• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.565-568
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• 2008

본 논문은 RGB 24bit 컬러 영상으로 전달되는 카메라 원영상에 대해 사람의 얼굴을 안정적으로 감지할 수 있는 알고리즘을 제시한다. RGB 입력영상을 HSI 기반의 컬러모델로 변환하여 피부 색상을 추출하고 그리드 영상을 기반으로 CCL (Connected-Component Labeling) 알고리즘을 적용하여 피부 블럽을 검출한 뒤, 아다부스트 알고리즘을 이용하여 얼굴 영역과 얼굴이 아닌 다른 피부 영역을 구분한다. 제안방법은 일반적으로 얼굴 감지를 위하여 폭넓게 사용되고 있는 아다부스트 알고리즘만을 적용하였을 때보다 얼굴감지 오류를 줄일 수 있다.

• The Journal of the Korea Contents Association
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• v.15 no.12
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• pp.8-17
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• 2015

This paper presents a sound into color signal conversion using a colored-hearing synesthesia. The aim of the present paper is to implement a real-time conversion system which focuses on both hearing and sight which account for a great part of bodily senses. The proposed method of the real-time conversion of color into sound, in this paper, was simple and intuitive where scale, octave and velocity were extracted from MIDI input signals, which were converted into hue, intensity and saturation, respectively, as basic elements of HSI color model. In experiments, we implemented both the hardware system for delivering MIDI signals to PC and the VC++ based software system for monitoring both input and output signals, so we made certain that the conversion was correctly performed by the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.637-640
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• 2022

3차원 물체의 탐지와 자세 추정은 실내외 환경에서 장면 이해, 로봇의 물체 조작 작업, 자율 주행, 증강 현실 등과 같은 다양한 응용 분야들에서 공통적으로 요구되는 매우 중요한 시각 인식 기술이다. 깊이 지도를 요구하는 기존 연구들과는 달리, 본 논문에서는 RGB 컬러 영상만을 이용해 미지의 물체들, 즉 3차원 CAD 모델을 가지고 있지 않은 새로운 물체들을 탐지해내고, 이들의 자세를 추정해낼 수 있는 새로운 신경망 모델을 제안한다. 제안 모델에서는 최근 빠른 속도로 발전하고 있는 깊이 추정 기술을 이용함으로써, 깊이 측정 센서 없이도 물체 자세 추정에 필요한 깊이 지도를 컬러 영상에서 구해낼 수 있다. 본 논문에서는 벤치마크 데이터 집합을 이용한 실험을 통해, 제안 모델의 유용성을 평가한다.

• Journal of Digital Contents Society
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• v.4 no.1
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• pp.11-21
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• 2003

In computer vision, the goal of stereopsis is to determine the surface structure of real world form two or more perspective views of scene. It is similar to human visual system. We can avoid obstacles, recognize objects, and manipulate machine using three-dimensional information. Until recently, only gray-level images have been used as input to computation for depth determination, but the availability of color can further enhance the performance of computational stereopsis. There are many models to provide efficient color system. The simplest model, RGB model treats color as if it were composed of separate entities. Each color channel is processed individually by the same stereopsis module as used in the gray-level model. His Model decouples intensity component from color information. So it can deal with color properties without defect intensity information. Opponent color model is based on human visual system. In this model, the red-green-blue colors are combined into three opponent channels before further processing.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.291-298
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• 2009

In this paper we proposed a vision based realtime hand gesture recognition method. To extract skin color, we translate RGB color space into YCbCr color space and use CbCr color for the final extraction. To find the center of extracted hand region we apply practical center point extraction algorithm. We use Kalman filter to tracking hand region and use HMM(Hidden Markov Model) algorithm (learning 6 type of hand gesture image) to recognize it. We demonstrated the effectiveness of our algorithm by some experiments.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.301-310
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• 2021

The TATI model is a Traffic Accident Text to RGB Image model, which is a methodology proposed in this paper for predicting the severity of traffic accidents. Traffic fatalities are decreasing every year, but they are among the low in the OECD members. Many studies have been conducted to reduce the death rate of traffic accidents, and among them, studies have been steadily conducted to reduce the incidence and mortality rate by predicting the severity of traffic accidents. In this regard, research has recently been active to predict the severity of traffic accidents by utilizing statistical models and deep learning models. In this paper, traffic accident dataset is converted to color images to predict the severity of traffic accidents, and this is done via CNN models. For performance comparison, we experiment that train the same data and compare the prediction results with the proposed model and other models. Through 10 experiments, we compare the accuracy and error range of four deep learning models. Experimental results show that the accuracy of the proposed model was the highest at 0.85, and the second lowest error range at 0.03 was shown to confirm the superiority of the performance.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.33-38
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• 2010

In this paper, we proposed an algorithm implementing mouse functions using hand motion and number of fingers which are extracted from an image sequence. The sequence is acquired through a web camera and processed with image processing algorithms. The sequence is first converted from RGB model to YCbCr model to efficiently extract skin area and the extracted area is further processed using labeling, opening, and closing operations to decide the center of a hand. Based on the center position, the number of fingers is decided, which serves as the information to decide and perform a mouse function. Experimental results show that 94.0% of pointer moves and 96.0% of finger extractions are successful, which opens the possibility of further development for a commercial product.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.37-43
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• 2014

In this paper, we propose a robust lip detection algorithm using color clustering. At first, we adopt AdaBoost algorithm to extract facial region and convert facial region into Lab color space. Because a and b components in Lab color space are known as that they could well express lip color and its complementary color, we use a and b component as the features for color clustering. The nearest neighbour clustering algorithm is applied to separate the skin region from the facial region and K-Means color clustering is applied to extract lip-candidate region. Then geometric characteristics are used to extract final lip region. The proposed algorithm can detect lip region robustly which has been shown by experimental results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.25-33
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• 2006

This paper proposed device-independent color matching algorithm based on the 3D RGB lookup table (LUT) between mobile camera and mobile LCD (Liquid Crystal Display) to improve the color-fidelity. Proposed algorithm is composed of thee steps, which is device characterization, gamut mapping, 3D RGB-LUT design. First, the characterization of mobile LCD is executed using the sigmoidal function, different from conventional method such as GOG (Gain Offset Gamma) and S-curve modeling, based on the observation of electro-optical transfer function of mobile LCD. Next, mobile camera characterization is conducted by fitting the digital value of GretagColor chart captured under the daylight environment (D65) and tristimulus values (CIELAB) using the polynomial regression. However, the CIELAB values estimated by polynomial regression exceed the maximum boundary of the CIELAB color space. Therefore, these values are corrected by linear compression of the lightness and chroma. Finally, gamut mapping is used to overcome the gamut difference between mobile camera and moible LCD. To implement the real-time processing, 3D RGB-LUT is designed based on the 3D RGB-LUT and its performance is evaluated and compared with conventional method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.509-510
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• 2010

본 논문에서는 마우스 제어를 위한 불변 모멘트 기반의 손 인식 알고리즘을 제안한다. 이를 위하여 배경영상과 입력영상의 차이를 구하고, RGB 컬러모델을 HSV 컬러모델로 변환하여 피부색상과 유사한 영역을 얻었다. 이 둘 사이의 교집합을 통하여 손 영역을 추출하고 모폴로지 연산을 통해 잡음을 제거한 다음 불변 모멘트를 이용하여 손 영역을 인식하였다. 제안된 방법은 손의 이동, 크기 변화, 회전에 무관하게 손을 인식할 수 있다.