Color Features Research Articles

Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor

This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.

The Color Harmony Estimation Model Construction Based on Two Layers of MLE and BPNN in the Color Matching Field

In the field of art, color matching is widely used in various art designs, such as images, posters, clothing, and interior home design. Among them, harmonious color matching is the decisive factor in whether a design is popular or not. To solve the problem of estimating color harmony, this study analyzes from the perspective of color pairs and uses the two-layer maximum likelihood estimation method to make preliminary predictions of color harmony by statistically modeling paired color preferences in existing datasets. After obtaining the preliminary estimation of color harmony, multiple linear regression is selected for denoising processing. Subsequently, the preliminary prediction results were refined using a backpropagation neural network, extracting various color features in different color spaces, and ultimately obtaining accurate harmony estimates. The results indicate that, compared with existing methods, the proposed method can simulate the aesthetic cognition of different users towards different color themes. Under the same statistical method, the model can maintain good harmony estimation and experimental results. This method can promote the development of related research fields, such as quickly evaluating the color harmony of an image, and one click color changing in scenes such as clothing, home, 3D models, etc. according to different user needs.

Classification of Open and Closed Pistachio Shells Using Machine Vision Approach

Pistachio nuts are a type of nut that is widely consumed around the world due to their high nutritional value and pleasant taste. Pistachios are usually sold in their shells, either open or closed. However, closed-shell pistachios are not well received by consumers, resulting in a lower commercial value. It is essential to be able to distinguish between open and closed pistachio shells in order to ensure quality control during production processes and processing. This can be done manually or by using mechanical devices. Manual inspection and categorization of pistachio nuts have traditionally been done by workers, but this process is inefficient in terms of time and money. Mechanical separation of open and closed-shell pistachio can damage the kernels of open-shell nuts due to the needle mechanism used in the sorting process. This study aims to classify pistachio nuts using a machine vision-based system and evaluate its applicability in terms of classification accuracy. The system is evaluated on the Antep pistachio species, which can be distinguished from other pistachio varieties, such as Siirt and Urfa pistachios, based on their shape, size, and taste properties. The machine vision system in this study classifies pistachio nuts into closed and open shell classes in a completely automated manner. In this study, 1,000 Antep pistachio nuts images were obtained and examined, including 500 open and 500 closed nuts. The images were pre-processed and prepared for feature extraction. From the images, a total of 14 color features were extracted. Although the single feature was used, promising classification accuracy rates of 95.6%, 94.8%, and 93.6% from the Random Forest, Support Vector Machine (SVM), and Logistic Regression were achieved, respectively. The performances of classifiers were compared to each other. Almost similar performances were detected. These results demonstrate that the Random Forest classifier is the most effective algorithm for classifying open and closed Antep pistachio nuts.

Machine learning-enabled attapulgite/polyimide nanofiber composite aerogels-based colorimetric sensor array for real-time monitoring of balsa fish freshness

This paper presents the development and application of attapulgite/polyimide nanofiber composite aerogels (ATP/PI NFAs) integrated with a range of acid-base indicators, fabricated using electrospinning and freeze-drying technologies. A detailed characterization of the ATP/PI NFAs revealed a 3D multi-level pore structure that enhanced the mass transfer of target gas molecules and their interaction with probe molecules. Utilizing machine learning approaches, we designed an ATP/PI NFAs-based colorimetric sensor array capable of real-time evaluation of balsa fish freshness. Color features sensitive to changes in freshness were selected using principal component analysis and random forest. Partial least squares regression and random forest regression models were established, achieving the prediction of total volatile basic nitrogen content in balsa fish. The system was validated using a national standard method to demonstrate its accuracy and practicality. The combination of advanced ATP/PI NFAs-based colorimetric sensor array with robust machine learning models paves the way for food safety monitoring.

A LiDAR-Camera Joint Calibration Algorithm Based on Deep Learning.

Multisensor (MS) data fusion is important for improving the stability of vehicle environmental perception systems. MS joint calibration is a prerequisite for the fusion of multimodality sensors. Traditional calibration methods based on calibration boards require the manual extraction of many features and manual registration, resulting in a cumbersome calibration process and significant errors. A joint calibration algorithm for a Light Laser Detection and Ranging (LiDAR) and camera is proposed based on deep learning without the need for other special calibration objects. A network model constructed based on deep learning can automatically capture object features in the environment and complete the calibration by matching and calculating object features. A mathematical model was constructed for joint LiDAR-camera calibration, and the process of sensor joint calibration was analyzed in detail. By constructing a deep-learning-based network model to determine the parameters of the rotation matrix and translation matrix, the relative spatial positions of the two sensors were determined to complete the joint calibration. The network model consists of three parts: a feature extraction module, a feature-matching module, and a feature aggregation module. The feature extraction module extracts the image features of color and depth images, the feature-matching module calculates the correlation between the two, and the feature aggregation module determines the calibration matrix parameters. The proposed algorithm was validated and tested on the KITTI-odometry dataset and compared with other advanced algorithms. The experimental results show that the average translation error of the calibration algorithm is 0.26 cm, and the average rotation error is 0.02°. The calibration error is lower than those of other advanced algorithms.

Multi-Feature Extraction and Selection Method to Diagnose Burn Depth from Burn Images

Burn wound depth is a significant determinant of patient treatment. Typically, the evaluation of burn depth relies heavily on the clinical experience of doctors. Even experienced surgeons may not achieve high accuracy and speed in diagnosing burn depth. Thus, intelligent burn depth classification is useful and valuable. Here, an intelligent classification method for burn depth based on machine learning techniques is proposed. In particular, this method involves extracting color, texture, and depth features from images, and sequentially cascading these features. Then, an iterative selection method based on random forest feature importance measure is applied. The selected features are input into the random forest classifier to evaluate this proposed method using the standard burn dataset. This method classifies burn images, achieving an accuracy of 91.76% when classified into two categories and 80.74% when classified into three categories. The comprehensive experimental results indicate that this proposed method is capable of learning effective features from limited data samples and identifying burn depth effectively.

An Accurate Strain Gauge Positioning Approach Based on Geometry and Color Features

An Accurate Strain Gauge Positioning Approach Based on Geometry and Color Features

Classification of wheat flour levels in powdered spices using visual imaging

The present research aims to investigate the ability of image processing in the detection of wheat flour adulterant in different spices. Different samples of black pepper, red pepper, and cinnamon were prepared with different levels of wheat flour adulteration. The images of the samples were captured using a visible camera. An image processing algorithm was designed and codded in MATLAB software to process the acquired images. Different color and textural features were extracted. The efficient features were selected and classified using artificial neural network and support vector machine. In the present research, the classification accuracies of artificial neural network analysis for black pepper, red pepper, and cinnamon products were 97.8, 100.00, and 100.00 %, respectively. During the experiment, these results were better than the performance of the support vector machine, (93.33, 100.00, and 98.88 %, respectively). Visible image processing can be used accurately to detect different adulterant levels that increase product quality while reducing operating costs.

CCM-Net: Color compensation and coordinate attention guided underwater image enhancement with multi-scale feature aggregation

Due to the light scattering and wavelength absorption in water, underwater images exhibit blurred details, low contrast, and color deviation. Existing underwater image enhancement methods are divided into traditional methods and deep learning-based methods. Traditional methods either rely on scene prior and lack robustness, or are not flexible enough resulting in poor enhancement effects. Deep learning methods have achieved good results in the field of underwater image enhancement due to their powerful feature representation ability. However, these methods cannot enhance underwater images with various degradations because they do not consider the inconsistent attenuation of different color channels and spatial regions. In this paper, we propose a novel asymmetric encoder-decoder network for underwater image enhancement, called CCM-Net. Concretely, we first introduce the prior knowledge-based encoder, which includes color compensation (CC) modules and feature extraction modules that consist of depth-wise separable convolution and global-local coordinate attention (GLCA). Then, we design a multi-scale feature aggregation (MFA) module to integrate shallow, middle, and deep features. Finally, we deploy a decoder to reconstruct the underwater images with the extracted features. Extensive experiments on publicly available datasets demonstrate that our CCM-Net effectively improves the visual quality of underwater images and achieves impressive performance.

An ultra-high-definition multi-exposure image fusion method based on multi-scale feature extraction

Multiple exposure image fusion is a technique used to obtain high dynamic range images. Due to its low cost and high efficiency, it has received a lot of attention from researchers in recent years. Currently, most deep learning-based multiple exposure image fusion methods extract features from different exposure images using a single feature extraction method. Some methods simply rely on two different modules to directly extract features. However, this approach inevitably leads to the loss of some feature information during the feature extraction process, thus further affecting the performance of the model. To minimize the loss of feature information as much as possible, we propose an ultra-high-definition (UHD) multiple exposure image fusion method based on multi-scale feature extraction. The method adopts a U-shaped structure to construct the overall network model, which can fully exploit the feature information at different levels. Additionally, we construct a novel hybrid stacking paradigm to combine convolutional neural networks and Transformer modules. This combined module can extract both local texture features and global color features simultaneously. To more efficiently fuse and extract features, we also design a cross-layer feature fusion module, which can adaptively learn the correlation between features at different layers. Numerous quantitative and qualitative results demonstrate that our proposed method performs well in UHD multiple exposure image fusion.

Machine learning with analysis-of-variance-based method for identifying rice varieties

Rice, which has the highest production and consumption rates worldwide, is one of the main nutrients in our country because it is economical and nutritious. Rice undergoes various stages of production from the field to the dinner tables, with the cleaning phase involving separation of rice from unwanted materials. There are different varieties of rice such as arborio, basmati, aracadaga, ipsala, and jasmine. Therefore, it is necessary to identify rice varieties based on customer demands. Rice variety identification is primarily based on its outer appearance, including color, size, and shape. In this study, 75,000 rice grain images, including 15,000 images for each variety, were considered. Using a combination of 12 morphological features, four shape features, and 90 color features obtained from five different color spaces, 106 features were extracted from the images. An analysis of variance (ANOVA) was employed to select high-rank features, which were then fed to a support vector machine (SVM) for classification. The experimental results demonstrated that the utilization of 40 high-rank features yielded impressive outcomes, with a validation accuracy of 99.89 %, cost value of 16, test accuracy of 99.81 %, and cost value of 28. These high accuracy rates are particularly noteworthy, as they indicate the robustness and reliability of the proposed methodology in distinguishing between different rice varieties with minimal error. This high level of precision is critical for practical applications, such as quality control in rice production, to ensure that customers receive the specific rice variety they demand. Implementing this methodology in real-world scenarios could significantly enhance the efficiency and accuracy of the rice variety identification processes, benefiting both producers and consumers.

Image Processing and Support Vector Machine (SVM) for Classifying Environmental Stress Symptoms of Pepper Seedlings Grown in a Plant Factory

Environmental factors such as temperature, humidity, light, and CO2 influence plant growth, and unfavorable environmental conditions cause stress in plants, producing symptoms in their early growth stages. The increasing importance of optimizing crop management strategies has led to a rising demand for the precise evaluation of stress symptoms during early plant growth. Advanced technologies are transforming plant health monitoring through enabling image-based stress analysis. Machine learning (ML) models can effectively identify the important features and morphological changes connected with various stress conditions through the use of large datasets acquired from high-resolution plant images. Therefore, the objective of this study was to develop a method for classifying the early-stage stress symptoms of pepper seedlings and enabling their identification and quantification using image processing and a support vector machine (SVM). Two-week-old pepper seedlings were grown under different temperatures (20, 25, and 30 °C), light intensity levels (50, 250, and 450 µmol m−2s−1), and day–night hours (8/16, 10/14, and 16/8) in five controlled plant growth chambers. Images of the seedling canopies were captured daily using a low-cost red, green, and blue (RGB) camera over a two-week period. Eighteen color features, nine texture features using the gray-level co-occurrence matrix (GLCM), and one morphological feature were extracted from each image. A two-way ANOVA and multiple mean comparison (Duncan) analysis were used to determine the statistical significance of the treatment effects. To reduce feature overlap, sequential feature selection (SFS) was applied, and a support vector machine (SVM) was used for stress classification. The SFS method was used to identify the optimal features for the classification model, leading to substantial increases in stress classification accuracy. The SVM model, using these selected features, achieved a classification accuracy of 82% without the SFS and 86% with the SFS. To address overfitting, 5- and 10-fold cross-validation were used, resulting in MAEs of 0.138 and 0.163 for the polynomial kernel, respectively. The SVM model, evaluated with the ROC curve and confusion matrix, achieved a classification accuracy of 85%. This classification approach enables real-time stress monitoring, allowing growers to optimize environmental conditions and enhance seedling growth. Future directions include integrating this system into automated cultivation environments to enable continuous, efficient stress monitoring and response, further improving crop management and productivity.

A framework of insole blanking robot based on adaptive edge detection and FSPS-BIT* path planning

Insole blanking production technology plays a vital role in contemporary machining and manufacturing industries. Existing insole blanking production models have limitations because most robots are required to accurately position the workpiece to a predetermined location, and special auxiliary equipment is usually required to ensure the precise positioning of the robot. In this paper, we present an adaptive blanking robotic system for different lighting environments, which consists of an industrial robot arm, an RGB-D camera configuration, and a customized insole blanking table and mold. We introduce an innovative edge detection framework that utilizes color features and morphological parameters optimized through particle swarm optimization (PSO) techniques to Adaptive recognition of insole edge contours. A path planning framework based on FSPS-BIT* is also introduced, which integrates the BIT* algorithm with the FSPS algorithm for efficient path planning of the robotic arm.

Harnessing AI for precision tonsillitis diagnosis: a revolutionary approach in endoscopic analysis.

Diagnosing and treating tonsillitis pose no significant challenge for otolaryngologists; however, it can increase the infection risk for healthcare professionals amidst the coronavirus pandemic. In recent years, with the advancement of artificial intelligence (AI), its application in medical imaging has also thrived. This research is to identify the optimal convolutional neural network (CNN) algorithm for accurate diagnosis of tonsillitis and early precision treatment. Semi-supervised learning with pseudo-labels used for self-training was adopted to train our CNN, with the algorithm including UNet, PSPNet, and FPN. A total of 485 pharyngoscopic images from 485 participants were included, comprising healthy individuals (133 cases), patients with the common cold (295 cases), and patients with tonsillitis (57 cases). Both color and texture features from 485 images are extracted for analysis. UNet outperformed PSPNet and FPN in accurately segmenting oropharyngeal anatomy automatically, with average Dice coefficient of 97.74% and a pixel accuracy of 98.12%, making it suitable for enhancing the diagnosis of tonsillitis. The normal tonsils generally have more uniform and smooth textures and have pinkish color, similar to the surrounding mucosal tissues, while tonsillitis, particularly the antibiotic-required type, shows white or yellowish pus-filled spots or patches, and shows more granular or lumpy texture in contrast, indicating inflammation and changes in tissue structure. After training with 485 cases, our algorithm with UNet achieved accuracy rates of 93.75%, 97.1%, and 91.67% in differentiating the three tonsil groups, demonstrating excellent results. Our research highlights the potential of using UNet for fully automated semantic segmentation of oropharyngeal structures, which aids in subsequent feature extraction, machine learning, and enables accurate AI diagnosis of tonsillitis. This innovation shows promise for enhancing both the accuracy and speed of tonsillitis assessments.

Pollinator response to yellow UV-patterned versus white UV-patternless flower dimorphism in Anemone palmata.

Flower colour polymorphisms are uncommon but widespread among angiosperms and can be maintained by a variety of balancing selection mechanisms. Anemone palmata is mostly yellow-flowered, but white-flowered plants coexist in some populations. We analysed the distribution of colour morphs of A. palmata across its range. We also characterised their colours and compared their vegetative and sexual reproductive traits, pollinator attention and fitness. The range of A. palmata is limited to the Western Mediterranean, while white-flowered plants are restricted to Portugal and SW Spain, where they occur at low proportions. Yellow flowers have a characteristic UV pattern, with a UV-absorbing centre and UV-reflecting periphery, which is absent in the white morph. Colour features of both morphs were highly delineated, making it easy for pollinators to distinguish them. Both morphs were protogynous, with the same duration of sexual stages, and the main floral traits related to pollinator attraction, apart from flower colour, were similar. Hymenoptera and Diptera were the main pollinators, showing preference for the yellow morph, clear partitioning of pollinator groups between the two colour morphs and a marked constancy to flower colour during foraging. Both morphs combined clonal propagation with sexual reproduction, but sexual reproductive potential was lower in white-flowered plants. Finally, female fitness was higher in the yellow morph. Pollinator partitioning and colour constancy could maintain this polymorphism, despite the lower visitation rate and fitness of white-flowered plants, which could facilitate their clonal propagation.

A lightweight color and geometry feature extraction and fusion module for end-to-end 6D pose estimation

Although advancements in red–green–blue-depth (RGB-D)-based six degree-of-freedom (6D) pose estimation methods, severe occlusion remains challenging. Addressing this issue, we propose a novel feature fusion module that can efficiently leverage the color and geometry information in RGB-D images. Unlike prior fusion methods, our method employs a two-stage fusion process. Initially, we extract color features from RGB images and integrate them into a point cloud. Subsequently, an anisotropic separable set abstraction network-like network is utilized to process the fused point cloud, extracting both local and global features, which are then combined to generate the final fusion features. Furthermore, we introduce a lightweight color feature extraction network to reduce model complexity. Extensive experiments conducted on the LineMOD, Occlusion LineMOD, and YCB-Video datasets conclusively demonstrate that our method significantly enhances prediction accuracy, reduces training time, and exhibits robustness to occlusion. Further experiments show that our model is significantly smaller than the latest popular 6D pose estimation models, which indicates that our model is easier to deploy on mobile platforms.

The color features and k-nearest neighbor algorithm for classifying betel leaf image

Piper betle L. (betel) is a species that belongs to the genus Piper and is a type of medicinal plant that is quite well known to the general population. The varieties of the leaf color may distinguish are red, green, and black betel. However, consumers still need assistance determining the differences between the many types of betel leaf. Therefore, using image processing techniques, this research contributes to building a classification method for distinguishing betel leaves based on color attributes. This approach anoints for the region of interest detection, feature extraction, and classification. In addition, three different classifiers, naïve Bayes, support vector machine, and k-nearest neighbors (k-NN), were used during the classification process. The evaluation for this study used a percentage split to divide a total of 180 images between the training and testing phases. The method’s performance provided the highest accuracy value possible, 100%, by utilizing the color characteristics with the k-NN classifier.

Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods.

Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moderate processing. In this study, a self-established grain image acquisition platform was built. Degree of bran layer remaining (DOR) datasets is established with image capturing and processing (grain color, texture, and shape features extraction). The mapping relationship between DOR and the DOM is in-depth analyzed. Rice grain DOR typical machine learning and deep learning prediction models are established. The results indicate that the optimized Catboost model can be established with cross-validation and grid search method, with the best accuracy improving from 84.28% to 91.24%, achieving precision 91.31%, recall 90.89%, and F1-score 91.07%. Shapley additive explanations analysis indicates that color, texture, and shape feature affect Catboost prediction accuracy, the feature importance: color>texture>shape. The YCbCr-Cb_ske and GLCM-Contrast features make the most significant contribution to rice milling quality prediction. The feature importance provides theoretical and practical guidancefor grain DOM prediction model. PRACTICAL APPLICATION: Rice milling degree prediction and detection are valuable for rice milling process in practical application. In this paper, image processing and machine learning methods provide an automated, nondestructive, and cost-effective way to predict the quality of rice. The study may serve as a valuable reference for improving rice milling methods, retaining rice nutrition, and reducing broken rice yield.

MACHINE LEARNING ALGORITHM FOR RAPID OBJECT DETECTION BASED ON COLOR FEATURES

The identification of objects is of utmost importance in a wide range of computer vision applications, such as surveillance systems, autonomous cars, and environmental monitoring. Accurate and efficient object recognition methods are crucial in pastoral environments, characterized by the prominent presence of cattle and other objects, to provide effective analysis and decision-making processes. The present study paper introduces an innovative methodology for efficient identification of objects in pastoral landscapes through the utilization of a Colour Feature Extreme Learning Machine (CF-ELM). The CF-ELM method integrates color characteristics with the ELM algorithm to attain enhanced object detection accuracy while preserving computational economy. The experimental findings provide empirical evidence supporting the efficacy and efficiency of the suggested approach in the detection of items within pastoral landscapes. In addition to the CF-ELM, an algorithm for desktop-based categorization of items within a pastoral environment is provided, with individual speeds ranging from 0.05 s to 0.17 s for a single image, evaluated in each color space. The algorithm is intended for usage in scenarios with challenging and variable terrain, making it appropriate for application in agricultural or pastoral settings.

Plant Monitoring System Using Gray Level Co-occurrence Matrix and Weight Based Artificial Neural Network Algorithm over Internet of Things

The potential deployment of the plant monitoring system in cutting-edge technologies is currently generating a great deal of interest. A new IoT-based technology is used to track both the development and wellness of the plant. Setting up cross-device connectivity over the internet is the idea underlying IoT devices. It is a sizable network that links people and various interconnected elements in order to gather and transmit data. This research article aims to generate a manufacturing Internet of Things (IoT) based plant monitoring system that uses IoT sensors to detect environmental conditions. The IoT term is used to link objects to the internet and make it easier for consumers to obtain data. The technology can accurately perceive the surroundings in agriculture and convey the data to users. The system keeps track of several factors like soil moisture, temperature, and light intensity. The method begins with the collection of plant images and noise reduction pre-processing. Once the images have been segmented, they are done so utilizing the Region based K-Means Clustering (RKMC) technique. Following that, Gray Level Co-occurrence Matrix (GLCM) is utilized to extract features, with a focus on extracting the more informative characteristics like color, texture, and shape features. The classifying process is completed by utilizing Weight based Artificial Neural Network (WANN) algorithm for improving the plant monitoring system performance significantly. It offers IoT-based solutions to categorize plant illnesses and observe variables including soil moisture, air temperature, and pH values. Te findings of the suggested GLCM-WANN algorithm show better performers than existing methods for obtained values of computation complexity, accuracy, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).