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      다국어 초록 (Multilingual Abstract)

      Recently, the mobile industries grow up rapidly and their performances are improved. So the usage of mobile devices is increasing in our life. Also mobile devices equipped with a high-performance camera, so the image stitching can carry out on the mobile devices instead of the desktop. However the mobile devices have limited hardware to perform the image stitching which has a lot of computational complexity. In this paper, we have proposed improved feature descriptor extraction and matching method for efficient image stitching on mobile environment. Our method can reduce computational complexity using extension of orientation window and reduction of dimension feature descriptor when feature descriptor is generated. In addition, the computational complexity of image stitching is reduced through the classification of matching points. In our results, our method makes to improve the computational time of image stitching than the previous method. Therefore our method is suitable for the mobile environment and also that method can make natural-looking stitched image.
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      Recently, the mobile industries grow up rapidly and their performances are improved. So the usage of mobile devices is increasing in our life. Also mobile devices equipped with a high-performance camera, so the image stitching can carry out on the mob...

      Recently, the mobile industries grow up rapidly and their performances are improved. So the usage of mobile devices is increasing in our life. Also mobile devices equipped with a high-performance camera, so the image stitching can carry out on the mobile devices instead of the desktop. However the mobile devices have limited hardware to perform the image stitching which has a lot of computational complexity. In this paper, we have proposed improved feature descriptor extraction and matching method for efficient image stitching on mobile environment. Our method can reduce computational complexity using extension of orientation window and reduction of dimension feature descriptor when feature descriptor is generated. In addition, the computational complexity of image stitching is reduced through the classification of matching points. In our results, our method makes to improve the computational time of image stitching than the previous method. Therefore our method is suitable for the mobile environment and also that method can make natural-looking stitched image.

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      참고문헌 (Reference)

      1 H. Bay, "SURF: Speeded Up Robust Features" 110 (110): 346-359, 2008

      2 D. Chen, "Robust Image Retrieval Using Multiview Scalable Vocabulary Trees" 7257 : 72570-, 2009

      3 P. Viola, "Rapid Object Detection Using a Boosted Cascade of Simple Feature" 1 : 511-518, 2001

      4 Y. Ke, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors" 511-517, 2004

      5 G. Takacs, "Outdoors Augmented Reality on Mobile Phone Using Loxel-Bbased Visual Feature Organization" 427-434, 2008

      6 M. Brown, "Invariant Features from Interest Point Groups" 2-5, 2002

      7 P. F. McLauchlan, "Image Mosaicing Using Sequential Bundle Adjustment" 20 (20): 751-759, 2002

      8 "Image Data Set"

      9 R. Szeliski, "Image Alignment and Stitching: A Tutorial" Microsoft Research 2004

      10 R. Szeliski, "Image Alignment and Stitching: A Tutorial" Preliminary draft 2005

      1 H. Bay, "SURF: Speeded Up Robust Features" 110 (110): 346-359, 2008

      2 D. Chen, "Robust Image Retrieval Using Multiview Scalable Vocabulary Trees" 7257 : 72570-, 2009

      3 P. Viola, "Rapid Object Detection Using a Boosted Cascade of Simple Feature" 1 : 511-518, 2001

      4 Y. Ke, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors" 511-517, 2004

      5 G. Takacs, "Outdoors Augmented Reality on Mobile Phone Using Loxel-Bbased Visual Feature Organization" 427-434, 2008

      6 M. Brown, "Invariant Features from Interest Point Groups" 2-5, 2002

      7 P. F. McLauchlan, "Image Mosaicing Using Sequential Bundle Adjustment" 20 (20): 751-759, 2002

      8 "Image Data Set"

      9 R. Szeliski, "Image Alignment and Stitching: A Tutorial" Microsoft Research 2004

      10 R. Szeliski, "Image Alignment and Stitching: A Tutorial" Preliminary draft 2005

      11 Y. Xiong, "Gradient Domain Iimage Blending and Implementation on Mobile Devices" MobiCase 2009

      12 J. P. Snyder, "Geometry of Mapping Satellite" 48 (48): 1593-1602, 1982

      13 Y. Xiong, "Fast Image Stitching and Editing for Panorama Painting on Mobile Phones" 298-306, 2010

      14 S. Rhee, "Efficient Image Stitching Using Fast Feature Descriptor Extraction and Matching" 2 (2): 65-70, 2013

      15 W. Chen, "Efficient Extraction of Robust Image Features on Mobile Devices" 1-2, 2007

      16 T. Schenk, "Digital photogrammetry, vol. 1" 1999

      17 C. Tang, "Automatic Registration based on Improved SIFT for Medical Microscopic Sequence Images" 1 : 580-583, 2008

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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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