论文标题
基于梯度的特征从原始拜耳图像提取
Gradient-based Feature Extraction From Raw Bayer Pattern Images
论文作者
论文摘要
在本文中，研究了Demosaicing对梯度提取的影响，并提出了基于原始的拜耳模式图像的基于梯度的特征提取管道。理论上和实验上都表明，拜耳模式图像适用于基于中心差梯度的特征提取算法而没有性能降解，甚至在某些情况下具有出色的性能。拟议的基于拜耳模式的梯度提取管道中，应用了在各种演示算法中广泛使用的色差恒定性假设。实验结果表明，从拜耳模式图像中提取的梯度足够强大，可以用于基于基于基于的梯度（HOG）的人行道检测算法和基于偏移不变的特征变换（SIFT）基于匹配算法的直方图。
In this paper, the impact of demosaicing on gradient extraction is studied and a gradient-based feature extraction pipeline based on raw Bayer pattern images is proposed. It is shown both theoretically and experimentally that the Bayer pattern images are applicable to the central difference gradient-based feature extraction algorithms without performance degradation, or even with superior performance in some cases. The color difference constancy assumption, which is widely used in various demosaicing algorithms, is applied in the proposed Bayer pattern image-based gradient extraction pipeline. Experimental results show that the gradients extracted from Bayer pattern images are robust enough to be used in histogram of oriented gradients (HOG)-based pedestrian detection algorithms and shift-invariant feature transform (SIFT)-based matching algorithms.
