• 한국지능시스템학회논문지
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• 제11권5호
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• pp.406-412
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• 2001

본 논문은 기존의 퍼지 단층 퍼셉트론 알고리즘의 학습 시간과 수렴성을 개선하기 위해 인간 신경계의 생리학적 뉴런 구조를 분석하며 퍼지 논리를 이용한 새로운 뉴런 구조를 제시하고, 이를 바탕으로 생리학적 퍼지 단층 퍼셉트론(P-FLSP: Physiological Fuzzy Single Layer Perceptron)에 대한 학습 모형과 학습 알고리즘을 제안한다. 제안된 학습 알고리즘의 성능을 평가하기 위해 Exclusive OR 문제, 3-bit parity 문제 그리고 차량 번호판 인식 문제 등에 적용하여 피곤의 피지 단층 퍼셉트론 알고리즘과 성능을 비교, 분석하였다. 실험 결과에서는 제안된 학습 알고리즘(P-FSLP)이 기존의 퍼지 단층 학습 알고리즘보다 지역 최소화에 빠질 가능성이 감소하였으며 학습 시간과 수렴성도 개선되었을 뿐만 아니라, 영상 인식등에 대한 응용 가능성도 제시되었다.

• 한국컴퓨터정보학회논문지
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• 제24권11호
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• pp.71-78
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• 2019

현재 상용화된 대부분의 3D 디스플레이는 왼쪽 눈과 오른쪽 눈의 입력영상을 다르게 만들어 입체감을 만드는 양안시차 방식을 적용하고 있다. 하지만 상용화된 3D 영상 출력 장치는 성능 부족에 의한 시청자의 불편 유발과 시청위치의 제약 등의 문제들이 있다. 본 논문에서는 기존 Dual-cell 구조 대비 시야각, Crosstalk 감소, 광투과도를 향상할 수 있는 Single-cell 구조의 편광안경식 입체영상 시스템을 개발하고, 투과도 실험과 시야각 평가를 통해 Single-cell 구조의 편광안경식 입체영상 시스템 특성분석과 Dual-cell 구조 대비 성능향상 효과 분석을 진행하였다. 분석 결과 Single-cell 구조가 Dual-cell 구조 대비 투과도 부분에서 약 25% 이상의 높은 성능을 보이며, 시야각 평가 중 입체 영상 특성품질의 주요지표인 3D crosstalk 지표는 약 37% 이상 향상되는 것을 확인할 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.520-525
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• 1992

This paper reports several mathematical properties of the filter vector developed for processing linearly-additive spatially-invariant image sequences. In this filtering of an image sequence into a single filtered image, the information about the image components originally distributed over the entire sequence is compressed into the one new image in a way that the desired component is enhanced and the undesired (interfering) components and noise are suppressed.

• 한국전기전자재료학회논문지
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• 제31권7호
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• pp.490-495
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• 2018

A multielectrode electronic paper film capable of expressing a single-color image was fabricated by injecting color electronic ink into an electronic paper panel; on the basis of its reflective or transparent properties, it is possible to control the expression of six single-color images and their transmittance. In this study, a single-color image was represented by driving a multielectrode electronic paper film; color coordinates were measured. The six capable single colors were yellowish pink (0.444, 0.354), white (0.355, 0.352), black (0.241, 0.241), orange (0.514, 0.360), reddish orange (0.606, 0.338), and reddish purple (0.469, 0.145). Color particles used in this paper were black and white, by which six colors are accomplished, but more single-color images can be combined by using cyan, magenta, and yellow particles.

• 디지털콘텐츠학회 논문지
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• 제18권8호
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• pp.1635-1640
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• 2017

본 논문에서는 싱글 야외 영상에서 날씨 분류를 위한 계층적 이미지 트리 모델을 정의하고, 영상의 밝기와 k-평균 세분화 영상을 이용한 날씨 분류 알고리즘을 제안하였다. 계층적 이미지 트리 모델의 첫 번째 레벨에서 실내와 야외 영상을 구분하고, 두 번째 레벨에서는 야외 영상이 주간, 야간 또는 일출/일몰 영상인지를 밝기 영상과 k-평균 세분화 영상을 이용하여 판단하였다. 마지막 레벨에서는 두 번째 레벨에서 주간 영상으로 분류된 경우 에지 맵과 안개 율을 기반으로 맑은 영상 또는 안개 영상인지를 최종 추정하였다. 실험 결과, 날씨 분류가 설계 규격대로 수행됨을 확인할 수 있었으며, 제안하는 방법이 주어진 영상에서 효과적으로 날씨 특징이 검출됨을 보였다.

• International journal of advanced smart convergence
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• 제9권3호
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• pp.118-126
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• 2020

In this paper, we present an effective single image fog removal by using the Joint Bright and Dark Channel (JBDC) and pixel-based transmission estimation to enhance the visibility of outdoor images susceptible to degradation due to weather and environmental conditions. The conventional methods include refinement process of coarse transmission with heavy computational complexity. The proposed transmission estimation reveals excellent edge-preserving performance and does not require the refinement process. We estimate the atmospheric light in pixel-based fashion, which can improve the transmission estimation performance and visual quality of the restored image. Moreover, we propose an adaptive transmission estimation to enhance the visual quality specifically in sky regions. Comprehensive experiments on various fog images show that the proposed method exhibits reduced computational complexity and excellent fog removal performance, compared with the existing methods; thus, it can be applied to various fields including real-time devices.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2006년도 6th International Meeting on Information Display
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• pp.294-297
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• 2006

Using a single LC panel and two lighting systems, we have developed an advanced LCD system called "Double-Sided LCD." It has only one LC panel and two lighting systems, but it can display images of the same size and resolution on both the front and the rear side. Furthermore, utilizing a simple and thin lighting system we have reduced dramatically its module thickness up to 3.0mm, much thinner than that of conventional dual-type LCD, which is quite appropriate for the use of mobile applications.

• 한국컴퓨터정보학회논문지
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• 제22권3호
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• pp.45-51
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• 2017

In this paper, we propose a consecutive-frame super-resolution method to tackle a moving object problem. The super-resolution is a method restoring a high resolution image from a low resolution image. The super-resolution is classified into two types, briefly, single-frame super-resolution and consecutive-frame super-resolution. Typically, the consecutive-frame super-resolution recovers a better than the single-frame super-resolution, because it use more information from consecutive frames. However, the consecutive-frame super-resolution failed to recover the moving object. Therefore, we proposed an improved method via moving object detection. Experimental results showed that the proposed method restored both the moving object and the background properly.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2012-2015
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• 2003

This paper proposes a pose-invariant face recognition method using cylindrical model and stereo camera. We divided this paper into two parts. One is single input image case, the other is stereo input image case. In single input image case, we normalized a face's yaw pose using cylindrical model, and in stereo input image case, we normalized a face's pitch pose using cylindrical model with estimated object's pitch pose by stereo geometry. Also, since we have advantage that we can utilize two images acquired at the same time, we can increase overall recognition rate by decision-level fusion. By experiment, we confirmed that recognition rate could be increased using our methods.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.525-528
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• 2019

Atmospheric particle removal is a challenging task and attacks wide interests in computer vision filed. In this paper, we proposed a single image snow removal framework based on deep residual networks. According to the fact that there are various snow sizes in a snow image, the inception module which consists of different filter kernels was adopted to extract multiple resolution features of the input snow image. Except the traditional mean square error loss, the perceptual loss and total variation loss were employed to generate more clean images. Experimental results on synthetic and realistic snow images indicated that the proposed method achieves superior performance in respect of visual perception and objective evaluation.