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A Novel Multiscale Edge Detection Approach Based on Nonsubsampled Contourlet Transform and Edge Tracking

机译:基于非下采样Contourlet变换和边缘跟踪的多尺度边缘检测新方法

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摘要

Edge detection is a fundamental task in many computer vision applications. In this paper, we propose a novel multiscale edge detection approach based on the nonsubsampled contourlet transform (NSCT): a fully shift-invariant, multiscale, and multidirection transform. Indeed, unlike traditional wavelets, contourlets have the ability to fully capture directional and other geometrical features for images with edges. Firstly, compute the NSCT of the input image. Secondly, the. K-means clustering algorithm is applied to each level of the NSCT for distinguishing noises from edges. Thirdly, we select the edge point candidates of the input image by identifying the NSCT modulus maximum at each scale. Finally, the edge tracking algorithm from coarser to finer is proposed to improve robustness against spurious responses and accuracy in the location of the edges. Experimental results show that the proposed method achieves better edge detection performance compared with the typical methods. Furthermore, the proposed method also works well for noisy images.
机译:边缘检测是许多计算机视觉应用程序中的一项基本任务。在本文中,我们提出了一种基于非下采样轮廓波变换(NSCT)的新颖的多尺度边缘检测方法:完全位移不变,多尺度和多方向变换。实际上,与传统的小波不同,Contourlet具有完全捕获具有边缘的图像的方向性和其他几何特征的能力。首先,计算输入图像的NSCT。其次,。将K均值聚类算法应用于NSCT的每个级别,以区分边缘的噪声。第三,我们通过识别每个尺度上的最大NSCT模量来选择输入图像的边缘点候选。最后,提出了从粗到细的边缘跟踪算法,以提高针对杂散响应的鲁棒性和边缘位置的准确性。实验结果表明,与传统方法相比,该方法具有更好的边缘检测性能。此外,所提出的方法也适用于嘈杂的图像。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第2期|504725.1-504725.14|共14页
  • 作者单位

    Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450000, Peoples R China.;

    Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450000, Peoples R China.;

    Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450000, Peoples R China.;

    Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450000, Peoples R China.;

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