• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.179-182
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• 2009

서로 다른 색상의 조명환경에서 촬영된 영상으로부터 동일 객체를 자동으로 검출하기 위하여 객체의 색상 비교가 요구된다. 본 논문에서는 서로 다른 조명 영상들에서 비교 대상 객체들의 색상을 비교 분석하기 위하여, 조명 차이 요소를 제거하고, 입력영상을 목표 조명영상으로 변환하기 위한 색 보정 기법을 제안한다. 제안 색상 보정 기법은 촬영전에 색상 팔렛트를 이용하여 조명색상 정보를 분석하여 각 조명간 RGB 색상 요소별 차이를 전처리 단계에서 계산한다. 각 조명환경에서 촬영한 영상에 대해, 미리 계산된 조명간 차이값을 입력되는 각 영상화소값에 반영함으로써 영상의 색상을 보정한다. 실험에서, 서로 다른 색상의 조명 조건에서 촬영된 두 영상에 대하여 하나의 영상을 기준 영상으로 선정하고, 다른 하나의 영상에 제안 보정처리를 수행한다. 보정 전후 영상과 기준 영상과의 가시적인 비교 방법과 히스토그램 비교에 의하여 제안 보정 기법의 성능을 평가한다.

• The Journal of Korea Robotics Society
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• v.6 no.2
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• pp.165-172
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• 2011

This paper presents a robust lane detection algorithm based on RGB color and shape information during autonomous car control in realtime. For realtime control, our algorithm increases its processing speed by employing minimal elements. Our algorithm extracts yellow and white pixels by computing the average and standard deviation values calculated from specific regions, and constructs elements based on the extracted pixels. By clustering elements, our algorithm finds the yellow center and white stop lanes on the road. Our algorithm is insensitive to the environment change and its processing speed is realtime-executable. Experimental results demonstrate the feasibility of our algorithm.

• The Research Journal of the Costume Culture
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• v.17 no.2
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• pp.309-319
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• 2009

This study's goal is to analyze the color characteristics of Blue used in men's fashion for design developing process. First, we researched the previous studies and examined documents about color characteristics of Blue in general as well as coloration in fashion design and men's fashion. We composed color samples by collecting two-color coloration used in men's fashion collection for 5 years from 2004 S/S to 2008 F/W through a specialized fashion information web-sites. We limited the colors from Blue Green(BG) to Purple Blue(PB). Second, we analyzed the characteristics of hue combination and tone combination. A total of 351 pictures were collected and RGB and HV/C value were converted with Munsell Conversion program(ver.8.0.1). Color data has been sorted to 10 hues and 12 PCCS tones. From this, we were able to figure out that similar/same hue coloration was used more than contrary hue coloration and similar/same tone coloration was used more than contrary tone coloration for Blue. We've limited Blue coloration characteristics of men's fashion to two-color coloration for an analysis; the succeeding study will need to examine on the characteristics of multi-coloration and detailed Blue coloration image by various garments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.107-115
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• 2009

Skin color detection is used to classify input pixels into skin and non skin area, and it requires the classifier to have a high classification rate. In previous work, most classifiers used single color model for skin color detection. However the classification rate can be increased by using more than one color model due to the various characteristics of skin color distribution in different color models, and the MLP is also invested as a more efficient classifier with less parameters than other classifiers. But the input dimension and required parameters of MLP will be increased when using two color models in skin color detection, as a result, the increased parameters will cause the huge teaming time in MLP. In this paper, we propose a MLP based classifier with less parameters in two color models. The proposed partially connected MLP based on two color models can reduce the number of weights and improve the classification rate. Because the characteristic of different color model can be learned in different partial networks. As the experimental results, we obtained 91.8% classification rate when testing various images in RGB and CbCr models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.697-704
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• 2018

Since the face in image content corresponds to individual information that can distinguish a specific person from other people, it is important to accurately detect faces not hidden in an image. In this paper, we propose a method to accurately detect a face from input images using a deep learning algorithm, which is one of the machine learning methods. In the proposed method, image input via the red-green-blue (RGB) color model is first changed to the luminance-chroma: blue-chroma: red-chroma ($YC_bC_r$) color model; then, other regions are removed using the learned skin color model, and only the skin regions are segmented. A CNN model-based deep learning algorithm is then applied to robustly detect only the face region from the input image. Experimental results show that the proposed method more efficiently segments facial regions from input images. The proposed face area-detection method is expected to be useful in practical applications related to multimedia and shape recognition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.35-46
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• 2007

This paper proposes a six-color separation method of reducing unnecessary usage of colorants based on the limitation of total colorant amount and dot visibility ordering. First, the CIELAB values of input RGB image are estimated through the color-mixing model and compared with pre-calculated CIELAB values corresponding to all combination of CMYKlclm colorants with a constraint of color difference, thereby selecting initial CMYKlclm candidates. Next, the limitation on total colorant amount Is imposed on initial CMYKlclm candidates to remove the excessive amounts of colorants, and then final CMYKlclm candidates are determined by minimizing the usage of light cyan and light magenta in the dark region based on the dot visibility ordering of C, M, Y, K, lc, and lm. Through the experiment, the proposed method is shown to reduce the excessive amount of colorants with preserving good image quality.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.465-472
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• 2003

For an effective pre-treatment process of a face input image, it is necessary to detect each of face components, calculate the face area, and estimate the rotary angle of the face. A proposed method of this study can estimate an robust result under such renditions as some different levels of illumination, variable fate sizes, fate rotation angels, and background color similar to skin color of the face. The first step of the proposed method detects the estimated face area that can be calculated by both adapted skin color Information of the band-wide HSV color coordinate converted from RGB coordinate, and skin color Information using histogram. Using the results of the former processes, we can detect a lip area within an estimated face area. After estimating a rotary angle slope of the lip area along the X axis, the method determines the face shape based on face information. After detecting eyes in face area by matching a partial template which is made with both eyes, we can estimate Y axis rotary angle by calculating the eye´s locations in three dimensional space in the reference of the face area. As a result of the experiment on various face images, the effectuality of proposed algorithm was verified.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.159-164
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• 2015

Color is distinguished due to the light in which natural light is reflected by object and made with combination of RGB(red, green, blue; three colors). This study proposes color analysis system with optical method to be used conveniently. Color information of sample is determined with the optical sensor. By using the CIE diagram in particular, it detects purity value and wavelength. The method to distinguish color is very economical, simple, and convenient. The result can be used to confirm accurate information of color for various applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.428-434
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• 2015

A new tongue examination model by using color analysis according to the taste division of tongue. The proposed system consists of a tongue image acquisition in a predefined template, taste region segmentation, color distribution analysis and abnormality decision of tongue by color analysis using Hue-Saturation histograms and the part of a mobile application service. We divided 4 basic taste(bitter, sweet, salty and sour) regions and performed color distribution analysis targeting each region under HSI(Hue Saturation Intensity) color model. To minimize the influence of illumination, the histograms of H and S components only except U are utilized. Using the analyzed results, the abnormality is discriminated by the criteria of the histogram range of normal tongues. Finally, a self tongue diagnosis system which can be used anytime and anywhere on mobile environment.

• Journal of Multimedia Information System
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• v.7 no.2
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• pp.97-106
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• 2020

The subjective experience would degrade the current artificial artistic aesthetic analysis. Since Qiong kiln ceramics have a long history and occupy a very important position in ceramic arts, we employed computer-aided technologies to quickly automatically accurately and quantitatively process a large number of Qiong kiln ceramic images and generate the detailed statistical data. Because the color features are simple and significant visual characteristics, the color features of Qiong kiln ceramics are analyzed for the quantitative aesthetics. The Qiong kiln ceramic images are segmented with GrabCut algorithm. Three moments (1st-order, 2nd-order, and 3rd-order) are calculated in two typical color spaces, namely RGB and HSV. The discrimination powers of the color features are analyzed according to various dynasties (Tang Dynasty, Five Dynasties, Song Dynasty) and various utensils (Pot, kettle, bowl), which are helpful to the selection of the discriminant color features among various dynasties and utensils. This paper is helpful to promoting the quantitative aesthetic research of Qiong kiln ceramics and is also conducive to the research on the aesthetics of other ceramics.