hue components showed differences between each of the specified colour patterns of the exporting reference system. This information led to the development of a hybrid Bayesian classification algorithm based on the components R and H. Its accuracy was tested with a set of cherry samples within the colour range of interest. The algorithm was implemented by means of a real time C++ code in Microsoft Visual Studio environment. When testing, the algorithm showed a 100% effectiveness in classifying a sample set of cherries into the five standardized cherry classes. The components of the hardware-software system for implementing the methodology are low cost, thus ensuring an affordable commercial deployment.

Flesh of pumpkin from ecological farming as part of fruit suitable for non-destructive cultivar classification using computer vision

The aim of this study was to build the discriminative models for distinguishing the different cultivars of flesh of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ based on selected textures of the outer surface of images of cubes. The novelty of research involved the use of about 2000 different textures for one image. The highest total accuracy (98%) of discrimination of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ was determined for models built based on textures selected from the color space Lab and the IBk classifier and some of the individual cultivars were classified with the correctness of 100%. The total accuracy of up to 96% was observed for color space RGB and 97.5% for color space XYZ. In the case of color channels, the total accuracies reached 91% for channel b, 89.5% for channel X, 89% for channel Z.

Pixel-Based Image Processing for CIE Standard Sky Classification through ANN

Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image-processing methods are applied. While the use of the traditional RGB color space for image-processing yielded good results (ANN accuracy equal to 86.6%), other color spaces, such as Hue Saturation Value (HSV), which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image-processing methods could improve ANN-algorithm accuracy, depending on the image information required for the classification problem.

Using Texture Analyses and Statistical Classification for Detection Plant Leaf Diseases

The proposed method is based on classifying 15 types of plant leaf disease. Hue saturation value was used to delete the background and the healthy areas to show only the affected area in the image. Texture analyses adopted in image features extractions from the R component &G component &B component and creating 3 components which are RG and RB and GB color of the RGB color space images of diseased leaves. Building image classifier using statistical method for classification.

Steganography on Digital Color Image Using Modulo Function and Pseudo-Random Number Generator

In this era, many people exchange data digitally either through the internet or other communication channels. However, data sent digitally can be seen by unauthorized people. Therefore, data security is essential. One of the data security techniques is steganography. This study employs steganography based on Pixel Value Modification (PVM) method using the modulo function. This technique will be implemented to insert a text message in digital images in RGB color space. Besides, we use the Pseudo-Random Number Generator with a secret key to enhance the inserted messages’ security. To measure the performance of the proposed method, testing is carried out by comparing the stego image to cover image based on three criteria, namely imperceptibility, fidelity, and recovery. PVM method using modulo function successfully hides text that the length is less than 200 characters into 255×255-pixel color images. Imperceptibility testing is done by distributing a questionnaire to six people randomly. The results showed that all respondents answered that each test image's stego image and cover image had no difference. At the same time, the results of the fidelity test show that the MSE value is close to zero, and the PSNR value is above 40 dB. Furthermore, recovery testing will check whether the extracted message is the same as the secret message inserted in the cover image. The results showed that all messages inserted were extracted correctly for recovery criteria if given the correct secret key.