A robust to outliers method for estimating the homography

Qi, Naixin; Sheng-xiu, Zhang; Cao, Lu; Yang, Xiaogang; Sun, Qiao

doi:10.1109/ccdc.2017.7978770

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…We employ Scale-Invariant Feature Transform (SIFT) on the base image and adjoining image to detect the key match points across the adjacent tiles. To select the robust key match points, we employ the RANSAC algorithm with the homography model [37], to avoid misleading points. The RANSAC method computes a homography and provides a prediction of outliers.…”

Section: Extraction Of Robust Key Match Points In Overlapping Regionsmentioning

confidence: 99%

Automatic Generation of Seamless Mosaics Using Invariant Features

et al. 2021

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The acquisition of satellite images over a wide area is often carried out across seasons because of satellite orbits and atmospheric conditions (e.g., cloud cover, dust, etc.). This results in spectral mismatch between adjacent scenes as the sun angle and the atmospheric conditions will be different for different acquisitions. In this work, we developed an approach to generate seamless mosaics using Scale-Invariant Features Transformation (SIFT). In this process, we make use of the overlapping areas between two adjacent scenes and then map spectral values of one imagery scene to another based on the filtered points detected by SIFT features to create a seamless mosaic. We make use of the Random Sample Consensus (RANSAC) method successively to filter out obtained SIFT points across adjacent tiles and to remove spectral outliers across each band of an image. Several high resolution satellite images acquired with WorldView-2 and Dubaisat-2 satellites, and medium resolution Sentinel-2 satellite imagery are used for experimentation. The experimental results show that the proposed approach can generate good seamless mosaics. Furthermore, Sentinel-2’s level 2A (L2A) product surface reflectance data is used to adjust the spectral values for color consistency.

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Section: Extraction Of Robust Key Match Points In Overlapping Regionsmentioning

confidence: 99%

Automatic Generation of Seamless Mosaics Using Invariant Features

et al. 2021

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Comparative Study: Outlier Elimination through Fundamental and Homography Matrices

Tumurbaatar,

Sengee,

Ochirbat

et al. 2024

J Multimed Inf Syst

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This paper presents a comparative study between two robust estimation approaches: homography matrix-based RANSAC and fundamental matrix-based RANSAC, for outlier elimination in various computer vision applications. The study focuses on the critical task of reliably estimating correspondences across two-view images. The Random Sample Consensus (RANSAC) algorithm is employed to estimate accurate homography and fundamental matrices robustly, even in the presence of outliers. Image datasets are utilized for experimental analysis, including rotations and translations of object. The performance of both methods is compared in terms of accuracy, robustness based on their geometric properties with the different test dataset. Experimental results demonstrate that the homography matrix-based RANSAC method works well with planar movements of the objects, while the fundamental matrix-based RANSAC method performs better with 3D movements of the objects. The paper concludes by discussing the implications of these findings and highlighting the suitability of each approach.

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A robust to outliers method for estimating the homography

Cited by 2 publications

References 11 publications

Automatic Generation of Seamless Mosaics Using Invariant Features

Automatic Generation of Seamless Mosaics Using Invariant Features

Comparative Study: Outlier Elimination through Fundamental and Homography Matrices

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