• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.403-410
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• 2004

Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.

• Smart Media Journal
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• v.1 no.4
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• pp.27-34
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• 2012

In this paper, a novel text binarization is presented that can deal with some complex conditions, such as shadows, non-uniform illumination due to highlight or object projection, and messy backgrounds. To locate the target text region, a focus line is assumed to pass through a text region. Next, connected component analysis and stroke width estimation based on location information of the focus line is used to locate the bounding box of the text region, and each box of connected components. A series of classifications are applied to identify whether each CC(Connected component) is text or non-text. Also, a modified K-means clustering method based on an HCL color space is applied to reduce the color dimension. A text binarization procedure based on location of text component and seed color pixel is then used to generate the final result.

• Journal of Periodontal and Implant Science
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• v.31 no.3
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• pp.501-511
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• 2001

Several indices have been developed that use bleeding and color changes as indicators of early gingival pathology. In the presence of gingivitis, vascular proliferation and reduction of keratinization owing to increase redness in gingiva. Descriptions of healthy gingiva are numerous, ranging from pale pink and coral pink to deep red and violet. This terms are not objective. Because of perception of color depends on a lot of factors such as light source, object, observer and so on. It is difficult to make an objective expression. Therefore the using of mechanical equipment is recommended to exclude these variables and observer's vias. The purpose of this study was to evaluate gingival color change after scaling & subgingival root planing. The other purpose of this study was to research the correlation of pocket depth, P.B.I. score and gingival color change. After photo-taking and storaging the image of gingival color into a computer, color change was examine with an image analysis program. Results were as follow; 1. Color of healed gingiva after scaling & subgingival root planing was significantly differ from color of inflamed gingiva(p<0.01). 2. Color of healed gingiva after scaling was similar to color of healed gingiva after subgingival root planing(p<0.05). 3. There was statistically significant correlation between color change of red component and pocket depth after scaling & subgingival root planing(p<0.01). 4. There was no correlation between color change of green, blue component and pocket depth after scaling & subgingival root planing(p<0.01). 5. There was statistically significant correlation between between color change of red component and P.B.I. score after scaling & subgingival root planing(p<0.01). 6. There was no correlation between color changes of green, blue component and P.B.I. score after scaling & subgingival root planing(p<0.01). 7. Increase of pocket depth and P.B.I. score were significantly correlated to the amount of color change(p<0.01). 8. P.B.I. score had a higher correlation with color change than pocket depth(p<0.01).

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.131-136
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• 2007

This paper proposes a new approach to eliminate the reflectance component for the detection of text in color images. Color images, printed by color printing technology, normally have an illumination component as well as a reflectance component. It is well known that a reflectance component usually obstructs the task of detecting and recognizing objects like texts in the scene, since it blurs out an overall image. We have developed an approach that efficiently removes reflectance components while preserving illumination components. We decided whether an input image hits Normal or Polarized for determining the light environment, using the histogram which consisted of a red component. We were able to go ahead through the ability to extract by reducing the blur phenomenon of text by light because reflection component by an illumination change and removed it and extracted text. The experimental results have shown a superior performance even when an image has a complex background. Text detection and recognition performance is influenced by changing the illumination condition. Our method is robust to the images with different illumination conditions.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.229-236
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• 2008

This paper examines effectiveness of using color images instead of grayscale ones for automatic lipreading. First, we show the effect of color information for performance of humans' lipreading. Then, we compare the performance of automatic lipreading using features obtained by applying principal component analysis to grayscale and color images. From the experiments for various color representations, it is shown that color information is useful for improving performance of automatic lipreading; the best performance is obtained by using the RGB color components, where the average relative error reductions for clean and noisy conditions are 4.7% and 13.0%, respectively.

• Journal of Sensor Science and Technology
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• v.29 no.2
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• pp.133-140
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• 2020

This study proposes a method of blending visible and near infrared images to enhance edge details and local contrast. The proposed method consists of radiance map generation and color compensation. The radiance map is produced by a Laplacian pyramid and a soft mixing method based on principal component analysis. The color compensation method uses the ratio between the composed radiance map and the luminance channel of a visible image to preserve the visible image chrominance. The proposed method has better edge details compared to a conventional visible and NIR image blending method.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.54-61
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• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Journal of the Korean Applied Science and Technology
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• v.28 no.4
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• pp.393-401
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• 2011

Natural Curcumin belongs to Zingiber Officinale Roscoe was known to possess natural odor, natural taste, natural color, and other pharmaceutical & chemical characteristics. Natural Curcumin extract was made to use ethanol as a solvent was to show a yellow color having state of solid powder and an active component. Natural Curcumin extract tested pharmaceutical & chemical experiment to dilute in curcumin 1%-water solution. Curcumin extract tested antimicrobial experiment using microbe, and tested dye experiment using fiber. Some conclusions in the result of characteristics experiment was obtained as follow. The result of antimicrobial experiment showed that the growth of staphylococcus aureus (ATCC-001) and aspergillus niger (ATCC-002) as microbes decreased according to passage of time. This phenomenon could know that Curcumin compoment showed influence to antimicrobial effect. Also, the result of dye experiment showed that cotton and sick with fiber dyeing dyed in direction of dark yellow color. This phenomenon could know that Curcumin extract showed influence to dyeing effect in observation of optical electron microscope(OEM.) The result of instrument analysis ascertained inorganic components of K(53.300ppm), Na(1.150ppm), Ca(0.711ppm), Ti(0.351ppm), Li(0.256ppm), Cu(0.233ppm) etcs from Curcumin component with ICP/OES, and ascertained organic components of propanoic acid(1.859), benzene(10.814), phenol(14.194) etcs from Curcumin component with GC/MSD.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.817-823
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• 2004

This paper describes a three dimensional motion recognition algorithm and a system which adopts the algorithm for non-contact human-computer interaction. From sequence of stereos images, five feature regions are extracted with simple color segmentation algorithm and then those are used for three dimensional locus calculation precess. However, the result is not so stable, noisy, that we introduce principal component analysis method to get more robust motion recognition results. This method can overcome the weakness of conventional algorithms since it directly uses three dimensional information motion recognition.

• Current Optics and Photonics
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• v.6 no.6
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• pp.608-618
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• 2022

By analyzing the signal-to-noise ratio (SNR) theory of the conventional true color night vision system, we found that the output image SNR is limited by the wavelength range of the system response λ₁ and λ₂. Therefore, we built a double-channel four-band true color night vision system to expand the system response to improve the output image SNR. In the meantime, we proposed an image fusion method based on principal component analysis (PCA) and nonsubsampled shearlet transform (NSST) to obtain the true color night vision images. Through experiments, a method based on edge extraction of the targets and spatial dimension decorrelation was proposed to calculate the SNR of the obtained images and we calculated the correlation coefficient (CC) between the edge graphs of obtained and reference images. The results showed that the SNR of the images of four scenes obtained by our system were 125.0%, 145.8%, 86.0% and 51.8% higher, respectively, than that of the conventional tri-band system and CC was also higher, which demonstrated that our system can get true color images with better quality.