基于加权主成分分析的边连接

Kemal Özkan, Ş. Işık
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引用次数: 0

摘要

边缘检测是图像分割、压缩和配准等图像处理任务中一个复杂而棘手的研究领域。在这项研究中,我们提出了一种新的边缘连接方法,通过在不同类型的图像上应用PCA的概念来确定有吸引力的边缘段。为了利用角度信息确定方向,对被处理点周围的块进行主成分分析。具体来说,通过考虑最大和最小特征值对应的特征向量之间的夹角来考虑水平方向和垂直方向。通过对有噪声和无噪声图像的实验,我们发现该方法对噪声具有鲁棒性,能够很好地保留图像的结构,提取出定位良好的直线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weighted principal component analysis based edge linking
As a complicated and troublesome research area, the edge detection is a fundamental step in terms of some image processing tasks including segmentation, compression and registration. In this study, we present a new approach for edge linking by applying the concept of the PCA on different types of images to determine the attractive edge segments. To determine the direction by using the angle information, the PCA decomposition is carried out on the block around the processed point. Specifically, the horizontal and vertical directions are taken into account by considering the angle between the eigenvectors corresponding to the largest and smallest eigenvalues. After making some experiments on noisy and noise free images, we have observed that the proposed method is robust to noise, preserves the structure of image and extracts well-localized and straight lines.
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