• 한국정보전자통신기술학회논문지
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• 제6권4호
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• pp.249-254
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• 2013

In the area of computer vision, face recognition is being intensively researched. It is generally known that before a face is recognized it must be localized. Skin-color information is an important feature to segment skin-color regions. To extract skin-color regions the skin-color model based on multi-layer perceptron has been proposed. Extracted regions are analyzed to emphasize ellipsoidal regions. The results from this study show good accuracy for our vehicle driver face detection system.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2002년도 International Meeting on Information Display
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• pp.1059-1063
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• 2002

The evolution of color CRT electron gun design over the past 40 years is addressed. Many milestones of CRT E-gun design are cited. For the future survival of color CRT the multi-beam group color E-gun and the recently announced multi-beam type index gun are suggested as the answer to the challenge of the next generation's requirements of low power and high performance color CRT

• 정보처리학회논문지B
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• 제14B권5호
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• pp.337-342
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• 2007

다중-레벨은 영상의 모든 레벨에서 서로 다른 영상으로 표현할 수 있는 장점을 가지고 있다. 본 논문은 다양한 컬러 영상의 표현에서 워터마크의 강인성와 무감지성을 보장하기 위해서 컬러 영상을 YCbCr 컬러 공간으로 변환하고, 다중-레벨의 Y-요소에 대해 저해상도로부터 전체 해상도로 대역 확산하는 다중-레벨 데이터 은닉을 위한 디지털 워터마킹 내장 기법을 제안한다. 컬러 신호에서 Y-신호와 저해상도의 워터마크 내장은 시각적으로 드러날 위험은 크지만 다양한 컬러와 영상의 변형에서 워터마크의 강인성을 보장할 수 있다. 실험 결과, 워터마크가 내장된 웨이브릿 압축 영상에서 워터마크의 강인성과 무감지성을 확인할 수 있었다.

• 천문학회지
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• 제29권spc1호
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• pp.75-76
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• 1996

We have started multi-color imaging program of nearby galaxies since last year and present preliminary result here. We selected 12 nearby galaxies classfied from E to Sab type and observed in BVRIJHK' bands. Photomtric parameters such as isophotal diameter, axial ratio, isophotal magnitude were measured and observed colors were compared with theoritical model. We find a standard evolution model agrees well with observed results.

• 산업경영시스템학회지
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• 제43권1호
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• 전기학회논문지P
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• 제62권2호
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• pp.90-94
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• 2013

In this paper, we propose a new algorithm to improve the contrast ratio, to preserve information of bright regions and to maintain the color of backlight image that appears with a great relative contrast. Backlight images of the natural environment have characteristics for difference of local brightness; the overall image contrast improvement is not easy. To improve the contrast of the backlight images, MSR (Multi-Scale Retinex) algorithm using the existing multi-scale Gaussian filter is applied. However, existing multi-scale Gaussian filter involves color distortion and information loss of bright regions due to excessive contrast enhancement and noise because of the brightness improvement of dark regions. Moreover, it also increases computational complexity due to the use of multi-scale Gaussian filter. In order to solve these problems, a linear MSR is performed that reduces the amount of computation from the HSV color space preventing the color distortion and information loss due to excessive contrast enhancement. It can also remove the noise of the dark regions which is occurred due to the improved contrast through edge preserving filter. Through experimental evaluation of the average color difference comparison of CIELAB color space and the visual assessment, we have confirmed excellent performance of the proposed algorithm compared to conventional MSR algorithm.

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 1998년도 가을 학술발표대회 논문집 디자인 학 연구
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• pp.36-37
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• 1998

• 정보처리학회논문지A
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• 제8A권3호
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• pp.227-234
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• 2001

In this paper we propose a block-type parallel preconditioner for solving large sparse nonsymmetric linear systems, which we expect to be scalable. It is Multi-Color Block SOR preconditioner, combined with direct sparse matrix solver. For the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with Multi-Color ordering. Since most of the time is spent on the diagonal inversion, which is done on each processor, we expect it to be a good scalable preconditioner. We compared it with four other preconditioners, which are ILU(0)-wavefront ordering, ILU(0)-Multi-Color ordering, SPAI(SParse Approximate Inverse), and SSOR preconditiner. Experiments were conducted for the Finite Difference discretizations of two problems with various meshsizes varying up to $1025{\times}1024$. CRAY-T3E with 128 nodes was used. MPI library was used for interprocess communications, The results show that Multi-Color Block SOR is scalabl and gives the best performances.

• 한국산업정보학회논문지
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• 제16권2호
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• pp.31-38
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• 2011

피부색은 자동화된 얼굴 인식을 위한 매우 중요한 정보 중의 하나이다. 본 논문에서는 다층 신경망(Multi-Layer Perceptron)을 이용한 피부 영역 검출 기법을 제안하였다. 제안된 방법은 적응적 조명 보정 기법을 통해 피부색 영역의 검출 성능을 개선하였고, 전처리 필터를 적용하여 피부색이 아닌 영역을 먼저 제거시킴으로써 처리 속도를 향상시켰다. 제안된 방법의 실험 결과 기존의 방법과 비교하여 보다 우수한 검출 결과를 나타냈으며, 처리 속도 또한 약 31~49% 향상시킬 수 있었다.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2009년도 9th International Meeting on Information Display
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• pp.1546-1549
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• 2009

In recent studies on tone reproduction with the objective of reproducing natural looking colors in digital images, an integrated multi-scale retinex (IMSR) has produced great naturalness in the resulting images. Most methods, including IMSR, work in RGB or quasi-RGB color spaces. As such, this produces hue distortion from the perspective of the human visual system. Accordingly, this paper proposes the hue preserved multi-scale Retinex (HPMSR) method to obtain a high contrast and naturalness. The proposed method enhanced the $L^*$ and saturation values in CIELAB color space. As a result, the visibility in dark shadows in the resulting images was improved.