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      KCI등재

      깊이영상에서 효율적인 핸드 마우스를 위한 3D 포인팅

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      https://www.riss.kr/link?id=A101701592

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      This paper proposes a 3D pointing interface that is designed for the efficient application of a hand mouse. The proposed method uses depth images to secure high-quality results even in response to changes in lighting and environmental conditions and uses the normal vector of the palm of the hand to perform 3D pointing. First, the hand region is detected and tracked using the existing conventional method; based on the information thus obtained, the region of the palm is predicted and the region of interest is obtained. Once the region of interest has been identified, this region is approximated by the plane equation and the normal vector is extracted. Next, to ensure stable control, interpolation is performed using the extracted normal vector and the intersection point is detected. For stability and efficiency, the dynamic weight using the sigmoid function is applied to the above detected intersection point, and finally, this is converted into the 2D coordinate system. This paper explains the methods of detecting the region of interest and the direction vector and proposes a method of interpolating and applying the dynamic weight in order to stabilize control. Lastly, qualitative and quantitative analyses are performed on the proposed 3D pointing method to verify its ability to deliver stable control.
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      This paper proposes a 3D pointing interface that is designed for the efficient application of a hand mouse. The proposed method uses depth images to secure high-quality results even in response to changes in lighting and environmental conditions and u...

      This paper proposes a 3D pointing interface that is designed for the efficient application of a hand mouse. The proposed method uses depth images to secure high-quality results even in response to changes in lighting and environmental conditions and uses the normal vector of the palm of the hand to perform 3D pointing. First, the hand region is detected and tracked using the existing conventional method; based on the information thus obtained, the region of the palm is predicted and the region of interest is obtained. Once the region of interest has been identified, this region is approximated by the plane equation and the normal vector is extracted. Next, to ensure stable control, interpolation is performed using the extracted normal vector and the intersection point is detected. For stability and efficiency, the dynamic weight using the sigmoid function is applied to the above detected intersection point, and finally, this is converted into the 2D coordinate system. This paper explains the methods of detecting the region of interest and the direction vector and proposes a method of interpolating and applying the dynamic weight in order to stabilize control. Lastly, qualitative and quantitative analyses are performed on the proposed 3D pointing method to verify its ability to deliver stable control.

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      참고문헌 (Reference)

      1 "http://www.dh.aist.go.jp/en/research/centered/dhand-link2/"

      2 "http://www.cyberglovesystems.com/products /cyberglove-ii/overview"

      3 S. J. Miller, "The Method of Least Squares" Mathematics Department Brown University 2006

      4 N. D. Binh, "Real-time hand tracking and gesture recognition system" 362-368, 2005

      5 S. Malassiotis, "Real-time hand posture recognition using range data" 26 (26): 1027-1037, 2008

      6 A. Aksaç, "Real-time Multi-Objective Hand Posture/Gesture Recognition by Using Distance Classifiers and Finite State Machine for Virtual Mouse Operations" II-457-II-461, 2011

      7 R. Y. Wang, "Real-Time Hand-Tracking with a Color Glove" 28 (28): 2009

      8 S. I. Joo, "Real-Time Depth-Based Hand Detection and Tracking" 2014 : 1-17, 2014

      9 D. Eberly, "Least Squares Fitting of Data"

      10 D. D. Luong, "Human Computer Interface Using the Recognized Finger Parts of Hand Depth Silhouette via Random Forests" 905-909, 2013

      1 "http://www.dh.aist.go.jp/en/research/centered/dhand-link2/"

      2 "http://www.cyberglovesystems.com/products /cyberglove-ii/overview"

      3 S. J. Miller, "The Method of Least Squares" Mathematics Department Brown University 2006

      4 N. D. Binh, "Real-time hand tracking and gesture recognition system" 362-368, 2005

      5 S. Malassiotis, "Real-time hand posture recognition using range data" 26 (26): 1027-1037, 2008

      6 A. Aksaç, "Real-time Multi-Objective Hand Posture/Gesture Recognition by Using Distance Classifiers and Finite State Machine for Virtual Mouse Operations" II-457-II-461, 2011

      7 R. Y. Wang, "Real-Time Hand-Tracking with a Color Glove" 28 (28): 2009

      8 S. I. Joo, "Real-Time Depth-Based Hand Detection and Tracking" 2014 : 1-17, 2014

      9 D. Eberly, "Least Squares Fitting of Data"

      10 D. D. Luong, "Human Computer Interface Using the Recognized Finger Parts of Hand Depth Silhouette via Random Forests" 905-909, 2013

      11 F. Kirac, "Hierarchically constrained 3D hand pose estimation using regression forests from single frame depth data" 2013

      12 M. Elmezain, "Hand trajectory-based gesture spotting and recognition using HMM" 3577-3580, 2009

      13 X. Liu, "Hand Gesture Recognition using Depth Data" 529-534, 2004

      14 Z. Song, "Hand Detection and Gesture Recognition Exploit Motion Times Image in Complicate Scenarios" 6545 : 628-636, 2010

      15 S. I. Joo, "Dynamic Soft Cascade : Application to Gesture Recognition" University of Soongsil 2014

      16 P. Suryanarayan, "Dynamic Hand Pose Recognition using Depth Data" 3105-3108, 2010

      17 D. Kim, "Digits : Freehand 3D Interactions Anywhere Using a Wrist-Worn Gloveless Sensor" 167-176, 2012

      18 F. Dominio, "Combining multiple depth-based descriptors for hand gesture recognition" 2013

      19 S. P. Priyal, "A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments" 46 (46): 2202-2219, 2013

      20 D. J. Sturman, "A Survey of Glove-based Input" 14 (14): 30-39, 1994

      21 A. Kurakin, "A Real Time System for Dynamic Hand Gesture Recognition with a Depth Sensor" 1975-1979, 2012

      22 H. J. Park, "A Method for Controlling Mouse Movement using a Real-Time Camera" Brown University 2010

      23 T. G. Zimmerman, "A Hand Gesture Interface Device" 189-192, 1987

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2026 평가예정 재인증평가 신청대상 (재인증)
      2020-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2017-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2013-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2007-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2006-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2004-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.44 0.44 0.44
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.43 0.38 0.58 0.15
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