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Improved SIFT matching algorithm for 3D reconstruction from endoscopic images

Published: 11 December 2011 Publication History

Abstract

SIFT (Scale Invariant Feature Transform) is wildly used in image matching but suffered from low matching pairs when employed in endoscope image. This paper presented an improved algorithm based on SIFT, the core contribution of which is Zone Matching approach. By collecting all feature points in a neighbor patch around the coordinate of an objective feature in the key image and finding the closest one to the feature in current image, the Zone Matching can obtain more matching pairs in shorter time. The experiment result shows a good improvement on the matching results both in matching number and computing time.

References

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Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 90--110.
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Mikolajczyk, K., and Scmid, C. 2004. Scale and affine invariant interest point detectors. International Journal of Computer Vision 60, 438--469.
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Morel, J. M., and Yu, G. 2009. Asift: A new framework for fully affine invariant image comparison. Imaging Sciences 2, 438--469.
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Rahul, Y. K., and Sukthankar. 2004. A more distinctive representation for local image descriptors. IEEE Computer Vision and Pattern Recognition 2, 506.
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Tang, C.-Y., Wu, Y.-L., Hor, M.-K., and Wang, W.-H. 2008. Modified sift descriptor for image matching under interference. IEEE Machine Learning and Cybernetics 27, 3294--3300.

Cited By

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  • (2023)Self-supervised endoscopic image key-points matchingExpert Systems with Applications10.1016/j.eswa.2022.118696213(118696)Online publication date: Mar-2023
  • (2023)Learning-based keypoint registration for fetoscopic mosaickingInternational Journal of Computer Assisted Radiology and Surgery10.1007/s11548-023-03025-719:3(481-492)Online publication date: 9-Dec-2023
  • (2022)A Comparative Evaluation of Feature Detectors and Descriptors for Monocular Endoscopic Sparse Depth Reconstruction2022 The 5th International Conference on Control and Computer Vision10.1145/3561613.3561624(63-69)Online publication date: 9-Nov-2022
  • Show More Cited By

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  1. Improved SIFT matching algorithm for 3D reconstruction from endoscopic images

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    cover image ACM Conferences
    VRCAI '11: Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
    December 2011
    617 pages
    ISBN:9781450310604
    DOI:10.1145/2087756
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 11 December 2011

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    Author Tags

    1. 3D reconstruction
    2. endoscopy
    3. feature matching
    4. improved SIFT

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    Overall Acceptance Rate 51 of 107 submissions, 48%

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    Cited By

    View all
    • (2023)Self-supervised endoscopic image key-points matchingExpert Systems with Applications10.1016/j.eswa.2022.118696213(118696)Online publication date: Mar-2023
    • (2023)Learning-based keypoint registration for fetoscopic mosaickingInternational Journal of Computer Assisted Radiology and Surgery10.1007/s11548-023-03025-719:3(481-492)Online publication date: 9-Dec-2023
    • (2022)A Comparative Evaluation of Feature Detectors and Descriptors for Monocular Endoscopic Sparse Depth Reconstruction2022 The 5th International Conference on Control and Computer Vision10.1145/3561613.3561624(63-69)Online publication date: 9-Nov-2022
    • (2020)Endoscopic image feature matching via motion consensus and global bilateral regressionComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2020.105370190(105370)Online publication date: Jul-2020
    • (2017)Feature-point extraction based on an improved SIFT algorithm2017 17th International Conference on Control, Automation and Systems (ICCAS)10.23919/ICCAS.2017.8204463(345-350)Online publication date: Oct-2017
    • (2016)A statistical approach to rank the matched image points2016 24th Iranian Conference on Electrical Engineering (ICEE)10.1109/IranianCEE.2016.7585706(1214-1218)Online publication date: May-2016
    • (2016)Automatic GrabCut based lung extraction from endoscopic images with an initial boundary2016 IEEE 13th International Conference on Signal Processing (ICSP)10.1109/ICSP.2016.7878051(1374-1378)Online publication date: Nov-2016
    • (2015)Fuzzy zoning for feature matching technique in 3D reconstruction of nasal endoscopic imagesComputers in Biology and Medicine10.1016/j.compbiomed.2015.09.02167(83-94)Online publication date: Dec-2015
    • (2014)A log-ratio pair approach to endoscopic image matching2014 IEEE Workshop on Statistical Signal Processing (SSP)10.1109/SSP.2014.6884606(185-188)Online publication date: Jun-2014
    • (2012)Optical-Tracker-Based 3D Reconstruction for Endoscopic EnvironmentApplied Mechanics and Materials10.4028/www.scientific.net/AMM.249-250.1277249-250(1277-1282)Online publication date: Dec-2012

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