• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1031-1043
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• 2013

Extracting hand region from images is the first part in the process to recognize hand posture and gesture interaction. Therefore, a good segmenting method is important because it determines the overall performance of hand recognition systems. Conventional hand segmentation researches were prone to changing illumination conditions or limited to the ability to detect multiple people. In this paper, we propose a robust technique based on the fusion of skin-color data and depth information for hand segmentation process. The proposed algorithm uses skin-color data to localize accurate seed location for region-growing from a complicated background. Based on the seed location, our algorithm adjusts each detected blob to fill up the hole region. A region-growing algorithm is applied to the adjusted blob boundary at the detected depth image to obtain a robust hand region against illumination effects. Also, the resulting hand region is used to train our skin-model adaptively which further reduces the effects of changing illumination. We conducted experiments to compare our results with conventional techniques which validates the robustness of the proposed algorithm and in addition we show our method works well even in a counter light condition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.439-445
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• 2021

Low-cost, ultra-high-speed cameras, made possible by the development of image sensors and small displays, can be very useful in image processing and pattern recognition. This paper introduces an algorithm that corrects irregular lighting from a high-speed image that is continuously input with a slight time interval, and which then obtains an exposed skin color region that is the area of interest in a person from the corrected image. In this study, the non-uniform lighting effect from a received high-speed image is first corrected using a frame blending technique. Then, the region of interest is robustly obtained from the input high-speed color image by applying an elliptical skin color distribution model generated from iterative learning in advance. Experimental results show that the approach presented in this paper corrects illumination in various types of color images, and then accurately acquires the region of interest. The algorithm proposed in this study is expected to be useful in various types of practical applications related to image recognition, such as face recognition and tracking, lighting correction, and video indexing and retrieval.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.457-464
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• 2021

The advent of the big data era has enabled the rapid development of deep learning that learns rules by itself from data. In particular, the performance of CNN algorithms has reached the level of self-adjusting the source data itself. However, the existing image processing method only deals with the image data itself, and does not sufficiently consider the heterogeneous environment in which the image is generated. Images generated in a heterogeneous environment may have the same information, but their features may be expressed differently depending on the photographing environment. This means that not only the different environmental information of each image but also the same information are represented by different features, which may degrade the performance of the image analysis model. Therefore, in this paper, we propose a method to improve the performance of the image color constancy model based on Adversarial Learning that uses image data generated in a heterogeneous environment simultaneously. Specifically, the proposed methodology operates with the interaction of the 'Domain Discriminator' that predicts the environment in which the image was taken and the 'Illumination Estimator' that predicts the lighting value. As a result of conducting an experiment on 7,022 images taken in heterogeneous environments to evaluate the performance of the proposed methodology, the proposed methodology showed superior performance in terms of Angular Error compared to the existing methods.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.10 no.4
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• pp.78-84
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• 1996

In this study, 3-D object recognition and restoration independent of the object translation for automotive kind recognition in parking administration system using an ultrasonic sensor array, neural networks and invariant moments are presented. Using invariant moment vectors of the acquired data 16$\times$8 pixels, 3-D objects could be classified by SCL (Simple Competitive Learning) neural networks. Modified SCL neural networks using the 16$\times$8 low resolution image was used for object restoration of 32$\times$32 high resolution image. Invariant moment vectors kept constant independent of the object translation. The recognition rates for the training and the testing data were 98[%] and 95[%], respectively. The experimental results have shown that ultrasonic sensor array with the neural networks could be applied for the detection of the automobiles and classification of the automotive kind.

• Communications of Mathematical Education
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• v.10
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• pp.169-188
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• 2000

급변하고 있는 정보화시대애서 수학교육은 예전의 암기식, 주입식에서 벗어나 새롭게 변화될 필요가 있다. 컴퓨터 매체가 수학교육에 도입된 결과 수학 내용과 수학을 이해하는 방법, 교수 ${\cdot}$ 학습 방법을 변화시키고 있으며 교수 ${\cdot}$ 학습이 일어나는 사회 ${\cdot}$ 문화적 환경을 변화시키고 있다. 학생들이 컴퓨터 테크놀러지를 이용해 수학적 이해를 얻고 수학적 힘을 길러 의사소통자, 문제해결자가 되도록 도와야 한다. 또한 실생활적인 맥락에서 상황화되는 중요한 아이디어를 동시에 가르침으로써 효율성을 성취하고 내용적 과잉을 극복하고 새 수학의 혁신, 다양성, 연속적 성장을 체계적으로 지지해야 한다. 이 글에서는 학생들의 개념적 이해와 문제해결을 돕기 위해 테크놀러지의 역할을 조명해보고 DERIVE(TI-92)를 이용한 수학 학습 예시를 제시하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.523-525
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• 2002

얼굴 검출은 하나의 영상으로부터 얼굴 존재 유무를 판단하고 그 위치와 방향, 크기 등을 알아내는 기술로 정의된다. 그러나 영상내의 특정 위치에 대한 얼굴 여부의 판단은 여러 가지 환경 변화와 매우 다양한 종류의 얼굴로 인해 정확하고 빠른 검출이 어렵다. 따라서 본 논문에서는 얼굴여부를 판단하기 위한 학습 데이터를 최적화하여 일반적인 외형기반의 알고리즘에 적용할 수 있는 방법을 제안한다. 제안된 방법은 영상에 대한 기본적인 전처리부터 입력으로 사용될 데이터의 추출에 이르기까지 최대한의 환경변화를 고려함으로써실제 적용 시 정확하고 빠른 판단이 가능하도록 하였다. 영상의 전처리로는 조명의 보상과 히스토그램 평활화가 사용되었고, 입력으로 사용하기 위한 학습 데이터의 정렬과 영상 샘플링 방법이 제안되었다. 얼굴 여부의 판단 실험은 각각 역전파 신경망, 마할라노비스 거리를 사용하여 영상의 얼굴 여부를 판정하고, 성공률을 측정하였다. 실험 결과 최적화 방법을 적용했을 때 적용하기 전보다 높은 성능의 성공률을 보였다.

• Journal of Korean language education
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• v.28 no.4
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• pp.367-402
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• 2017

The current study seeks to understand the multi-faceted desires of language learners through the theoretical lens of imagined communities (Norton, 2001). Particularly, the study focuses on the learners of Korean language-one of the less commonly taught languages in the U.S. that has received relatively less attention in previous literature on second language motivation. The study analyzed and compared the narratives told by eleven Korean language learners in a post-secondary language program, and identified four types of imagined communities: Communities of K-pop Culture, Communities of Professionals, Communities of Korean Family and Relatives, and Communities of ethnic Koreans. The study found that these imagined communities were not restricted to a specific region or an ethnic group but encompassed various populations connected through the use of Korean language. The study also found variability within what has been readily labelled as heritage motivation (or motivation related to heritage), as well as striking differences between heritage language learners and non-heritage language learners in terms of their scope of imagination.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.248-254
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• 2009

The aim of this study is to optimize the light source combination which can maximize the capability of the illuminance and color temperature of circadian lighting apparatus. To achieve this goal, the circadian lighting apparatus was consisted of two different types of fluorescent lamps having different color temperature of 2000K and 8000K, respectively, and the capability of the illuminance and color temperature of circadian lighting apparatus was evaluated by optical simulation as the number of the respective lamps were varied. Considering the Kruithof's curve and exceptional cases, the ranges of illuminance and color temperature for the living activities were reclassified in 4 groups - gathering, studying, relaxing and sleeping - so that the target range of illuminance and color temperature of lighting apparatus was settled. As a result, in the case of adopting two fixtures in which four 2000K lamps and five 8000K lamps were consisted, respectively to one fixture, the highest illuminance was expected at 4000K and over 500lx of illuminance was calculated between 3000K and 6000K. Through the optimized combination of light sources, the range of illuminance and color temperature were calculated as $44{\sim}750lx$ and $2500{\sim}6500K$, respectively.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.816-824
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• 2009

In the face space, face images due to illumination and pose variations have a nonlinear distribution. Active Appearance Models (AAM) based on the linear model have limits to the nonlinear distribution of face images. In this paper, we assume that a few clusters of face images are given; we build local AAMs according to the clusters of face images, and then select a proper AAM model during the fitting phase. To solve the problem of updating fitting parameters among the models due to the model changing, we propose to build in advance relationships among the clusters in the parameter space from the training images. In addition, we suggest a gradual model changing to reduce improper model selections due to serious fitting failures. In our experiment, we apply the proposed model to Yale Face Database B and compare it with the previous method. The proposed method demonstrated successful fitting results with strongly illuminated face images of deep shadows.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.9
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• pp.1-9
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• 2015

The aim of this study is to deduce color temperature and illuminance by conducting the preference & affective evaluation consequent on illuminance change of ambient light in case of the lighting method of ambient light mixed with task light in time of reading which is visual work action among the action in learning space. As a result of the prior survey on preferred lighting method in time of the act of reading targeting 20 experts before doing evaluation, the method of lighting mixed with ambient light and task light was found to be the highest. Such a result is analyzed to be attributable to the fact that the less the difference in illuminance of nearby space and work surface because of the mixed method of lighting, the less the glare, which makes a reader feels easy and concentrate on reading. On the basis of descriptive statistics of evaluation results and impact analysis by category, this study recommends the application of combinations of ambient light illuminance ranging from 40lx to 100lx with color temperature of 5500~6000K in case of the method of lighting mixed with general light and task light.