An End to End Framework to High Performance Geometry-Aware Multi-Scale Keypoint Detection and Matching in Fisheye Imag

Pourian, Niloufar; Nestares, Oscar

doi:10.1109/icip.2019.8803707

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Most recently, Pourian et al [ 27 ] proposed an end-to-end framework to enhance the precision of the descriptor matching between multiple wide-angle images. In their work, the global matching and the local matching of descriptors are combined in three stages.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, the descriptors are distorted excessively near the edge of the fisheye image since it is distorted based on the plane perspective model. Most recently, Pourian et al [27] proposed an end-to-end framework to enhance the precision of the descriptor matching between multiple wide-angle images. In their work, the global matching and the local matching of descriptors are combined in three stages.…”

Section: Related Workmentioning

confidence: 99%

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FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Zhang

Song

Ding

et al. 2021

Sensors

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Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Zhang

Song

Ding

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…A deep architecture was developed in [32] to learn to find good correspondences for multiple types of images, but the recall rate was low in severely distorted scenes. An end-to-end framework was introduced in [33] with the aim to enhance both precision and recall in the fisheye image matching process, but they transformed the fisheye image to a rectilinear perspective image to remove the radial distortion, which will lead to the loss of image information. To develop learning-based methods for fisheye image matching, the key lies in how to model the distortion in a deep neural network, but there is no representative method at present.…”

Section: Related Workmentioning

confidence: 99%

Spherically Optimized RANSAC Aided by an IMU for Fisheye Image Matching

Liang

Chen

et al. 2021

Remote Sensing

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Fisheye cameras are widely used in visual localization due to the advantage of the wide field of view. However, the severe distortion in fisheye images lead to feature matching difficulties. This paper proposes an IMU-assisted fisheye image matching method called spherically optimized random sample consensus (So-RANSAC). We converted the putative correspondences into fisheye spherical coordinates and then used an inertial measurement unit (IMU) to provide relative rotation angles to assist fisheye image epipolar constraints and improve the accuracy of pose estimation and mismatch removal. To verify the performance of So-RANSAC, experiments were performed on fisheye images of urban drainage pipes and public data sets. The experimental results showed that So-RANSAC can effectively improve the mismatch removal accuracy, and its performance was superior to the commonly used fisheye image matching methods in various experimental scenarios.

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Guided Sparse Feature Matching Via Coarsely Defined Dense Matches

Pourian

Nestares

2020

2020 IEEE International Conference on Image Processing (ICIP)

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An End to End Framework to High Performance Geometry-Aware Multi-Scale Keypoint Detection and Matching in Fisheye Imag

Cited by 3 publications

References 17 publications

FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Spherically Optimized RANSAC Aided by an IMU for Fisheye Image Matching

Guided Sparse Feature Matching Via Coarsely Defined Dense Matches

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