Zhang Meng, Jin Haiyan, Xiao Zhaolin, Guillemot Christine
IEEE Trans Image Process. 2022;31:6164-6174. doi: 10.1109/TIP.2022.3202099. Epub 2022 Sep 28.
Many computer vision applications rely on feature detection and description, hence the need for computationally efficient and robust 4D light field (LF) feature detectors and descriptors. In this paper, we propose a novel light field feature descriptor based on the Fourier disparity layer representation, for light field imaging applications. After the Harris feature detection in a scale-disparity space, the proposed feature descriptor is then extracted using a circular neighborhood rather than a square neighborhood. It is shown to yield more accurate feature matching, compared with the LiFF LF feature, with a lower computational complexity. In order to evaluate the feature matching performance with the proposed descriptor, we generated a synthetic stereo LF dataset with ground truth matching points. Experimental results with synthetic and real-world dataset show that our solution outperforms existing methods in terms of both feature detection robustness and feature matching accuracy.
许多计算机视觉应用都依赖于特征检测和描述，因此需要计算效率高且鲁棒的4D光场（LF）特征检测器和描述符。在本文中，我们针对光场成像应用提出了一种基于傅里叶视差层表示的新型光场特征描述符。在尺度-视差空间中进行哈里斯特征检测后，使用圆形邻域而非方形邻域提取所提出的特征描述符。与LiFF LF特征相比，它能产生更准确的特征匹配，且计算复杂度更低。为了评估所提出描述符的特征匹配性能，我们生成了一个带有真实匹配点的合成立体LF数据集。合成数据集和真实世界数据集的实验结果表明，我们的解决方案在特征检测鲁棒性和特征匹配准确性方面均优于现有方法。
