Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel). 2020 Nov 19;20(22):6630. doi: 10.3390/s20226630.
Feature point detection is the basis of computer vision, and the detection methods with geometric invariance and illumination invariance are the key and difficult problem in the field of feature detection. This paper proposes an illumination-invariant feature point detection method based on neighborhood information. The method can be summarized into two steps. Firstly, the feature points are divided into eight types according to the number of connected neighbors. Secondly, each type of feature points is classified again according to the position distribution of neighboring pixels. The theoretical deduction proves that the proposed method has lower computational complexity than other methods. The experimental results indicate that, when the photometric variation of the two images is very large, the feature-based detection methods are usually inferior, while the learning-based detection methods performs better. However, our method performs better than the learning-based detection method in terms of the number of feature points, the number of matching points, and the repeatability rate stability. The experimental results demonstrate that the proposed method has the best illumination robustness among state-of-the-art feature detection methods.
特征点检测是计算机视觉的基础，具有几何不变性和光照不变性的检测方法是特征检测领域的关键和难点。本文提出了一种基于邻域信息的光照不变特征点检测方法。该方法可以概括为两步。首先，根据连接邻居的数量将特征点分为八类。其次，根据相邻像素的位置分布再次对每类特征点进行分类。理论推导证明，与其他方法相比，所提出的方法具有更低的计算复杂度。实验结果表明，当两幅图像的光照变化非常大时，基于特征的检测方法通常表现不佳，而基于学习的检测方法表现更好。然而，在特征点数量、匹配点数量和重复性稳定性方面，我们的方法优于基于学习的检测方法。实验结果表明，在所提出的特征检测方法中，该方法具有最好的光照鲁棒性。
