使用惯性矩的直线检测

C. Beumier
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引用次数: 7

摘要

直线检测是许多模式识别应用中图像分析的基本步骤,特别是在寻找人造物体时。像点或弧一样,直线段是用于对象识别的高级表示的基元。提出了一种检测直线度最小直线的新方法。它包括一个点连接程序,验证由线段点的转动惯量测量的直线度约束。如果扩大的线段具有有限的惯性矩,则在线段上添加一个邻近点。通过对实现进行计算优化,尽快放弃不合适的段,并通过增量方案将惯性计算的代价限制在当前点。直线检测算法具有通用性,包含直观的直线度参数,以像素为单位表示。它被应用于几种类型的图像,这里展示了在EuroSDR挑战框架下对卫星图像中的道路进行检测,以及对印刷电路板的导电条纹进行检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Straight-Line Detection Using Moment of Inertia
Straight-line detection is a basic step of image analysis for many pattern recognition applications, especially when looking for man made objects. Like points or arcs, straight line segments are primitives on which to base a higher-level representation for object recognition. We propose a novel method for the detection of lines with minimum straightness. It consists of a point linking procedure verifying a straightness constraint measured by the moment of inertia of the segment points. A neighbor point is added to the segment if the enlarged segment has a limited moment of inertia. The implementation was computationally optimized to abandon inappropriate segments as soon as possible and to limit the cost of inertia computation to the current point thanks to an incremental scheme. The straight-line detection algorithm is general purpose and contains an intuitive straightness parameter expressed in pixel. It was applied to several types of images and is demonstrated here for the detection of roads in satellite images in the framework of the EuroSDR challenge and for the detection of conducting stripes of printed circuit board.
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