• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.98-101
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• 2006

본 논문에서는 얼굴영상 인식의 전처리 단계인 피부 영역 자동 검출시 적용 가능하며 조명변화에 강건한 피부 영역 검출을 위한 혼합 컬러모델을 제시한다. 또한, 사용자별로 차이를 보이는 다양한 피부색을 자동으로 인식하고 사용자에 적합한 피부색 영역을 결정하기 위하여 제시한 컬러 모델을 기반으로 한 피부색 영역 모델링 전처리 단계를 제시한다. 우선, 사용자 및 사용 카메라에 따라 차이를 보이는 피부색에 대한 영역 모델을 구축하기 위하여 화면상의 가운데에 손이나 얼굴 영역이 위치하도록 하고 일정 프레임의 화면 정보를 취득한다. 취득 화면 정보로서 각 픽셀에 대한 정규화 된 RGB 성분 및 H 성분, V 성분 정보를 추출하고 이에 대한 평균화된 혼합 컬러 모델을 구축한다. H성분으로 피부색과 비슷한 배경을 제거하고 여기에 YUV 성분 중 적색에서 밝기 값을 뺀 성분인 V 값을 한 번 더 사용하여 밝기 값을 제거한 보다 뚜렷한 얼굴영역을 검출한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.463-467
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• 2004

본 논문에서는 컬러정보를 이용하여 외부 조명의 영향에 대응하면서 얼굴 후보영역을 추출하고, 추출된 후보 영역으로부터 다채널 스킨컬러 모델로 특정 정보를 추출하는 검출 기법을 제시한다. 외부 조명에 민감한 스킨컬러 특성을 고려해 색상정보와 광도를 분리할 수 있는 Y $C_{r}$ , $C_{b}$ 색상모델을 이용하며, Green, Blue 채널의 정보를 Gaussian 확률밀도 모델로부터 $C_{b-}$ $C_{g}$ 의 좁은 범위에 분포되어 있는 스킨컬러 영역 밀도를 모델링한다. 또한 얼굴영역에 Region Restricting과 임계값 반복 알고리즘을 사용하여 눈 영역 검출 과정을 보이고, 실시간 복합 얼굴 검출 시스템 조명방식에 의해 결과를 나타낸다.다.

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.57-64
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• 2014

Recently, many human-friendly communication methods have been studied for human-machine interface(HMI) without using any physical devices. One of them is the vision-based gesture recognition that this paper deals with. In this paper, we define some gestures for interaction with objects in a predefined virtual world, and propose an efficient method to recognize them. For preprocessing, we detect and track the both hands, and extract their silhouettes from the low-resolution hand images captured by a webcam. We modeled skin color by two Gaussian distributions in RGB color space and use blob-matching method to detect and track the hands. Applying the foodfill algorithm we extracted hand silhouettes and recognize the hand shapes of Thumb-Up, Palm and Cross by detecting and analyzing their modes. Then, with analyzing the context of hand movement, we recognized five predefined one-hand or both-hand gestures. Assuming that one main user shows up for accurate hand detection, the proposed gesture recognition method has been proved its efficiency and accuracy in many real-time demos.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.12
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• pp.560-567
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• 2008

In this paper, we discuss a simple and straightforward binarization procedure which can generate black/white comics from the video frame image. Generally, the region of human's skin is colored white or light gray, while the dark region is filled with the irregular but regular patterns like hatching in most of the black/white comics. Note that it is not enough for simple threshold method to perform this work. Our procedure is decoupled into four processes. First, we use bilateral filter to suppress noise color variation and reserve boundaries. Then, we perform mean-shift segmentation for each similar colored pixels to be clustered. Third, the clustered regions are merged and extended by our region extension algorithm considering each color of their regions. Finally, we decide which pixels are on or off using by our dynamic binarization method based on the HSV color model. Our novel black/white cartooning procedure was so successful to render comic cuts from a well-known cinema in a resonable time and manual intervention.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• Annual Conference of KIPS
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• 2006.05a
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• pp.681-684
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• 2006

본 논문은 능동형상모델(Active Shape Model: ASM)을 사용하여 손바닥의 형상을 추출하고 경계형상을 추적하기 위한 방법을 제안한다. 먼저, 경계추적을 위한 초기위치를 입력하기 위해 컬러영상에서 피부색영역의 위치 정보를 통해 중심점을 찾고 그 값을 통해 ASM을 이용하여 손바닥의 영역을 찾는다. ASM은 다양한 경계형상의 학습을 통해 평균값과 형상의 지배적 변형을 나타내는 형상벡터를 추출하기 위한 방법론이며 생체조직과 같은 형상이 일정하지 않고 평균형상을 기준으로 변화하는 형상의 외형을 추출, 추적하기에 적합한 기술이다. 본 논문에서는 피부색 특징을 이용하여 초기 손바닥의 위치를 찾고 이러한 위치정보를 이용하여 손 경계형상의 변화를 추적하는 방법을 실험을 통해 검증하였다

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.12
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• pp.1062-1067
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• 2005

This paper discusses a skeleton feature extraction method for ubiquitous robot system. The skeleton features are used to analyze human motion and pose estimation. In different conventional feature extraction environment, the ubiquitous robot system requires more robust feature extraction method because it has internal vibration and low image quality. The new hybrid silhouette extraction method and adaptive skeleton model are proposed to overcome this constrained environment. The skin color is used to extract more sophisticated feature points. Finally, the experimental results show the superiority of the proposed method.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.214-216
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• 2006

Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.141-144
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• 2010

본 논문에서는 컴퓨터 비전에 기반을 둔 방법으로 실시간으로 사람의 손의 모양을 인식하는 알고리즘을 제안한다. 기본적인 전처리 과정과 피부 값의 검출을 통해서 사용자의 피부색상을 검출한 후 팔 영역과 얼굴영역을 제거하고, 손 영역만 검출한 뒤 손의 무게중심을 구한다. 그 후에 손의 궤적을 추적하기 위해 칼만필터를 이용하였으며, 손의 모양을 인식하기 위한 방법으로 Hidden Markov Model을 이용하여 사용자의 손 모양 6가지를 학습한 후 인식하였다. 실험을 통하여 제안한 방법의 효과를 입증하였다.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.318-326
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• 2005

This paper presents the image feature extraction method and the image classification technique to select the harmful image flowed from the Internet by grade of image contents such as harmlessness, sex-appealing, harmfulness (nude), serious harmfulness (adult) by the characteristic of the image. In this paper, we suggest skin area detection technique to recognize whether an input image is harmful or not. We also propose the ROI detection algorithm that establishes region of interest to reduce some noise and extracts harmful degree effectively and defines the characteristics in the ROI area inside. And this paper suggests the multiple-SVM training method that creates the image classification model to select as 4 types of class defined above. This paper presents the multiple-SVM classification algorithm that categorizes harmful grade of input data with suggested classification model. We suggest the skin likelihood image made of the shape information of the skin area image and the color information of the skin ratio image specially. And we propose the image feature vector to use in the characteristic category at a course of traininB resizing the skin likelihood image. Finally, this paper presents the performance evaluation of experiment result, and proves the suitability of grading image using image feature classification algorithm.