Rectangle detection based on Harris corner

ZHANG Cong-peng; WEI Xue-guang

doi:10.3788/OPE.20142208.2259

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Rectangle detection based on Harris corner

更新时间：2020-08-13

- Rectangle detection based on Harris corner
- Optics and Precision Engineering Vol. 22, Issue 8, Pages: 2259-2266(2014)
- 作者机构：
  
  北方工业大学机电工程学院北京,100144
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142208.2259
  CLC： TP391.4
- Received：13 November 2013，
  
  Revised：23 January 2014，
  
  Published：25 August 2014
- 稿件说明：
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张从鹏, 魏学光,. 基于Harris角点的矩形检测[J]. 光学精密工程, 2014,22(8): 2259-2266

ZHANG Cong-peng, WEI Xue-guang,. Rectangle detection based on Harris corner[J]. Editorial Office of Optics and Precision Engineering, 2014,22(8): 2259-2266
张从鹏, 魏学光,. 基于Harris角点的矩形检测[J]. 光学精密工程, 2014,22(8): 2259-2266 DOI： 10.3788/OPE.20142208.2259.

ZHANG Cong-peng, WEI Xue-guang,. Rectangle detection based on Harris corner[J]. Editorial Office of Optics and Precision Engineering, 2014,22(8): 2259-2266 DOI： 10.3788/OPE.20142208.2259.

摘要

为了快速精确地对矩形进行识别和检测，开发了一套图像采集系统，并提出了基于改进的Harris角点检测法的快速矩形识别算法。首先，只针对多种角点中的L型角点进行快速检测，并通过亚像素级后处理提高角点的位置精度。然后，根据得到的高精度角点位置信息，任意组合角点并遴选相互平行且长度相等的直线段组，从而匹配出相互垂直且四个角点重合的平行直线段；将其作为矩形的四条边，进而循环识别出图像中所有的矩形元素。提出了伪矩形图形元素的甄别判据，以提高算法的精确度和可靠性。实验结果表明：分别用基于Harris角点和基于Hough变换的矩形检测算法处理同一图片时，前者的运算速度为后者的9.5倍；其图像识别精度能达到亚像素级，最大误差为0.4 pixel。该算法满足工业应用中高实时性、高精度的要求，并且稳定性好，抗干扰能力强。

Abstract

To recognize and detect a rectangle rapidly and accurately

an image collection system was established and a rapid detection algorithm for rectangles was proposed based on Harris corner detection algorithm. First

the L-shaped corner points were selectively recognized from all kinds of corner points in the detecting image by improving the traditional Harris corner detection algorithm

and the position accuracy of corner points was promoted by sub-pixel post-processing. Then

according to the obtained high-precision angular position information

some parallel straight line segment pairs with equal length were grouped and the perpendicular parallel line segment pairs with four overlap corner points were matched

by which the four sides of a rectangle were obtained. Furthermore

all the rectangle elements were detected in the processed image. In order to improve the accuracy and reliability of the rectangular recognition algorithm

the identifying criterion of the pseudo rectangular graphic elements was provided. Finally

the sensor performance testing experiments were carried out. Experimental results indicate that the rectangular recognition speed of Harris corner point algorithm is 8.5 times faster than that of Hough algorithm

and the rectangular image recognition maximum position error is 0.4 pixel. The Harris corner rectangle detection method has strong anti-interference capability and stability and can meet the high real-time and precision detection requirements of industrial application.

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Keywords

references

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