Correlating multiple redundant scales for corner detection

I. C. de Paula, F. Medeiros, G.A. Mendonca, Cornélia J. P. Passarinho, Isaura N. S. Oliveira
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引用次数: 2

Abstract

Corner detection is an important task in computer vision and image processing applications. Basically, corners are high curvature points (HCP), which can be detected by contour analysis. In this paper we propose an approach to detect corners using multiscale analysis. The algorithm provides an undecimated wavelet decomposition of the angulation signal of a shape contour and the high curvature points are identified by correlating multiple redundant scales. The goal is to detect the dominant points of a shape that accurately represent it. Assessment results have shown that the method succeeded in reconstructing the shape contour using the detected HCPs. A novel evaluation measure is also presented in order to confirm that the proposed algorithm outperforms other methods used for testing and comparison purposes. The technique is promising and effective for image retrieval applications.
关联多个冗余尺度进行角点检测
角点检测是计算机视觉和图像处理应用中的一项重要任务。基本上,拐角是高曲率点(HCP),可以通过轮廓分析来检测。本文提出了一种基于多尺度分析的角点检测方法。该算法对形状轮廓的角度信号进行无损小波分解,并通过关联多个冗余尺度来识别高曲率点。目标是检测一个形状的主要点,准确地表示它。评估结果表明,该方法成功地利用检测到的hcp重建了形状轮廓。为了确认所提出的算法优于用于测试和比较目的的其他方法,还提出了一种新的评估度量。该技术在图像检索中具有良好的应用前景和效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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