Scale Invariant and Noise Robust Interest Points With Shearlets
Scale Invariant and Noise Robust Interest Points With Shearlets
复制标题
具有剪切波的尺度不变性和噪声鲁棒兴趣点
DOI：
10.1109/tip.2017.2687122
复制
发表时间：
2016
影响因子：
10.6
通讯作者：
E. De Vito
中科院分区：
文献类型：
--
作者：
Miguel A. Duval;Nicoletta Noceti;F. Odone;E. De Vito
Searlets是一种相对较新的定向多尺度信号分析框架，已被证明即使在存在大量噪声的情况下也能有效地增强信号的不连续性，例如在多个尺度上的边缘和角落。在本文中，我们考虑了shearlets框架中的类BLOB特性。我们推导了一种对斑点检测非常有效的度量，并在此度量的基础上提出了一种斑点检测器和关键点描述，它们的结合性能优于现有的含噪声和压缩图像的算法。我们还证明了在连续情况下，该度量满足理想尺度不变性。我们评估了我们的算法对不同类型的噪声的稳健性，包括模糊、压缩伪影和高斯噪声。此外，我们还对基准数据进行了比较分析，特别是对噪声容忍度和图像压缩。
Shearlets are a relatively new directional multi-scale framework for signal analysis, which have been shown effective to enhance signal discontinuities, such as edges and corners at multiple scales even in the presence of a large quantity of noise. In this paper, we consider blob-like features in the shearlets framework. We derive a measure, which is very effective for blob detection, and, based on this measure, we propose a blob detector and a keypoint description, whose combination outperforms the state-of-the-art algorithms with noisy and compressed images. We also demonstrate that the measure satisfies the perfect scale invariance property in the continuous case. We evaluate the robustness of our algorithm to different types of noise, including blur, compression artifacts, and Gaussian noise. Furthermore, we carry on a comparative analysis on benchmark data, referring, in particular, to tolerance to noise and image compression.