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A Light Field FDL-HCGH Feature in Scale-Disparity Space
IEEE Transactions on Image Processing ( IF 10.6 ) Pub Date : 2022-09-19 , DOI: 10.1109/tip.2022.3202099
Meng Zhang , Haiyan Jin , Zhaolin Xiao , Christine Guillemot

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.

中文翻译:

尺度视差空间中的光场 FDL-HCGH 特征

许多计算机视觉应用依赖于特征检测和描述,因此需要计算高效且鲁棒的 4D 光场 (LF) 特征检测器和描述符。在本文中,我们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,用于光场成像应用。在尺度视差空间中进行哈里斯特征检测之后,然后使用圆形邻域而不是方形邻域来提取所提出的特征描述符。与 LiFF LF 特征相比,它显示出更准确的特征匹配,计算复杂度更低。为了使用所提出的描述符评估特征匹配性能,我们生成了一个具有地面实况匹配点的合成立体 LF 数据集。
更新日期:2022-09-19
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