BALF: Simple and Efficient Blur Aware Local Feature Detector

• URL: http://arxiv.org/abs/2211.14731v2
• Date: Tue, 29 Nov 2022 06:29:41 GMT
• Title: BALF: Simple and Efficient Blur Aware Local Feature Detector
• Authors: Zhenjun Zhao and Yu Zhai and Ben M. Chen and Peidong Liu
• Abstract summary: Local feature detection is a key ingredient of many image processing and computer vision applications. We propose a simple yet both efficient and effective keypoint detection method that is able to accurately localize the salient keypoints in a blurred image. Our method takes advantages of a novel multi-layer perceptron (MLP) based architecture that significantly improve the detection repeatability for a blurred image.
• Score: 14.044093492945334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Local feature detection is a key ingredient of many image processing and computer vision applications, such as visual odometry and localization. Most existing algorithms focus on feature detection from a sharp image. They would thus have degraded performance once the image is blurred, which could happen easily under low-lighting conditions. To address this issue, we propose a simple yet both efficient and effective keypoint detection method that is able to accurately localize the salient keypoints in a blurred image. Our method takes advantages of a novel multi-layer perceptron (MLP) based architecture that significantly improve the detection repeatability for a blurred image. The network is also light-weight and able to run in real-time, which enables its deployment for time-constrained applications. Extensive experimental results demonstrate that our detector is able to improve the detection repeatability with blurred images, while keeping comparable performance as existing state-of-the-art detectors for sharp images.

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