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Adaptive RANSAC and extended region-growing algorithm for object recognition over remote-sensing images

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Abstract

In this paper, a new approach is proposed for object recognition in remote-sensing images. In the proposed approach, the matching process between the object in the template and test images is done based on Scale Invariant Feature Transform (SIFT). To decrease the false matches of SIFT, an adaptive Random sample consensus (RANSAC) algorithm is used. In the proposed RANSAC, the threshold value is calculated adaptively based on the mean and variance of the correct and false matched points. Finally, the exact object boundary is extracted using the extended region-growing algorithm. The proposed algorithm uses the correct matched points as multiple seed points instead of a single seed point. The proposed method is implemented in MATLAB, and compared with classic object detection methods. Simulation results confirm the superiority of the proposed method based on some evaluation criteria such as precision, correct detection ratio and false alarm rate.

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Notes

  1. Matching process is the acquisition of correspondence between features of images (two or more) of a scene taken under different imaging conditions (different times, different angles and sensors).

  2. Maximally Stable External Region (MSER)

  3. www.google.com/maps/@28.6206265,52.5681873,189 m/data

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Authors and Affiliations

  1. Department of Electrical Engineering, Sirjan Brach, Islamic Azad University, Kerman, Iran

    Zahra Hossein-Nejad

  2. Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

    Mehdi Nasri

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  1. Zahra Hossein-Nejad
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  2. Mehdi Nasri
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Correspondence to Mehdi Nasri.

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Appendix 1

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Table 4 List of Abbreviations
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Hossein-Nejad, Z., Nasri, M. Adaptive RANSAC and extended region-growing algorithm for object recognition over remote-sensing images. Multimed Tools Appl 81, 31685–31708 (2022). https://doi.org/10.1007/s11042-022-13021-9

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