Blur-Countering Keypoint Detection via Eigenvalue Asymmetry

Chao Zhang,Takuya Akashi,Xuequan Lu

doi:10.1109/access.2020.3020561

Chao Zhang, Takuya Akashi + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3020561

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Abstract

Well-known corner or local extrema feature based detectors such as FAST and DoG have achieved noticeable successes. However, detecting keypoints in the presence of blur has remained to be an unresolved issue. As a matter of fact, various kinds of blur (e.g., motion blur, out-of-focus and space-variant) remarkably increase challenges for keypoint detection. As a result, those methods have limited performance. To settle this issue, we propose a blur-countering method for detecting valid keypoints for various types and degrees of blurred images. Specifically, we first present a distance metric for derivative distributions, which preserves the distinctiveness of patch pairs well under blur. We then model the asymmetry by utilizing the difference of squared eigenvalues based on the distance metric. To make it scale-robust, we also extend it to scale space. The proposed detector is efficient as the main computational cost is the square of derivatives at each pixel. Extensive visual and quantitative results show that our method outperforms current approaches under different types and degrees of blur. Without any parallelization, our implementation achieves real-time performance for low-resolution images (e.g., $320\times 240$ pixel).

Highlights

Keypoint detection, a fundamental technique in computer vision, has gained extensive attention in recent decades
We propose to measure the degree of asymmetry by averaging distances between derivative distributions radially to avoid the estimation of hypothetical axis of asymmetry
(3) In spite of the possible requirements for precise r during the procedure of feature descriptor extraction, we found in experiments that the discrete values (r = 20, 21, 22, . . .) are sufficient to make keypoints distinctive

Summary

Introduction

A fundamental technique in computer vision, has gained extensive attention in recent decades. It plays an important role in various applications such as image retrieval [1], [2], image stitching [3], [4], object recognition [5], [6] and so on. Most of the existing methods attempt to improve the robustness against photometric variations from two different aspects: methods with utilization of sharp features and data-driven approaches. These techniques are limited in the presence of image blur. A more popular way is to find local extremes over scale space generated by different sizes of

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