• Title/Summary/Keyword: 끝점탐지

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Skin Color Based Hand and Finger Detection for Gesture Recognition in CCTV Surveillance (CCTV 관제에서 동작 인식을 위한 색상 기반 손과 손가락 탐지)

  • Kang, Sung-Kwan;Chung, Kyung-Yong;Rim, Kee-Wook;Lee, Jung-Hyun
    • The Journal of the Korea Contents Association
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    • v.11 no.10
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    • pp.1-10
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    • 2011
  • In this paper, we proposed the skin color based hand and finger detection technology for the gesture recognition in CCTV surveillance. The aim of this paper is to present the methodology for hand detection and propose the finger detection method. The detected hand and finger can be used to implement the non-contact mouse. This technology can be used to control the home devices such as home-theater and television. Skin color is used to segment the hand region from background and contour is extracted from the segmented hand. Analysis of contour gives us the location of finger tip in the hand. After detecting the location of the fingertip, this system tracks the fingertip by using only R channel alone, and in recognition of hand motions to apply differential image, such as the removal of useless image shows a robust side. We explain about experiment which relates in fingertip tracking and finger gestures recognition, and experiment result shows the accuracy above 96%.

Color Area Correction Algorithm for Tracking Curved Fingertip (구부러진 손가락 끝점 추적을 위한 컬러 영역 보정 알고리즘)

  • Kang, Sung-Kwan;Chung, Kyung-Yong;Rim, Kee-Wook;Lee, Jung-Hyun
    • The Journal of the Korea Contents Association
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    • v.11 no.10
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    • pp.11-18
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    • 2011
  • In the field of image processing to track the fingertip much research has been done. The most common way to calculate the fingertip first, to extract color information. Then, it uses Blob Coloring algorithms which are expressed in blob functions the skin contour and calculates. The algorithm from contour decides the highest location with the fingertip. But this method when measuring it location from the finger condition which bents is not the actual fingertip and has the problem which detects the location which goes wrong. This paper proposes the color space correction algorithm to tracks the fingertip which bents. The method which proposes when tracking the fingertip from the finger condition which bents solves the problem which measures the location which goes wrong. Aim of this paper in compliance with the propensity of the users forecasts a problem in advance and corrects with improvement at the time of height boil an efficiency. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the proposed method. Accordingly, the satisfaction and the quality of services will be improved the image recognition.

Identification of Underwater Ambient Noise Sources Using MFCC (MFCC를 이용한 수중소음원의 식별)

  • Hwang, Do-Jin;Kim, Jea-Soo
    • Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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    • 2006.11a
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    • pp.307-310
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    • 2006
  • Underwater ambient noise originating from the geophysical, biological, and man-made acoustic sources contains much information on the sources and the ocean environment affecting the performance of the sonar equipments. In this paper, a set of feature vectors of the ambient noises using MFCC is proposed and extracted to form a data base for the purpose of identifying the noise sources. The developed algorithm for the pattern recognition is applied to the observed ocean data, and the initial results are presented and discussed.

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Identification of Underwater Ambient Noise Sources Using Hilbert-Huang Transfer (힐버트-후앙 변환을 이용한 수중소음원의 식별)

  • Hwang, Do-Jin;Kim, Jea-Soo
    • Journal of Ocean Engineering and Technology
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    • v.22 no.1
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    • pp.30-36
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    • 2008
  • Underwater ambient noise originating from geophysical, biological, and man-made acoustic sources contains information on the source and the ocean environment. Such noise affectsthe performance of sonar equipment. In this paper, three steps are used to identify the ambient noise source, detection, feature extraction, and similarity measurement. First, we use the zero-crossing rate to detect the ambient noisesource from background noise. Then, a set of feature vectors is proposed forthe ambient noise source using the Hilbert-Huang transform and the Karhunen-Loeve transform. Finally, the Euclidean distance is used to measure the similarity between the standard feature vector and the feature vector of the unknown ambient noise source. The developed algorithm is applied to the observed ocean data, and the results are presented and discussed.

Hand Pose Recognition Using Fingertip Detection (손가락 끝 점을 이용한 손 형상 인식)

  • Kim, Kyung-Ho;Lee, Chil-Woo
    • 한국HCI학회:학술대회논문집
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    • 2006.02a
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    • pp.1143-1148
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    • 2006
  • 사용자 친화형 유저 인터페이스 구현을 위해 인간의 손 형상을 실시간으로 인식하는 연구의 중요성이 부각되고 있다. 그러나 인간의 손은 자유도가 크기 때문에 손 형상을 정확히 인식하기란 매우 어렵고 또한 피부색과 유사한 색을 가지는 복잡한 배경에서는 더욱 곤란하다. 본 논문에서는 별도의 센서를 부착하지 않고 카메라를 사용하여 피부색 정보에 의한 손 형상을 분할한 후 손가락 끝 점을 찾는다. 찾은 손가락 끝점을 이용하여 방향을 탐지하는 알고리즘에 대해 기술한다. 이 방법은 템플리트 매칭을 이용하여 손가락 끝 점을 탐색한 후 찾은 손 가락 끝 점과 손목의 중심을 이용하여 전, 후, 좌, 우 방향을 탐지한다. 제안하는 방법을 이용하여 3D가상현실 공간에서의 Navigation에 응용하였으며, 실험결과 전진, 후진 및 좌측, 우측의 방향전환도 매우 좋은 결과를 보였다. 또한 본 논문에서 제안하는 방법은 마우스, 키보드, 조이스틱 등의 조작 없이 전, 후, 좌, 우 방향전환을 사용자가 직관적으로 지시함으로써 보다 자연스러운 인간과 컴퓨터의 상호작용을 제공할 수 있을 것이다.

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A Study on Lip Detection based on Eye Localization for Visual Speech Recognition in Mobile Environment (모바일 환경에서의 시각 음성인식을 위한 눈 정위 기반 입술 탐지에 대한 연구)

  • Gyu, Song-Min;Pham, Thanh Trung;Kim, Jin-Young;Taek, Hwang-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.4
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    • pp.478-484
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    • 2009
  • Automatic speech recognition(ASR) is attractive technique in trend these day that seek convenient life. Although many approaches have been proposed for ASR but the performance is still not good in noisy environment. Now-a-days in the state of art in speech recognition, ASR uses not only the audio information but also the visual information. In this paper, We present a novel lip detection method for visual speech recognition in mobile environment. In order to apply visual information to speech recognition, we need to extract exact lip regions. Because eye-detection is more easy than lip-detection, we firstly detect positions of left and right eyes, then locate lip region roughly. After that we apply K-means clustering technique to devide that region into groups, than two lip corners and lip center are detected by choosing biggest one among clustered groups. Finally, we have shown the effectiveness of the proposed method through the experiments based on samsung AVSR database.