Abstract
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.
Highlights
Feature detection and matching is one of the core areas of image processing in various applied fields, such as Visual based Simultaneously Localization and Mapping (V-SLAM), Structure from Motion (SfM), Augmented Reality (AR), general image retrieval, image mosaic, and image registration
In order to reduce the impact of distortion on the feature matching performance, we propose a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor based on the spherical perspective model, named Fisheye Spherical Distorted BRIEF (FSD-BRIEF)
In order to avoid the field of view (FoV) holes caused by the resampling approaches, and reduce the excessive distortion of descriptors in large FoV images, in this paper, we design a novel Fisheye Spherical Distorted BRIEF (FSD-BRIEF) descriptor, which is a distorted binary feature descriptor based on the spherical perspective model for fisheye images
Summary
Feature detection and matching is one of the core areas of image processing in various applied fields, such as Visual based Simultaneously Localization and Mapping (V-SLAM), Structure from Motion (SfM), Augmented Reality (AR), general image retrieval, image mosaic, and image registration. In order to reduce the impact of distortion on the feature matching performance, we propose a novel distorted BRIEF descriptor based on the spherical perspective model, named Fisheye Spherical Distorted BRIEF (FSD-BRIEF). We propose a method based on 3D gray centroid to determine the direction of each feature point in the spherical image. We build an attitude coordinate system of each feature point and propose a coordinate mapping method to project the BRIEF descriptor template on the fisheye image. A new method of determining the 3D gray centroid and the direction of feature points with pixel density function based on a spherical perspective model; 3. A novel descriptor template distortion method based on the spherical perspective model and the feature point attitude matrix.
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