• Title/Summary/Keyword: Color K-Means

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Classifying Color Codes Via k-Mean Clustering and L*a*b* Color Model (k-평균 클러스터링과 L*a*b* 칼라 모델에 의한 칼라코드 분류)

  • Yoo, Hyeon-Joong
    • The Journal of the Korea Contents Association
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    • v.7 no.2
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    • pp.109-116
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    • 2007
  • To reduce the effect of color distortions on reading colors, it is more desirable to statistically process as many pixels in the individual color region as possible. This process may require segmentation, which usually requires edge detection. However, edges in color codes can be disconnected due to various distortions such as dark current, color cross, zipper effect, shade and reflection, to name a few. Edge linking is also a difficult process. In this paper, k-means clustering was performed on the images where edge detectors failed segmentation. Experiments were conducted on 311 images taken in different environments with different cameras. The primary and secondary colors were randomly selected for each color code region. While segmentation rate by edge detectors was 89.4%, the proposed method increased it to 99.4%. Color recognition was performed based on hue, a*, and b* components, with the accuracy of 100% for the successfully segmented cases.

Analysis of Chicken Feather Color Phenotypes Classified by K-Means Clustering using Reciprocal F2 Chicken Populations (K-Means Clustering으로 분류한 닭 깃털색 표현형의 분석)

  • Park, Jongho;Heo, Seonyeong;Kim, Minjun;Cho, Eunjin;Cha, Jihye;Jin, Daehyeok;Koh, Yeong Jun;Lee, Seung-Hwan;Lee, Jun Heon
    • Korean Journal of Poultry Science
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    • v.49 no.3
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    • pp.157-165
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    • 2022
  • Chickens are a species of vertebrate with varying colors. Various colors of chickens must be classified to find color-related genes. In the past, color scoring was performed based on human visual observation. Therefore, chicken colors have not been measured with precise standards. In order to solve this problem, a computer vision approach was used in this study. Image quantization based on k-means clustering for all pixels of RGB values can objectively distinguish inherited colors that are expressed in various ways. This study was also conducted to determine whether plumage color differences exist in the reciprocal cross lines between two breeds: black Yeonsan Ogye (YO) and White Leghorn (WL). Line B is a crossbred line between YO males and WL females while Line L is a reciprocal crossbred line between WL males and YO females. One male and ten females were selected for each F1 line, and full-sib mating was conducted to generate 883 F2 birds. The results indicate that the distribution of light and dark colors of k-means clustering converged to 7:3. Additionally, the color of Line B was lighter than that of Line L (P<0.01). This study suggests that the genes underlying plumage colors can be identified using quantification values from the computer vision approach described in this study.

Color Code Detection and Recognition Using Image Segmentation Based on k-Means Clustering Algorithm (k-평균 클러스터링 알고리즘 기반의 영상 분할을 이용한 칼라코드 검출 및 인식)

  • Kim, Tae-Woo;Yoo, Hyeon-Joong
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.7 no.6
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    • pp.1100-1105
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    • 2006
  • Severe distortions of colors in the obtained images have made it difficult for color codes to expand their applications. To reduce the effect of color distortions on reading colors, it will be more desirable to statistically process as many pixels in the individual color region as possible, than relying on some regularly sampled pixels. This process may require segmentation, which usually requires edge detection. However, edges in color codes can be disconnected due tovarious distortions such as zipper effect and reflection, to name a few, making segmentation incomplete. Edge linking is also a difficult process. In this paper, a more efficient approach to reducing the effect of color distortions on reading colors, one that excludes precise edge detection for segmentation, was obtained by employing the k-means clustering algorithm. And, in detecting color codes, the properties of both six safe colors and grays were utilized. Experiments were conducted on 144, 4M-pixel, outdoor images. The proposed method resulted in a color-code detection rate of 100% fur the test images, and an average color-reading accuracy of over 99% for the detected codes, while the highest accuracy that could be achieved with an approach employing Canny edge detection was 91.28%.

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Automatic Intelligent Asymmetry Detection Using Digital Infrared Imaging with K-Means Clustering

  • Kim, Kwang Baek;Song, Doo Hoen
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.15 no.3
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    • pp.180-185
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    • 2015
  • Digital infrared thermal imaging is a non-invasive adjunctive diagnostic technique that allows an examiner to visualize and quantify changes in skin surface temperature. The asymmetry of temperature differences between the diseased and the contralateral healthy body parts can be automatically analyzed and has been studied in many areas of medical science. In this paper, we propose a method for intelligent automatic asymmetry detection based on a K-means analysis and a YCbCr color model. The implemented software successfully visualizes an asymmetric distribution of colors with respect to the patients’ health status.

Prediction and visualization of CYP2D6 genotype-based phenotype using clustering algorithms

  • Kim, Eun-Young;Shin, Sang-Goo;Shin, Jae-Gook
    • Translational and Clinical Pharmacology
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    • v.25 no.3
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    • pp.147-152
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    • 2017
  • This study focused on the role of cytochrome P450 2D6 (CYP2D6) genotypes to predict phenotypes in the metabolism of dextromethorphan. CYP2D6 genotypes and metabolic ratios (MRs) of dextromethorphan were determined in 201 Koreans. Unsupervised clustering algorithms, hierarchical and k-means clustering analysis, and color visualizations of CYP2D6 activity were performed on a subset of 130 subjects. A total of 23 different genotypes were identified, five of which were observed in one subject. Phenotype classifications were based on the means, medians, and standard deviations of the log MR values for each genotype. Color visualization was used to display the mean and median of each genotype as different color intensities. Cutoff values were determined using receiver operating characteristic curves from the k-means analysis, and the data were validated in the remaining subset of 71 subjects. Using the two highest silhouette values, the selected numbers of clusters were three (the best) and four. The findings from the two clustering algorithms were similar to those of other studies, classifying $^*5/^*5$ as a lowest activity group and genotypes containing duplicated alleles (i.e., $CYP2D6^*1/^*2N$) as a highest activity group. The validation of the k-means clustering results with data from the 71 subjects revealed relatively high concordance rates: 92.8% and 73.9% in three and four clusters, respectively. Additionally, color visualization allowed for rapid interpretation of results. Although the clustering approach to predict CYP2D6 phenotype from CYP2D6 genotype is not fully complete, it provides general information about the genotype to phenotype relationship, including rare genotypes with only one subject.

Color Sensible Psychology of Child in Image (영상에서의 아동의 색채 감성 심리)

  • Shin, Seong-Yoon;Baek, Jeong-Uk;Rhee, Yang-Won
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2010.10a
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    • pp.649-650
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    • 2010
  • This paper construct the sensibility database by extracting sensibility of 28 colors based on 12 color wheel. And, after the large color values are grouped by clustering of input image using k-mean algorithm, sensibility was extracted by matching with color and database. Also, we see the color sensible psychology of child using color distribution of children in painting.

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A Study on The Color Reproduction Properties of Domestic Newspaper Prints (국내 신문 인쇄의 색재현 특성에 대한 평가)

  • Park, Han-Areum;Kang, Sang-Hoon
    • Proceedings of the Korean Printing Society Conference
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    • 2007.11a
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    • pp.29-43
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    • 2007
  • Web offset newspaper prints is the most widely used printing process in domestic printing industry, but there are no industry-wide specifications for press control to assure consistent quality across newspaper printing plants. As newspaper printing becomes more of a commodity and less of an art, it is necessary to develop the print quality specification standard suitable for Korea web offset newspaper printing field. There are a few commonly employed printing standards such as, SNAP(Specification for Non-Heatset Publication) of the US, IFRA(INCA-FIEJ Research Association), J-color (Japan printing machinery association). And there exists KS for domestic standards, however, since it has been directly imported from ISO standards, there are discrepancies in printing materials and media. This paper aims to examine the color reproduction quality of domestic web offset newspaper prints by comparing with foreign standards. Printing experiments on this study have been performed at one of the domestic newspaper printing companies. The color reproduction properties of the web offset newspaper prints are measured by means of densitometry and colorimetry. These measures are evaluated comapratively with the international standards by means of analyzing, the major factors that determine the color reproduction quality of web offset newspapar prints.

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A Study on The Color Reproduction Properties of Domestic Newspaper Prints (국내 신문 인쇄의 색재현 특성에 관한 연구)

  • Park, Han-A-Reum;Kang, Sang-Hoon
    • Journal of the Korean Graphic Arts Communication Society
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    • v.25 no.1
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    • pp.27-41
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    • 2007
  • Web offset newspaper prints is the most widely used printing process in domestic printing industry, but there are no industry-wide specifications for press control to assure consistent quality across newspaper printing plants. As newspaper printing becomes more of a commodity and less of an art, it is necessary to develop the print quality specification standard suitable for Korea web offset newspaper printing field. There are a few commonly employed printing standards such as, SNAP (Specification for Non-Heatset Publication) of the US, IFRA(INCA-FIEJ Research Association), J-color (Japan printing machinery association). And there exists KS for domestic standards, however, since it has been directly imported from ISO standards, there are discrepancies in printing materials and media. This paper aims to examine the color reproduction quality of domestic web offset newspaper prints by comparing with foreign standards. Printing experiments on this study have been performed at one of the domestic newspaper printing companies. The color reproduction properties of the web offset newspaper prints are measured by means of densitometry and colorimetry. These measures are evaluated comapratively with the international standards by means of analyzing, the major factors that determine the color reproduction quality of web offset newspaper prints.

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Image Clustering using Color, Texture and Shape Features

  • Sleit, Azzam;Abu Dalhoum, Abdel Llatif;Qatawneh, Mohammad;Al-Sharief, Maryam;Al-Jabaly, Rawa'a;Karajeh, Ola
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.5 no.1
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    • pp.211-227
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    • 2011
  • Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.

TF color mapping for direct volume rendering with CNN (직접 볼륨 렌더링을 위한 CNN 기반 TF 색상 매핑)

  • Kim, Seokyeon;Jang, Yun
    • Journal of the Korea Computer Graphics Society
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    • v.27 no.5
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    • pp.25-32
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    • 2021
  • 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.