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Image-matching framework based on region partitioning for target image location

Author

Listed:
  • Xiaomin Liu

    (Harbin Institute of Technology
    Jiamusi University)

  • Jun-Bao Li

    (Harbin Institute of Technology)

  • Jeng-Shyang Pan

    (Shandong University of Science and Technology
    Fujian University of Technology)

  • Shuo Wang

    (Harbin Institute of Technology)

  • Xudong Lv

    (Harbin Institute of Technology)

  • Shuanglong Cui

    (Harbin Institute of Technology)

Abstract

The target-location problems of observation and combat-integrated UAVs utilized in battles makes image matching challenging and of vital significance. This paper presents a framework of image matching based on region partitioning for target-image location, working on complex simulated aerial images consisting of, for example, scale-changing, rotation-changing, blurred, and occlusion images. Originally, an image-evaluation approach based on a weighted-orientation histogram was proposed to judge whether the image is an image with good texture or a textureless image. Two approaches based on layered architecture are employed for images with good texture and textureless images. In these two approaches, an improved SIFT image-matching algorithm incorporating detected Harris corners into the keypoint set is suggested, and Bhattacharyya distance based on an orientation histogram was employed to select the best result among different region pairs. Experiment results illustrated that the image-matching approach based on image segmentation has a much higher rate of 42.04 when compared to the traditional approach.

Suggested Citation

  • Xiaomin Liu & Jun-Bao Li & Jeng-Shyang Pan & Shuo Wang & Xudong Lv & Shuanglong Cui, 2020. "Image-matching framework based on region partitioning for target image location," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(3), pages 269-286, July.
    • Handle: RePEc:spr:telsys:v:74:y:2020:i:3:d:10.1007_s11235-020-00657-x
    DOI: 10.1007/s11235-020-00657-x
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    References listed on IDEAS

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    1. Lien, Gudbrand D. & Hardaker, J. Brian & Richardson, James W., 2006. "Simulating Multivariate Distributions with Sparse Data: A Kernal Density Smoothing Procedure," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25449, International Association of Agricultural Economists.
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