HSV Color Research Articles

Automatic State Recognition of Multi-type Protection Platens based on YOLOv5 and Parallel Multi-Decision Algorithm

A protection platen is a vital component in relay protection systems. The manual inspection of protection platen states is long-term repetitive work with low efficiency and imposes a heavy burden on workers. In this work, we propose a new system to automatically detect the states of multi-type protection platens in images. This system can classify two protection platen categories and further recognize the states of protection platens. For the classification of protection platen types, we propose a new algorithm that automatically detects two protection platen types based on HSV (Hue, Saturation, Value) color space weighting operators. The proposed operators quantify the color variation in the protection platen and reduce the influence of environmental factors. With respect to the state recognition of protection platens, the Type-I protection platen states are automatically classified by the YOLOv5 (You only look once version 5) network. Since the Type-II protection platen has three primary states and more complicated structures, we investigate a new parallel multi-decision algorithm to recognize the states of Type-II protection platens based on the newly proposed watershed-color space difference-shape feature (W-CD-SF) method and the YOLOv5 network. The W-CD-SF technique can segment the protection platens and extract their shape features automatically. This multi-decision mechanism improves the robustness and generalization of state recognition. Experiments were conducted on the collected protection platen images containing 8,969 protection platens. The recognition accuracies of protection platen states exceed 95%. This system can provide auxiliary detection and long-term monitoring of protection platen states.

MCCA-Net: Multi-color convolution and attention stacked network for Underwater image classification

Underwater images are serious problems affected by the absorption and scattering of light. At present, the existing sharpening methods can't effectively solve all underwater image degradation problems, thus it is necessary to propose a specific solution to the degradation problem. To solve the above problems, the Multi-Color Convolutional and Attentional Stacking Network (MCCA-Net) for Underwater image classification are proposed in this paper. First, an underwater image is converted to HSV and Lab color spaces and fused to achieve a refined image. Then, the attention mechanism module is used to fine the extracted image features. At last, the vertically stacked convolution module fully utilizes different levels of feature information, which realizes the fusion of convolution and attention mechanism, optimizes feature extraction and parameter reduction, and improves the classification performance of the MCCA-Net model. Extensive experiments on underwater degraded image classification show that our MCCA-Net model and method outperform other models and improve the accuracy of underwater degraded image classification. Our image fusion method can achieve 96.39% accuracy on other models, and the MCCA-Net model achieves 97.38% classification accuracy.

INTERDISCIPLINARY EDUCATION THROUGH THE DEVELOPMENT OF A COST-EFFECTIVE PHOTOMETRIC pH METER SENSOR USING NATURAL PIGMENTS

There is a trend of development of analytical methodologies and technologies that allow in situ analysis, producing accurate information in real time, at low cost, using homemade experiments and devices to increase interest in scientific knowledge with a constructive approach in an interdisciplinary perspective in Chemistry Education. In this context, a photometric-chemometric method of analysis was developed to measure the pH of solutions using easily accessible and low-cost material based on the use of natural pigments found in red cabbage (Brassica oleracea L.). Calibrations and determinations were performed by RGB measurements of pigment coloration in solution at different pH values using the free app Photometrix, converted to HSV, YCbCr and YUV color spaces and processed by Partial Least Squares regression (PLS). The best PLS model found was HSV with mean central scale obtained pH measurements with RMSEP 0.98, R2 0.97 and bias close to zero. In addition, experimental data were statistically validated. Analyzes of predicted pH from three independents experiments revealed high recoveries (95-103%) and low relative standard deviations. Thus, the PhotoMetrix app was viable for colorimetric pH determinations using the low-cost photometric pH meter sensor and a smartphone, improving accessibility and applicability in Chemistry Education.

Method for Correcting Low-illumination Images Based on Adaptive Two-dimensional Gamma Function

A two-dimensional gamma function-based color image correction method based on the illumination-reflection model and multi-scale theory is proposed. Firstly, the original image is histogram equalized, then converted to HSV color space, and the light components of the scene are extracted by multi-scale Gaussian function, and then the parameters of the 2D gamma function are adjusted by the distribution characteristics of the light components to construct a 2D gamma function to enhance the image. Finally, the images are fused. The comparison with the classical algorithm shows that the algorithm in this paper can better reduce the effect of uneven illumination on the image and improve the brightness and contrast of the image.

Vision‐Based Dynamic Displacement Measurement of Isolation Bearing

The existing computer vision‐based structural displacement measurement method can only obtain structural displacement when the camera is fixed or the pose of the camera is known at all times. However, the aforementioned conditions cannot be met when an earthquake occurs. Therefore, this study presents a method to achieve a dynamic displacement measure of an isolated bearing from a video without the known camera motion: two cameras are set up along the horizontal orthogonal direction, and circular targets are set at the upper and lower connecting plates of the isolation bearing. A combination of HSV color space, Canny edge detection, and Hough transform circle detection is used to obtain the dynamic displacement of the isolation bearing. This task is done by measuring the relative displacement between the upper and lower target points. A basic mechanical performance test of LRB500 isolation bearing is used to verify the proposed method. The result shows that the horizontal displacement time history of the isolation bearing obtained by this method is almost the same as the result obtained by the displacement meter whether the camera is moving or fixed. However, compared with the results measured by the displacement meter, the absolute error of the peak horizontal and vertical displacements are both less than 1 mm, which indicates that the proposed method can be used in displacement monitoring under long‐term load and earthquake action of isolation bearing.

User Study Comparing Linearity and Orthogonalization for Polarimetric Visualizations

Traditionally, polarimetric imaging data is visualized by mapping angle of polarization, degree of polarization, and intensity to hue, saturation, and value coordinates of HSV color space. Due to possible perceptual uniformity issues in HSV, a method based on CAM02-UCS color space has been recently proposed. In this user study, the perceptual uniformity and nonlinear bias of the encoding of the degree of polarization parameter into the chromatic magnitude color channel is modeled by a power-law relationship between stimulus scale level and is estimated from responses to paired 2-alternative forced choice questions using Maximum Likelihood Difference Scaling. Estimated exponent and noise parameters for these methods are compared for same-hue and different-hue conditions to determine whether the chromatic magnitude channel can be used to orthogonality encode data parameters independently from the hue channel. Overall, the HSV condition displayed more nonuniformity, more nonlinear bias, and more non-orthogonality than the UCS condition. The results here indicate a lower bound for differences between methods since the intensity was chosen for the “best case” of HSV. These results further support the claim that the chromatic magnitude color channel of a uniform color space can be used to encode a data parameter independently of the hue channel in a multivariate colormapping visualization.

An improved ViBe-based approach for moving object detection

Moving object detection is a challenging task in the automatic monitoring field, which plays a crucial role in most video-based applications. The visual background extractor (ViBe) algorithm has been widely used to deal with this problem due to its high detection rate and low computational complexity. However, there are some shortcomings in the general ViBe algorithm, such as the ghost area problem and the dynamic background problem. To deal with these problems, an improved ViBe approach is presented in this paper. In the proposed approach, a mode background modeling method is used to accelerate the process of the ghost elimination. For the detection of moving object in dynamic background, a local adaptive threshold and update rate is proposed for the ViBe approach to detect foreground and update background. Furthermore, an improved shadow removal method is presented, which is based on the HSV color space combined with the edge detection method. Finally, some experiments were conducted, and the results show the efficiency and effectiveness of the proposed approach.

Hand Detection on HSV Color Space Model and Syntactic Extraction of Fingertip by Thinning Method for Hand Gesture Recognition

Hand Detection on HSV Color Space Model and Syntactic Extraction of Fingertip by Thinning Method for Hand Gesture Recognition

Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions. Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions. BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type. The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images. The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.

Pengaruh HSV Pada Pengolahan Citra Untuk Kematangan Buah Cabai

Image or image recognition has quite complex stages so that the computer is able to recognize what objects to identify, there are several stages used in the image processing process, image extraction data input and image classification, this study will examine the extent to which the algorithm for extraction have an influence on the final classification results. This study uses the image of chili as a test material and the results of the test are the introduction of chili with 4 levels, ripe, half cooked, raw and rotten. This study also uses tools to perform the image recognition process. The image recognition method used in this study is Hue Saturation Value (HSV) Gray Level Co-occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ3) for classification, in the process it will be tested. With 2 image recognition scenarios, the first method uses GLCM and LVQ3, the second scenario uses HSV GLCM and LVQ3. The results of this study are to see how much influence HSV has on the digital image recognition process of chili, the extraction results using GLCM extraction LVQ3 classification is 59.58%, and the results of LVQ3 classification using HSV and GLCM extraction yields 93.58%. In conclusion, the HSV extraction method provides an additional 34% accuracy compared to using only GLCM as the extraction method

Klasifikasi Jenis Jamur Menggunakan SVM dengan Fitur HSV dan HOG

Mushrooms are one of the plants that have so many varieties. Every variety has a different shape and color. But most people still feel difficult to know and classify every mushroom. Therefore, classification for mushroom is needed. Method for this research are Hue Saturation Value (HSV) as color segmentation, then Histogram of Oriented Gradient (HOG) as feature extraction, and Support Vector Machine (SVM) as a classification method. Mushrooms that being use are Agaricus, Amanita, Boletus, Cortinarius, Entoloma, Hygrocybe, Lactarius, Russula, Suillus. Total of mushrooms for this research are 900, with 100 each genus. This research using the k-fold Cross Validation method for 4-fold. From 900 images there are 675 for the training phase and 225 for the testing phase. Overall for this research got precision, recall, accuracy respectively 23.80%, 22.94%, and 82.69%. The best mushroom was Boletus with precision, recall, accuracy respectively 55.37%, 46.84%, and 89.69%.

Object Tracking Using HSV Values and OpenCV

Abstract: This research shows how to use colour and movement to automate the process of recognising and tracking things. Video tracking is a technique for detecting a moving object over a long distance using a camera. The main purpose of video tracking is to connect target objects in subsequent video frames. The connection may be particularly troublesome when things move faster than the frame rate. Using HSV colour space values and OpenCV in different video frames, this study proposes a way to track moving objects in real-time. We begin by calculating the HSV value of an item to be monitored, and then we track the object throughout the testing step. The items were shown to be tracked with 90 percent accuracy. Keywords: HSV, OpenCV, Object tracking, Video frames, GUI

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.

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.

Fast outdoor hazy image dehazing based on saturation and brightness

Haze usually limits the visibility and reduces the contrast of outdoor images. The removal of haze in a single image has always been a challenging problem. Recently, many methods have been proposed to effectively remove haze in harsh conditions. However, these methods fail to balance the dehazing effect and time consumption. This paper proposes a method based on HSV colour space to restore the visibility of uniform scattering medium. It can prevent the atmospheric light and transmission from being miscalculated. This method uses the brightness component of haze image to estimate the global atmospheric light, which reduces the influence of luminescent objects on atmospheric light estimation. Then, this paper deduces the estimation model of the saturation of scene radiance based on the atmospheric scattering model. In the meantime, according to the advantages and problems of the model, the fast estimation of the transmission is realized by using the stretching function. Finally, this paper solves the parameters in the model by iterative method. Since this method can estimate different media transmission for each pixel, better results can be obtained. The simulation results show that the algorithm is superior to the existing algorithms in terms of dehazing effect and time consumption.

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.

QIRHSI: novel quantum image representation based on HSI color space model

QIRHSI: novel quantum image representation based on HSI color space model

Real-time motion detection with candidate masks and region growing for moving cameras

A fast and reliable motion detection method is proposed for moving cameras. Existing methods generally build background models and then apply background subtraction for moving pixels detection. In background modeling, statistical methods and/or features, such as edges, local binary patterns are used. Parameters of the proposed methods need to be updated adaptively for better performance. In recent studies, adaptive learning rates are used with main and candidate background models. Simple and adaptive thresholding methods are used for foreground detection in different studies. We build a statistical background model with hue–saturation–value color space and perform background subtraction with the contribution of optical flow vectors. Dense optical flow is applied between warped the background model and the current frame to estimate optical flow vectors. If a pixel has a larger magnitude, we set a weight value for each pixel in the background subtraction process. Then, we extract two candidate foreground masks and apply some postprocessing techniques to determine the final mask. Our proposed method works fast in real time, and the experimental results on two different datasets show that our approach has the highest performance compared with the methods in the literature.

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.

An improved dark channel defogging algorithm based on the HSI colour space

When the traditional dark channel prior (DCP) algorithm is used for image defogging, it faces some problems such as unsatisfactory restoration of the target edge details, distortion in large-scale and high-brightness sky regions. To solve these problems, this paper proposes an improved dark channel defogging algorithm based on the HSI colour space, named the IDCP algorithm. First, an asymmetric mean filter window is used to obtain the modified dark channel image to improve the grey value mutation in the traditional dark channel image. Based on this work, an improved global atmospheric transmittance calculation method is proposed, which effectively improves the inapplicability of the dark channel prior in large-scale and high-brightness sky regions. Finally, the Laplacian operator is used to sharpen the image to enhance the target edge information. Simulation results show that the proposed algorithm can effectively solve the distortion phenomenon caused by the traditional DCP algorithm when handling large-scale and high-brightness sky regions, improve the target edge details, and restore the true colour of the image scene.