Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

Yong Li,Fang Chen,Junwei Zou,Xiang Shi,Lijun Wei

doi:10.3390/s150715595

Abstract

This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment.

Highlights

Keypoint and descriptor techniques have been widely applied in computer vision or pattern recognition
One method is to utilize the center-of-mass (COM), which is suitable for corners, and the other one is to utilize the histogram of intensities (HOI)
Due to the lack of main orientation, the keypoint matches built with the edge-oriented histogram (EOH) contain very few or no correct matches

Summary

Introduction

Keypoint and descriptor techniques have been widely applied in computer vision or pattern recognition. Applications include stereo vision, 3D scene reconstruction, human activity recognition, etc. Keypoints are often matched by computing the distance of their associated descriptors. The matching ability of descriptors is measured with the repeatability and distinctiveness, and in practice, a trade-off is often made between them. SIFT [1] and its variants with post-processing techniques (e.g., RANSAC) have witnessed many successful applications. On multi-sensor (multispectral) images, SIFT descriptors generate few correct mappings. The edge-oriented histogram (EOH) [2] was proposed, which utilizes only edge points and five bins for computing descriptors. EOH has a better matching performance on multispectral images than SIFT, but does not assign a main orientation to keypoints, which limits its application to images containing translation misalignment

Methods

Results

Conclusion