• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1287-1292
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• 2005

This paper presents an effective pattern classification model by designing an artificial neural network based pattern classifiers for face recognition. First, a RGB image inputted from a frame grabber is converted into a HSV image which is similar to the human beings' vision system. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction process is performed by matching with the edge and gray based templates. To make a light-invariant and qualified facial image, histogram equalization and intensity compensation processing using illumination plane are performed. The finally extracted and enhanced facial images are used for training the pattern classification models. The proposed H-ART2 model which has the hierarchical ART2 layers and F-LVQ model which is optimized by fuzzy membership make it possible to classify facial patterns by optimizing relations of clusters and searching clustered reference patterns effectively. Experimental results show that the proposed face recognition system is as good as the SVM model which is famous for face recognition field in recognition rate and even better in classification speed. Moreover high recognition rate could be acquired by combining the proposed neural classification models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권6호
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• pp.2289-2301
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• 2015

Mobile devices are becoming powerful enough to realize augmented reality (AR) application. This paper introduces two AR methods to estimate an environmental illumination distribution of a scene. In the first method, we extract the lighting direction and intensity from input images captured with a front-side camera of a mobile device, using its orientation sensor. The second method extracts shadow regions cast by three dimensional (3D) AR marker of known shape and size. Because previous methods examine per pixel shadow intensity, their performances are much affected by the number of sampling points, positions, and threshold values. By using a simple binary operation between the previously clustered shadow regions and the threshold real shadow regions, we can compute efficiently their relative area proportions according to threshold values. This area-based method can overcome point sampling problem and threshold value selection. Experiment results demonstrate that the proposed methods generate natural image with multiple smooth shadows in real-time.

• 디지털산업정보학회논문지
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• 제8권2호
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• pp.31-40
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• 2012

Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, credit-card verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and two-dimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two sub-images. Then, two-dimensional principal component analysis algorithm is separately applied to each sub-images. The individual scores obtained from two sub-images are integrated using a weighted-summation rule, and the fused-score is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMU-PIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.4170-4188
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• 2014

In this study, we present a denoising algorithm for high-frame-rate videos in an ultra-low illumination environment on the basis of Kalman filtering model and a new motion segmentation scheme. The Kalman filter removes temporal noise from signals by propagating error covariance statistics. Regarded as the process noise for imaging, motion is important in Kalman filtering. We propose a new motion estimation scheme that is suitable for serious noise. This scheme employs the small motion vector characteristic of high-frame-rate videos. Small changing patches are intentionally neglected because distinguishing details from large-scale noise is difficult and unimportant. Finally, a spatial bilateral filter is used to improve denoising capability in the motion area. Experiments are performed on videos with both synthetic and real noises. Results show that the proposed algorithm outperforms other state-of-the-art methods in both peak signal-to-noise ratio objective evaluation and visual quality.

• ALGAE
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• 제24권2호
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• pp.121-127
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• 2009

The unicellular green alga, Haematococcus pluvialis, accumulates the highest level of astaxanthin among knownastaxanthi.n-producing organisms. Light is the most important factor to induce astaxanthin by H. pluvialis. BIue andred LEDs, whose ${\lambda}_{max}$'s are 470 and 665 nm, respectively, were used for internally illuminated light sources.Fluorescent lamps were also used for both internal and external illumination sources. The astaxanthin levels in thesevarious lighting systems were analyzed and compared each other. The cultures under internally illuminated LEDsaccumulaled 20% more astaxanthin than those under fluorescent lamp. Furthermore, LEDs generated much lessheat than the fluorescent lamps, which gives one more reason for the LEDs being a suitable internal Light source forastaxanthin induction. The results reported here would lead novel designs of photobioreactors with improvementsof illumination methods for high level of astaxanthm production. The maximum astaxanthin concentrations as wellas the astaxanthin yield per supplied photon were increased by at least 20% when blue or red LEDs were supplied.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.575-586
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• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

• 한국컴퓨터그래픽스학회논문지
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• 제21권3호
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• pp.27-35
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• 2015

본 연구는 전역 조명 기법 중 하나인 환경-광 가림(ambient occlusion)을 이용한 볼륨 렌더링 방법을 설명한다. 볼륨 밀도 분포를 정규 분포로 가정하여, 환경-광 가림을 불투명도 전이함수의 변경과 무관하게 실시간 가시화할 수 있다. 전처리 과정에서 각 복셀 주변의 일정 크기 영역의 평균과 표준편차를 계산하여 두고, 가시화 단계에서 근방의 불투명도를 추정하여 밝기를 계산한다. 이 논문은 본 연구자들의 기존 연구를 발전시켜 이론적 모델을 일반화하고 출력 영상의 화질을 향상시킨다. 구체적으로 다양한 형태의 불투명도 전이함수를 사용할 수 있는 계산 모델을 제안한다. 그리고 영역의 크기를 다양하게 통계량을 생성하여 근처의 물체에 더 높은 가중치를 부여할 수 있도록 하였다. 최종적으로 환경-광 가림 효과와 지역 조명 효과를 혼합하여, 더 현실감 있는 화질의 볼륨 가시화 영상을 실시간으로 생성할 수 있다.

• 한국지능시스템학회논문지
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• 제24권1호
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• pp.71-77
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• 2014

본 논문에서는 저조도 및 음영이 생기는 조명 환경하에서 성능이 개선된 계량기 숫자 인식 방법을 제안한다. 저조도 및 음영 문제를 해결하기 위해 LN(Local Normalization) 처리 기법을 이용한 조명 정규화를 수행한 후, 계량기 숫자 영역 검출과 3단계 계량기 숫자 분할이 이루어진다. 마지막으로 분할된 숫자 데이터를 분류하기 위한 하이브리드 숫자 분류기가 적용된다. 제안된 하이브리드 숫자 분류기는 역전파 신경망과 템플레이트 매칭의 연속 결합으로 이루어지고, 계량기 숫자 분류에 보다 강인한 휴리스틱 규칙에 의해 최종적으로 숫자를 분류한다. 저조도 및 음영 조명 환경하의 다양한 계량기 종류에 대해 직접 촬영하여 자체 제작한 계량기 이미지 데이터베이스에 기반한 실험을 통해 본 논문에서 제안한 숫자 인식 방법을 평가하고, 제안된 계량기 숫자 인식 방법이 효과적으로 잘 동작함을 확인하였다.

• Current Optics and Photonics
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• 제1권1호
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• pp.23-28
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• 2017

The laser has been regarded as the potential illumination source for the next generation of projectors. However, currently the major issues in applying the laser as an illumination source for projectors are beam shaping and laser speckle. We present a compact solution for both issues by using a vibrating diffractive optical element (DOE). The DOE is designed and fabricated, and it successfully transforms the circular Gaussian laser beam to a low speckle contrast uniform rectangular pattern. Under a vibration frequency of 150 Hz and amplitude of $200{\mu}m$, the speckle contrast value is reduced from 67.67% to 13.78%, and the ANSI uniformity is improved from 24.36% to 85.54%. The experimental results demonstrate the feasibility and potential of the proposed scheme, and the proposed method is a feasible approach to the miniaturization of laser projection display illumination systems.

• 한국ITS학회:학술대회논문집
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• 한국ITS학회 2004년도 제3회 정기총회 및 추계학술대회
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• pp.53-57
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• 2004