• Journal of the Korea Society of Computer and Information
• /
• v.20 no.12
• /
• pp.9-14
• /
• 2015

In this paper, we present an color quantization method by complementing the disadvantage of K-means color quantization that is one of the well-known color quantization. We named the proposed method "octree-means" color quantization. K-means color quantization does not use all of the clusters because it initializes the centroid of clusters with random value. The proposed method complements this disadvantage by using the octree color quantization which is fast and uses the distribution of colors in image. We compare the proposed method to six well-known color quantization methods on ten test images to evaluate the performance. The experimental results show 68.29 percent of mean square error(MSE) and processing time increased by 14.34 percent compared with K-means color quantization. Therefore, the proposed method improved the K-means color quantization and perform an effective color quantization.

• The KIPS Transactions:PartB
• /
• v.13B no.3 s.106
• /
• pp.205-214
• /
• 2006

Texts in images have significant and detailed information about the scenes, and if we can automatically detect and recognize those texts in real-time, it can be used in various applications. In this paper, we propose a new text detection method that can find texts from the various camera-captured images and propose a text segmentation method from the detected text regions. The detection method proposes color variance as a detection feature in RGB color space, and the segmentation method suggests an improved K-means color clustering in RGB color space. We have tested the proposed methods using various kinds of document style and natural scene images captured by digital cameras and mobile-phone camera, and we also tested the method with a portion of ICDAR[1] contest images.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.3
• /
• pp.37-43
• /
• 2014

In this paper, we propose a robust lip detection algorithm using color clustering. At first, we adopt AdaBoost algorithm to extract facial region and convert facial region into Lab color space. Because a and b components in Lab color space are known as that they could well express lip color and its complementary color, we use a and b component as the features for color clustering. The nearest neighbour clustering algorithm is applied to separate the skin region from the facial region and K-Means color clustering is applied to extract lip-candidate region. Then geometric characteristics are used to extract final lip region. The proposed algorithm can detect lip region robustly which has been shown by experimental results.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.9
• /
• pp.76-90
• /
• 1999

In this paper, a color image retrieval algorithm is proposed based on color histogram and color texture. The representative color vectors of a color image are made from k-means clustering of its color histogram, and color texture is generated by centering around the color of pixels with its color vector. Thus the color texture means texture properties emphasized by its color histogram, and it is analyzed by Gaussian Markov Random Field (GMRF) model. The proposed algorithm can work efficiently because it does not require any low level image processing such as segmentation or edge detection, so it outperforms the traditional algorithms which use color histogram only or texture properties come from image intensity.

• Journal of Power System Engineering
• /
• v.20 no.4
• /
• pp.32-37
• /
• 2016

This paper proposes two measurement methods for injured rate of fish surface using color image segmentation method based on K-means clustering algorithm and Otsu's threshold algorithm. To do this task, the following steps are done. Firstly, an RGB color image of the fish is obtained by the CCD color camera and then converted from RGB to HSI. Secondly, the S channel is extracted from HSI color space. Thirdly, by applying the K-means clustering algorithm to the HSI color space and applying the Otsu's threshold algorithm to the S channel of HSI color space, the binary images are obtained. Fourthly, morphological processes such as dilation and erosion, etc. are applied to the binary image. Fifthly, to count the number of pixels, the connected-component labeling is adopted and the defined injured rate is gotten by calculating the pixels on the labeled images. Finally, to compare the performances of the proposed two measurement methods based on the K-means clustering algorithm and the Otsu's threshold algorithm, the edge detection of the final binary image after morphological processing is done and matched with the gray image of the original RGB image obtained by CCD camera. The results show that the detected edge of injured part by the K-means clustering algorithm is more close to real injured edge than that by the Otsu' threshold algorithm.

• Journal of information and communication convergence engineering
• /
• v.16 no.4
• /
• pp.258-263
• /
• 2018

Color Doppler sonography is a useful tool for examining blood flow and related indices. However, it should be done by well-trained operator, that is, operator subjectivity exists. In this paper, we propose an automatic blood flow area extraction method from brachial artery that would be an essential building block of computer aided color Doppler analyzer. Specifically, our concern is to examine hypertension suspicious (prehypertension) patients who might develop their symptoms to established hypertension in the future. The proposed method uses fuzzy C-means clustering as quantization engine with careful seeding of the number of clusters from histogram analysis. The experiment verifies that the proposed method is feasible in that the successful extraction rates are 96% (successful in 48 out of 50 test cases) and demonstrated better performance than K-means based method in specificity and sensitivity analysis but the proposed method should be further refined as the retrospective analysis pointed out.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.11
• /
• pp.1411-1416
• /
• 2020

In this paper, we propose a method of extraction analysis of blood flow area on color doppler ultrasonography by using 4-directional contour tracking and K-Means algorithm. In the proposed method, ROI is extracted and a binarization method with maximum contrast as a threshold is applied to the extracted ROI. 4-directional contour algorithm is applied to extract the trapezoid shaped region which has blood flow area of brachial artery from the binarized ROI. K-Means based quantization is then applied to accurately extract the blood flow area of brachial artery from the trapezoid shaped region. In experiment, the proposed method successfully extracts the target area in 28 out of 30 cases (93.3%) with field expert's verification. And comparison analysis of proposed K-Means based blood flow area extraction on 30 color doppler ultrasonography and brachial artery blood flow ultrasonography provided by a specialist yielded a result of 94.27% accuracy on average.

• Journal of the Korean Wood Science and Technology
• /
• v.14 no.1
• /
• pp.55-60
• /
• 1986

Discoloration sensitivities of woods grown in this country haven't reported yet. Therefore we examined discoloration sensitivities of domestic wood specimens to iron (0.1 %, $FeCl_3.6H_2O$), alkali (pH 12.0, NaOH). acid (pH 1.0, $C_2H_2O_4$) and exposing to sunlight (40 hrs), Thirty-six hardwood species were collected and examined. All specimens were prepared from heartwoods of the collected species. But the specimens of 4 Betula species were divided into sapwoods and heartwoods. By iron stain, the color differences (${\Delta}E$) of 21 wood specimens including one Betula sapwood showed above 12.0, which means strong discoloration sensitivities, and of 3 specimens including one Betula sapwood showed below 2.5, which means weak discolorations. The most strong iron discoloration species was Jungkukgulpi-namu (Pterocarya stenoptera). By alkali stain, the color differences (${\Delta}E$) of 3 wood specimens showed above 9.0, which means strong discoloration sensitivities, and of 18 wood specimens including 4 Berula sapwoods showed below 2.5, which means weak discolorations. By acid stain, the color differences (${\Delta}E$) of 6 wood specimens showed above 10.0 which means strong discoloration sensitivities, and of 12 wood specimens including one Betula sapwoods showed below 2.5, which means weak discolorations. By exposing to sunlight, the color differences (${\Delta}E$) of 31 wood specimens including one Betula sapwoods showed below 6.5, which means, strong discoloration sensitivities, and of only one specimens showed below 2.5, which means weak discoloration. The most strong discoloration species by exposing to sunlight was Guirung-namu (Prunus padus). In general, it was shown that hardwoods grown in Korea were most subject to change of color by exposing to sunlight and next were by iron stain. Domestic hardwoods showed some differences in discoloration sensitivities from domestic softwoods previously reported.

• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06a
• /
• pp.491-493
• /
• 2011

Traffic sign detection is the domain of automatic driver assistant systems. There are literatures for traffic sign detection using color information, however, color-based method contains ill-posed condition and to extract the region of interest is difficult. In our work, we propose a method for traffic sign detection using k-means clustering method, back-propagation neural network, and projection histogram features that yields the robustness for ill-posed condition. Using the color information of traffic signs enables k-means algorithm to cluster the region of interest for the detection efficiently. In each step of clustering, a cluster is verified by the neural network so that the cluster exactly represents the location of a traffic sign. Proposed method is practical, and yields robustness for the unexpected region of interest or for multiple detections.

• Journal of IKEEE
• /
• v.1 no.1 s.1
• /
• pp.93-100
• /
• 1997

In general, fuzzy C-means clustering method was used on the segmentation of true color image. However, this method requires number of clusters as an input. In this study, we suggest new method that uses subtractive and gravity fuzzy C-means clustering. We get number of clusters and initial cluster centers by applying subtractive clustering on color image. After coarse segmentation of the image, we apply gravity fuzzy C-means for optimizing segmentation of the image. We show efficiency of the proposed algorithm by qualitative evaluation.