Rotation Estimation: A Closed-Form Solution Using Spherical Moments.

Hicham Hadj-Abdelkader,Houssem-Eddine Benseddik,Omar Tahri

doi:10.3390/s19224958

Abstract

Photometric moments are global descriptors of an image that can be used to recover motion information. This paper uses spherical photometric moments for a closed form estimation of 3D rotations from images. Since the used descriptors are global and not of the geometrical kind, they allow to avoid image processing as features extraction, matching, and tracking. The proposed scheme based on spherical projection can be used for the different vision sensors obeying the central unified model: conventional, fisheye, and catadioptric. Experimental results using both synthetic data and real images in different scenarios are provided to show the efficiency of the proposed method.

Highlights

Rotation estimation from images is important for many application as for motion estimation and registration in image processing [1], pattern recognition [2], localization and control of ground/aerial vehicles [3], and computer vision [4]
RAM, the whole process including the computation of spherical moments of an image of size 480 × 640 pixels until the rotation estimation takes around 20 ms
Compared to the classical method based on the alignment of principal axis, the proposed solution has no ambiguities on rotation formula

Summary

Introduction

Rotation estimation from images is important for many application as for motion estimation and registration in image processing [1], pattern recognition [2], localization and control of ground/aerial vehicles [3], and computer vision [4]. The first method type uses Epipolar geometry applied to geometrical features as as points [5], lines [6], or contours [7] to estimate the fundamental matrix (uncalibrated vision sensors), the essential matrix, or homography matrix for the calibrated case. Examples of such methods using conventional cameras are Agrawal et al [8] and Malis et al [9] and using omnidirectional vision sensors is [10,11,12]. Those methods are referred to in the literature as dense, direct, or global

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Conclusion