IEEE Trans Image Process. 2016 Feb;25(2):643-57. doi: 10.1109/TIP.2015.2507981. Epub 2015 Dec 11.
We propose a framework for the detection of junctions in images. Although the detection of edges and key points is a well examined and described area, the multiscale detection of junction centers, especially for odd orders, poses a challenge in pattern analysis. The goal of this paper is to build optimal junction detectors based on 2D steerable wavelets that are polar-separable in the Fourier domain. The approaches we develop are general and can be used for the detection of arbitrary symmetric and asymmetric junctions. The backbone of our construction is a multiscale pyramid with a radial wavelet function where the directional components are represented by circular harmonics and encoded in a shaping matrix. We are able to detect M -fold junctions in different scales and orientations. We provide experimental results on both simulated and real data to demonstrate the effectiveness of the algorithm.
我们提出了一种用于图像中拐点检测的框架。尽管边缘和关键点的检测是一个经过充分研究和描述的领域，但在模式分析中，多尺度检测拐点中心，特别是奇数阶的拐点中心，仍然具有挑战性。本文的目标是构建基于二维可旋转小波的最优拐点检测器，这些小波在傅里叶域中是极可分离的。我们提出的方法具有通用性，可以用于检测任意对称和非对称的拐点。我们的构建的核心是一个具有径向小波函数的多尺度金字塔，其中方向分量由圆谐函数表示，并编码在一个整形矩阵中。我们能够在不同的尺度和方向检测 M 重拐点。我们在模拟和真实数据上提供了实验结果，以验证算法的有效性。
