Color Extraction Method Research Articles

Digital Visual Design Reengineering and Application Based on K-means Clustering Algorithm

INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability.OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved K-means clustering algorithm.METHODS: A visual gene extraction method based on an improved K-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based K-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments.RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the K-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the K-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the K-means-JSO algorithm converges faster and with better accuracy compared to the K-means-ABC, K-means-GWO, K-means-DE, K-means-CMAES, and K-means-WWCD algorithms.CONCLUSION: The color extraction method of the K-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalization ability, and inefficiency in visual gene extraction methods.

Research on the Application of Chinese Cultural Elements in Product Packaging Design under the Background of Big Data

Abstract The K-means color extraction method based on clustering is used to extract Chinese cultural color elements in this paper, and they are intelligently laid out to design product packaging colors. In the processing of graphic elements, a SIFT-based algorithm is used to match the feature points, and the Chinese cultural graphic elements are aligned to design the graphics of product packaging. To investigate the extraction and application effects of color elements and graphic elements, a validation test was conducted on this basis. The results show that the overall effect value of traditional packaging color design is between [0.6, 0.7], the overall effect value of intelligent packaging design based on Chinese cultural elements is between [0.88,0.96], and the overall effect of the application of graphic elements of Chinese culture to product packaging design is high, with a difference of about 0.1 from the expected effect. Based on this study is able to apply the elements of Chinese culture to product packaging design.

Saturation-Based Airlight Color Restoration of Hazy Images

Typically, images captured in adverse weather conditions such as haze or smog exhibit light gray or white color on screen; therefore, existing hazy image restoration studies have performed dehazing under the same assumption. However, hazy images captured under actual weather conditions tend to change color because of various environmental factors such as dust, chemical substances, sea, and lighting. Color-shifted hazy images have hindered accurate color perception of the images, and due to the dark haze color, they have worsened visibility compared to conventional hazy images. Therefore, various color correction-based dehazing algorithms have recently been implemented to restore colorcast images. However, existing color restoration studies are limited in that they struggle to distinguish between haze and objects, particularly when haze veils and images have a similar color or when objects with a high saturation value occupy a significant portion of the scene, resulting in overly grayish images and distorted colors. Therefore, we propose a saturation-based dehazing method that extracts only the hue of the cast airlight and preserves the information of the object. First, the proposed color correction method uses a dominant color extraction method for the clustering of CIELAB(LAB) color images and then assigns area scores to the classified clusters. Sorting of the airlight areas is performed using the area score, and gray world-based white balance is performed by extracting the hue of the area. Finally, the saturation of the restored image is used to separate and process the distant objects and airlight, and dehazing is performed by applying a weighting value to the depth map based on the average luminance. Our color restoration method prevents excessive gray tone and color distortion. In particular, the proposed dehazing method improves upon existing issues where near-field information is lost and noise is introduced in the far field as visibility improves.

Detection of Motorcycle Headlights Using YOLOv5 and HSV

"Electronic Traffic Law Enforcement" (ETLE) denotes a mechanism that employs electronic technologies to implement traffic regulations. This commonly entails utilizing a range of electronic apparatuses like cameras, sensors, and automated setups to oversee and uphold traffic protocols, administer fines, and enhance road security. ETLE systems are frequently utilized for identifying and sanctioning infractions like exceeding speed limits, disregarding red lights, and turning off the headlights. In Indonesia, there is currently no dedicated system designed to detect traffic violation, especially regarding vehicle headlights. Therefore, this research was conducted to detect vehicle headlights using digital images. With the results of this study, it will be possible to develop a system capable of classifying whether vehicle headlights are on or off. This research employed the deep learning method in the form of the YOLOv5 model, which achieved an accuracy of 94.12% in detecting vehicle images. Furthermore, the white color extraction method was performed by projecting the RGB space to HSV to detect the Region of Interest (ROI) of the vehicle headlights, achieving an accuracy of 73.76%. The results of this vehicle headlight detection are influenced by factors such as lighting, image capture angle, and vehicle type.

Phytoplankton Community Structure, Diversity, and Functional Groups in Urban River Under Different Black and Odorous Levels

Phytoplankton community characteristics, diversity, and functional group analysis can respond to the environmental quality status of water bodies. To investigate the influence of urban river black odors on the phytoplankton community, from November 2020 to March 2021, 16 urban rivers in the Sichuan Basin were surveyed for water quality with 38 sampling points, and the degree of urban river black odor was evaluated based on the improved fuzzy mathematical model method and the k-mean principal color extraction method. The rivers were classified into four types:non-black and non-odorous, black and non-odorous, black and odorous, and black and odorous. The results showed that, with black and odorous water, the phytoplankton abundance increased from 1.329×105 cells·L-1 to 6.627×105 cells·L-1, and the dominant phytoplankton phylum decreased from Cyanophyta-Chlorophyta-Bacillariophyta to Cyanophyta. The phytoplankton biomass increased from 64.056 μg·L-1 to 120.465 μg·L-1, and the dominant phytoplankton phylum changed from Chlorophyta-Bacillariophyta type, adapted to a nutrient environment, to Pyrrophyta-Bacillariophyta-Cryptophyta type, adapted to an organic matter environment. The Shannon-Weaver diversity index decreased from 2.45 to 1.98, and Simpson's index decreased from 0.84 to 0.73. Twenty nine functional groups of phytoplankton were observed in the study area, and the growth strategy was reduced from S, R, C, CR, and CS to R with black and odorous water. In summary, phytoplankton indicators can better reflect the state of river black odor, and phytoplankton monitoring is a promising tool for urban river black odor management.

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter

Computerization requires system testing and further system development, namely color feature extraction with KNN. Avocado is one that has a high protein content in it. This research uses the KNN algorithm method and feature extraction in order to get more effective results, the purpose of this research is to make it easier for people to choose the ripeness level of butter avocados because people still don't know about the maturity level of butter avocados. In this study, testing was carried out by bringing the avocado fruit closer to the cellphone camera connected to the researcher's internet, after which the application will automatically match the color of the avocado. to the system, the system will produce output based on that color with output in the form of the ripeness level of the avocado, whether it is ripe, ripe, half ripe, rotten and also generates information on how much longer the avocado will ripen. All stages of system development are carried out by analyzing data first, then taking sample data, training and testing datasets, then the results of the system will become benchmarks. The test data in this study used several types of avocado objects, namely: Raw, Half Ripe, Ripe, Ripe, Rotten. It consisted of 55 data samples consisting of 11 raw avocado samples, 11 half-ripe avocado samples, 11 ripe avocado samples, 11 ripe avocado samples and 11 rotten avocado samples. Obtained euclidean distance values ​​for each type of avocado butter. After that, the sum is done to get the overall level of accuracy by adding up the total euclidean distance with the total euclidean distance for each type of avocado. After getting the added value multiply it by 100%. Then the overall accuracy results obtained are 98.38%.

Design of Artistic Creation Style Extraction Model Based on Color Feature Data

In order to improve the style extraction and extraction ability of works of art, a color extraction method based on color features is proposed. The color feature extraction method is used to extract the style of visual works of art. The color feature region of works of art is segmented combined with a sparse scattered point reorganization method. Texture tracking and matching method is used for information fusion of works of art, combined with corner detection, three-dimensional edge contour feature detection method to realize texture filling and automatic rendering of art color extraction to improve art graphics’ color visual feature expression ability. The color feature data method is used for visual feature sampling and equalizing works of art. According to the equilibrium configuration results, the fuzzy clustering method is used to extract the color style of works of art to improve the style extraction and extraction identification ability of works of art. The simulation results show that this method has high accuracy in color extraction of works of art. It has a good effect on the extraction of art creation style and improves the ability of three-dimensional extraction and automatic extraction of art.

Color-deconvolution-based feature image extraction and application in water quality analysis.

We propose a feature color extraction method that improves the accuracy of water quality analysis using a digital image and eliminates the effect of interfering ions and chromogenic agents on the color after a color reaction. The proposed method is based on color deconvolution (CD) combined with machine learning for substance measurement in water. After an ordinary camera acquires the solution image after color reaction, the CD algorithm is applied to extract the feature image, calculate the first-order, second-order, and third-order color moments corresponding to RGB channels, and construct a gradient boosting regression tree prediction model based on color moment features to detect substances in water. In predicting ammonia, nitrite, and orthophosphate concentrations, the mean square error values were 0.01029, 0.00063, and 0.1361, and the mean absolute error values were 0.08103, 0.02231, and 0.32886, respectively. There was no significant difference in the results of the comparative spectrophotometric method on the actual water samples. The spiked recoveries of the samples ranged from 94% to 120%, confirming that the method can effectively measure the content of substances in water.

직접 볼륨 렌더링을 위한 CNN 기반 TF 색상 매핑

Direct Volume Rendering(DVR) renders by projecting data into a two-dimensional space without calculating the volume surfaces. In DVR, the transfer function(TF) assigns light properties such as color and transparency to the volume. However, it takes a long time for beginners to manipulate TF to understand volume data and assign colors. This paper proposes an approach to colorize the volume using sample images for intuitive volume rendering. We also discuss color extraction methods using K-means clustering.

Multi-angle automotive fuse box detection and assembly method based on machine vision

With the detection and assembly of automobile fuse box entering the automation stage, the traditional method has low detection accuracy, poor assembly efficiency and cannot automatically adjust the torque of the torque wrench according to the production needs. Therefore, it is necessary to propose a new method to improve the system’s shortcomings and improve the system’s application. This study proposes a multi-angle automotive fuse box detection and assembly method based on machine vision. Firstly, the HSV color extraction method and OCR character recognition method are used to obtain the fuse color and model on the fuse box, and the fuses of each model in the fuse box are quickly detected and recognized. Then, the upper surface image of the fuse box is captured by two cameras with different viewing angles. The coordinates and depth of field information generated by the difference in viewing angle are used to judge the position of the torque wrench, and the torque of the torque wrench is automatically adjusted. Finally, the prototype of the detection and assembly system is designed. The simulation results show that the detection accuracy, detection speed and reliability of the method reach a high level. The field test results of a certain type of fuse box on the prototype show that the recognition speed is about 0.085 s/piece, and the fuse detection accuracy for 99.9%, the position recognition accuracy of the torque wrench is 100%.

Processing images for red\u2013green dichromats compensation via naturalness and information-preservation considered recoloring

Color vision deficiency (CVD) is caused by anomalies in the cone cells of the human retina. It affects approximately 200 million individuals throughout the world. Although previous studies have proposed compensation methods, contrast and naturalness preservation have not been adequately and simultaneously addressed in the state-of-the-art studies. This paper focuses on red–green dichromats’ compensation and proposes a recoloring algorithm that combines contrast enhancement and naturalness preservation in a unified optimization model. In this implementation, representative color extraction and edit propagation methods are introduced to maintain global and local information in the recolored image. The quantitative evaluation results showed that the proposed method is competitive with state-of-the-art methods. A subjective experiment was also conducted and the evaluation results revealed that the proposed method obtained the best scores in preserving both naturalness and information for individuals with severe red–green CVD.

COLORS DISTRIBUTION IN POLYMER WASTES AND COLOR PREDICTION OF RECYCLED POLYMERS

In this paper, several image analysis techniques have been applied aiming to obtain useful information regarding the processing of municipal solid waste polymers. An automated color extraction method has been applied in order to quantize the color distribution in the polymer wastes aiming their resource and time-efficient separation according to their color. Also, by processing different density fractions from the polymer waste the color of the final material has been predicted, as well as the potential degradation of the material in the processing conditions used in the study. The image analysis techniques used in this paper could complement other polymer characterization techniques and could lead to an increase in the efficiency of waste recycling management by efficiently planning-ahead of the processing parameters and of the raw materials used (dyes, pigments).

Context-Aware Hypergraph Modeling for Re-identification and Summarization

Tracking and re-identification in wide-area camera networks is a challenging problem due to non-overlapping visual fields, varying imaging conditions, and appearance changes. We consider the problem of person re-identification and tracking, and propose a novel clothing context-aware color extraction method that is robust to such changes. Annotated samples are used to learn color drift patterns in a non-parametric manner using the random forest distance (RFD) function. The color drift patterns are automatically transferred to associate objects across different views using a unified graph matching framework . A hypergraph representation is used to link related objects for search and re-identification. A diverse hypergraph ranking technique is proposed for person-focused network summarization . The proposed algorithm is validated on a wide-area camera network consisting of ten cameras on bike paths. Also, the proposed algorithm is compared with the state of the art person re-identification algorithms on the VIPeR dataset .

Oxidation wear monitoring based on the color extraction of on-line wear debris

Oxidation associated wear usually involves high temperature and often accelerates lubrication degradation and failure processes. The color of oxide wear debris highly corresponds with the severities of oxidation wear. Therefore, on-line detection of oxide wear debris has the advantage of revealing the wear condition in a timely manner. This paper presents a color extraction method of wear debris for on-line oxidation monitoring. Images of moving wear particles in lubricant were captured via an on-line imaging system. Image preprocessing methods were adopted to separate wear particles from the background and to improve the image quality through a motion-blurred restoration process before the colors of the wear debris were extracted. By doing this, two typical types of oxide wear debris, red Fe2O3 and black Fe3O4, were identified. Furthermore, a statistical clustering model was established for automatic determination of the two typical types of oxide wear particles. Finally, the effectiveness of the proposed method was verified by performing real-time oxidation wear monitoring of experimental data. The proposed method provides a feasible approach to detect early oxidation wear and monitor its progress in a running machine.

つくばチャレンジ2013の課題を題材とした実環境におけるタスク遂行ロボットの開発

In Tsukuba Challenge 2013, every robot had to navigate on a given path and detect specific persons wearing showy color clothes. There are many ideal functions for achieving the mission. It is, however, difficult to implement all ideal functions since complex algorithms are required. This paper, therefore, considers simplified functions for achieving the mission even if complex algorithms are not implemented. Our robot can run long distance by using a unique localization method which uses both of magnetic and geometric landmarks. Moreover, it can detect the target based on a color extraction method. These abilities enable the robot to exactly work the simple functions. Through the experiments, it is shown that our robot can exactly achieve the given mission by using only the simplified functions.

One Color Extraction Method in Representation Techniques of Video Production

This study aims to get an effective marketing strategy in terms of business by introducing combined social video marketing and color marketing techniques. The method for research is in the range of research that implements highlighting techniques by extracting the core color and brand monochrome image, to analyze the specific process. The expected result is that the effects can contribute to the development and the activation of company or social video marketing of brand by using the new video production technique. Then, so as to propose a new video technique with the help of color marketing and the result of the analysis is utilized to know who use the effective activation and development of social video marketing in the future, and tries to present a technique for the new approach be. From the perspective of a company, the technique can effect in a positive way. They can provide consistent message to customers by the color effect which contains core message for brands. This technique is able to convey unique feeling and characteristic of the color, image and value of brand, concentrating peoples' sight on particular color when using black and white atmosphere. This can maximize its effect through emphasis through contrasting color, especially using color and non-color difference. At the same time this can utilize various way of expression through the effect gradually revealing color, without boring anybody. This paper expects this new way of expression and editing technique cause furthermore attempts on black and white in video making technique.

A Method for Identification of Harmful Video Images Using a 2-Dimensional Projection Map

This paper proposes a method for identification of harmful video images based on the degree of harmfulness in the video content. To extract harmful candidate frames from the video effectively, we used a video color extraction method applying a projection map. The procedure for identifying the harmful video has five steps, first, extract the I-frames from the video and map them onto projection map. Next, calculate the similarity and select the potentially harmful, then identify the harmful images by comparing the similarity measurement value. The method estimates similarity between the extracted frames and normative images using the critical value of the projection map. Based on our experimental test, we propose how the harmful candidate frames are extracted and compared with normative images. The various experimental data proved that the image identification method based on the 2-dimensional projection map is superior to using the color histogram technique in harmful image detection performance.

Color, texture and shape descriptor fusion with Bayesian network classifier for automatic image annotation

Due to the large amounts of multimedia data prevalent on the Web, Some images presents textural motifs while others may be recognized with colors or shapes of their content. The use of descriptors based on one’s features extraction method, such as color or texture or shape, for automatic image annotation are not efficient in some situations or in absence of the chosen type. The proposed approach is to use a fusion of some efficient color, texture and shape descriptors with Bayesian networks classifier to allow automatic annotation of different image types. This document provides an automatic image annotation that merges some descriptors in a parallel manner to have a vector that represents the various types of image characteristics. This allows increasing the rate and accuracy of the annotation system. The Texture, color histograms, and Legendre moments, are used and merged respectively together in parallel as color, texture and shape features extraction methods, with Bayesian network classifier, to annotate the image content with the appropriate keywords. The accuracy of the proposed approach is supported by the good experimental results obtained from ETH-80 databases.

Role of Fat Grafting in Primary Rhinoplasty

INTRODUCTION: Fat grafting is becoming an essential tool for revision rhinoplasty. This paper presents the utilization of fat for nasal volume restorations and contour adjustments, solely or in combination for primary rhinoplasty. METHODS: Autologous fat grafting was utilized in 59 primary rhinoplasty cases. In 12 cases “external nasal fat grafting” (without dissection) was the only component of the rhinoplasty, while in 47 cases it was part of the rhinoplasty in addition to cartilage grafting and other steps. The fat grafting was repeated in 1 – 3 times, differentially. Anatomic distribution of nasal fat grafting areas are listed as: Tip (n=10), columellar base (n=18), proximal nasal bridge (n=16), 1/3 middorsum (n=24), nasion (n=39), anterior columellar column (13) and glabellar/forehead projection (n=41) (Figure 1).Figure 1: Figure 1.PART A: Standardized preoperative and 8 weeks postinjection (last injection) lateral views of fat-only cases (n=12) were utilized (Figure 2). Color and surface extraction method (image pro 3) was used to quantify the projection differences.Figure 2: Figure 2.PART B: Complications were evaluated in 59 cases PART C: 10 independent subjects interpreted the preoperative and postoperative views of 59 cases in terms of improvement. Scaling for evaluation: Grade 1: negative progression Grade 2: no significant chang Grade 3: minimal improvement Grade 4: significant improvement RESULTS: PART A: Fraction of the overall lateral projection increase to the planned increase was 0.88 (statistically insignificant from 1 (hypothetic increase)). PART B: Complications Tip Excess: 3 (due to vertical shift) Supratip fullness: 4 (due to vertical shift) Inadequate height: 3 (required additional grafting) PART C: Grade 1: 1.2 of 59 cases Grade 2: 3.9 of 59 cases Grade 3: 18.4 of 59 cases Grade 4: 35.5 of 59 cases CONCLUSION: Nasal augmentation can be achieved in primary rhinoplasty cases. Volume shift due to the gravity may occur in some cases, use of PRP and extended external stabilizing pressure can be utilized to prevent this. Fat grafting can play a significant role in primary rhinoplasty cases, and delivers the advantage of minimized dissection, and improved skin quality due to the preadipocyte contribution. It can be utilized solely in limited number of cases. Differential dissection limitations and sequence of the maneuvers will be discussed.

Color system for skin color extraction

This paper describes a skin color extraction method which has high speed and high accuracy. There were many systems which could achieve the requirements, but they were useless due to tough limitations such as using two cameras to recognize correct coordinates, running only a specific background, wearing several gears, learning the users detailed information in advance, making a personalized database, and so on. In contrast, our approach, which uses the YIQ color system with a background estimation method for skin color extraction, achieved a good performance more than 30 fps, with strong robustness in complicated and poorly illuminated environments.