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      KCI등재 SCOPUS

      Real-Time Image Segmentation and Determination of 3D Coordinates for Fish Surface Area and Volume Measurement based on Stereo Vision

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      다국어 초록 (Multilingual Abstract)

      This paper proposes the real-time image segmentation and determination of 3D coordinates for fish surface area and volume measurements based on stereo vision. The segmentation process is performed in real-time on the video frame. Complexity and comput...

      This paper proposes the real-time image segmentation and determination of 3D coordinates for fish surface area and volume measurements based on stereo vision. The segmentation process is performed in real-time on the video frame. Complexity and computation time are reduced by segmenting hue and value feature spaces separately before combining result. Morphological erosion and morphological dilation are applied to remove any object pixels that are clustered as background pixels. Finally, image segmentation is obtained in the binary image of an object. A step-by-step approach to computing a binary image is applied to obtain segmentation image. The object is considered a fish on a platform with a known background color, a known geometric shape for the fish, and the stereo camera is in fixed position to capture an image. The objective of this paper is to obtain a simple but accurate 3D image that is capable of capturing the 3D coordinates values of the fish object. The followings are done for this task: First, the stereo camera is calibrated to collect its intrinsic parameters and distortion parameters correctly. Second, an image is captured by the camera, that is segmented in real-time to distinguish between the object’s destination and its surroundings. Finally, the object coordinates, surface area, and volume of the fish are measured. Experiment results show that the proposed method has good results in real-time segmentation, and shows different surface areas and volumes within about 6% and 5.3%, respectively.

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      목차 (Table of Contents)

      • Abstract
      • I. Introduction
      • II. Real-Time Image Segmentation
      • III. 3D Measurement and Camera Calibration
      • IV. Measurement of Fish Surface Area and Volume
      • Abstract
      • I. Introduction
      • II. Real-Time Image Segmentation
      • III. 3D Measurement and Camera Calibration
      • IV. Measurement of Fish Surface Area and Volume
      • Ⅴ. Experimental Results
      • Ⅴ. Conclusion
      • REFERENCES
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      참고문헌 (Reference)

      1 ZED Stereo Camera,

      2 이병룡, "유전자알고리즘을 이용한 영상분할 문턱값의 자동선정에 관한 연구" 제어·로봇·시스템학회 17 (17): 587-595, 2011

      3 S. Helmer, "Using stereo for object recognition" 3121-3127, 2010

      4 P. I. Coke, "Real-time vision, tracking and control" 622-629, 2000

      5 G. Saravanan, "Real time implementation of RGB to HSV/HSI/HSL and its reverse colorspace models" 462-466, 2016

      6 L. J. Belaid, "Image segmentation: a watershed transformation algorithm" 93-102, 2009

      7 H. Li, "Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms" 16 (16): 785-798, 1997

      8 J. Rantung M. T Tran, "Determination of the fish surface area and volume using ellipsoid approximation method applied for image processing" 465 : 334-347, 2017

      9 T. H. Nguyen, "Control of a mobile picking robot for path tracking and object grasping using a 3D vision stereo camera" Pukyong National University 2016

      10 S. Zhu, "An image segmentation algorithm in image processing based on threshold segmentation" 673-678, 2007

      1 ZED Stereo Camera,

      2 이병룡, "유전자알고리즘을 이용한 영상분할 문턱값의 자동선정에 관한 연구" 제어·로봇·시스템학회 17 (17): 587-595, 2011

      3 S. Helmer, "Using stereo for object recognition" 3121-3127, 2010

      4 P. I. Coke, "Real-time vision, tracking and control" 622-629, 2000

      5 G. Saravanan, "Real time implementation of RGB to HSV/HSI/HSL and its reverse colorspace models" 462-466, 2016

      6 L. J. Belaid, "Image segmentation: a watershed transformation algorithm" 93-102, 2009

      7 H. Li, "Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms" 16 (16): 785-798, 1997

      8 J. Rantung M. T Tran, "Determination of the fish surface area and volume using ellipsoid approximation method applied for image processing" 465 : 334-347, 2017

      9 T. H. Nguyen, "Control of a mobile picking robot for path tracking and object grasping using a 3D vision stereo camera" Pukyong National University 2016

      10 S. Zhu, "An image segmentation algorithm in image processing based on threshold segmentation" 673-678, 2007

      11 J. Fischer, "A feature descriptor for texture-less object representation using 2D and 3D cues from RGB-D data" 2112-2117, 2013

      12 Y. Sumi, "3D Object recognition in cluttered environments by segment-based stereo vision" 46 (46): 5-23, 2002

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-12-29 학회명변경 한글명 : 제어ㆍ로봇ㆍ시스템학회 -> 제어·로봇·시스템학회 KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-02 학술지명변경 한글명 : 제어.자동화.시스템공학 논문지 -> 제어.로봇.시스템학회 논문지
      외국어명 : Journal of Control, Automation and Systems Engineering -> Journal of Institute of Control, Robotics and Systems
      KCI등재
      2007-10-29 학회명변경 한글명 : 제어ㆍ자동화ㆍ시스템공학회 -> 제어ㆍ로봇ㆍ시스템학회
      영문명 : The Institute Of Control, Automation, And Systems Engineers, Korea -> Institute of Control, Robotics and Systems
      KCI등재
      2007-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2005-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2002-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.69 0.69 0.55
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.45 0.39 0.509 0.14
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