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      깊이정보를 이용한 실시간 손 영역 검출 및 추적 = Real-time Hand Region Detection and Tracking using Depth Information

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      https://www.riss.kr/link?id=A101869560

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      In this paper, we propose a real-time approach for detecting and tracking a hand region by analyzing depth images. We build a hand model in advance. The model has the shape information of a hand. The detecting process extracts out moving areas in an image, which are possibly caused by moving a hand in front of a camera. The moving areas can be identified by analyzing accumulated difference images and applying the region growing technique. The extracted moving areas are compared against a hand model to get justified as a hand region. The tracking process keeps the track of center points of hand regions of successive frames. For this purpose, it involves three steps. The first step is to determine a seed point that is the closest point to the center point of a previous frame. The second step is to perform region growing to form a candidate region of a hand. The third step is to determine the center point of a hand to be tracked. This point is searched by the mean-shift algorithm within a confined area whose size varies adaptively according to the depth information. To verify the effectiveness of our approach, we have evaluated the performance of our approach while changing the shape and position of a hand as well as the velocity of hand movement.
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      In this paper, we propose a real-time approach for detecting and tracking a hand region by analyzing depth images. We build a hand model in advance. The model has the shape information of a hand. The detecting process extracts out moving areas in an i...

      In this paper, we propose a real-time approach for detecting and tracking a hand region by analyzing depth images. We build a hand model in advance. The model has the shape information of a hand. The detecting process extracts out moving areas in an image, which are possibly caused by moving a hand in front of a camera. The moving areas can be identified by analyzing accumulated difference images and applying the region growing technique. The extracted moving areas are compared against a hand model to get justified as a hand region. The tracking process keeps the track of center points of hand regions of successive frames. For this purpose, it involves three steps. The first step is to determine a seed point that is the closest point to the center point of a previous frame. The second step is to perform region growing to form a candidate region of a hand. The third step is to determine the center point of a hand to be tracked. This point is searched by the mean-shift algorithm within a confined area whose size varies adaptively according to the depth information. To verify the effectiveness of our approach, we have evaluated the performance of our approach while changing the shape and position of a hand as well as the velocity of hand movement.

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      참고문헌 (Reference)

      1 황문구, "퍼지 추론을 이용한 비전 기반 실시간 손모양 인식" 한국정보기술학회 6 (6): 53-59, 2008

      2 양환석, "멀티미디어 시스템을 위한 영상내의 손 인식에 관한 연구" 한국콘텐츠학회 5 (5): 267-274, 2005

      3 석흥일, "동적 베이스망 기반의 양손 제스처 인식" 한국정보과학회 35 (35): 265-279, 2008

      4 "http://www.imageprocessingplace.com/downloads_V3 /root_ downloads/tutorials/contour_tracing_Abeer_George_Ghunei m/index.html"

      5 V. Pavlovic, "Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review" 19 (19): 677-695, 1997

      6 B. Stenger, "Template-Based Hand Pose Recognition Using Multiple Cues" 551-560, 2006

      7 C. P. Chen, "Real-time Hand Tracking on Depth Images" IEEE 1-4, 2011

      8 "PrimeSensor"

      9 A. Yilmaz, "Object tracing: A survey" 38 (38): 2006

      10 H. Breu, "Linear Time Euclidean Distance Transform Algorithms" 17 (17): 529-533, 1995

      1 황문구, "퍼지 추론을 이용한 비전 기반 실시간 손모양 인식" 한국정보기술학회 6 (6): 53-59, 2008

      2 양환석, "멀티미디어 시스템을 위한 영상내의 손 인식에 관한 연구" 한국콘텐츠학회 5 (5): 267-274, 2005

      3 석흥일, "동적 베이스망 기반의 양손 제스처 인식" 한국정보과학회 35 (35): 265-279, 2008

      4 "http://www.imageprocessingplace.com/downloads_V3 /root_ downloads/tutorials/contour_tracing_Abeer_George_Ghunei m/index.html"

      5 V. Pavlovic, "Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review" 19 (19): 677-695, 1997

      6 B. Stenger, "Template-Based Hand Pose Recognition Using Multiple Cues" 551-560, 2006

      7 C. P. Chen, "Real-time Hand Tracking on Depth Images" IEEE 1-4, 2011

      8 "PrimeSensor"

      9 A. Yilmaz, "Object tracing: A survey" 38 (38): 2006

      10 H. Breu, "Linear Time Euclidean Distance Transform Algorithms" 17 (17): 529-533, 1995

      11 C. Manresa, "Hand Tracking and Gesture Recognition for Human-Computer Interaction" 5 (5): 96-104, 2005

      12 P. Breuer, "Hand Gesture Recognition with a novel IR Time-of-Flight Range Camera-A pilot study" 4418 : 247-, 2007

      13 M. B. Holte, "Fusion of range and intensity information for view invariant gesture recognition" 1-7, 2008

      14 I. Oikonomidis, "Efficient model-based 3D tracking of hand articulations using Kinect" 2011

      15 S. H. Park, "3D hand tracking using Kalman filter in depth space" SPRINGER INTERNATIONAL PUBLISHING AG 2012 (2012): 1-18, 2012

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2027 평가 재인증평가 신청대상 (재인증)
      2021-01-01 등재 등재학술지 유지 (재인증) KCI등재
      2018-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2015-01-01 등재 등재학술지 유지 (계속평가) KCI등재
      2012-10-31 학술지명변경 한글명 : 소프트웨어 및 데이터 공학 -> 정보처리학회논문지. 소프트웨어 및 데이터 공학 KCI등재
      2012-10-10 학술지명변경 한글명 : 정보처리학회논문지B -> 소프트웨어 및 데이터 공학
      외국어명 : The KIPS Transactions : Part B -> KIPS Transactions on Software and Data Engineering
      KCI등재
      2010-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 등재 등재학술지 유지 (등재유지) KCI등재
      2003-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
      2002-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2000-07-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.35 0.35 0.28
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.23 0.19 0.511 0.06
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