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基于Shi-Tomasi角点验证的线段提取算法优化方法

骆开庆 邓军灿 蔡伟博 周育滨 张健

骆开庆, 邓军灿, 蔡伟博, 周育滨, 张健. 基于Shi-Tomasi角点验证的线段提取算法优化方法[J]. 华南师范大学学报(自然科学版), 2022, 54(1): 113-121. doi: 10.6054/j.jscnun.2022016
引用本文: 骆开庆, 邓军灿, 蔡伟博, 周育滨, 张健. 基于Shi-Tomasi角点验证的线段提取算法优化方法[J]. 华南师范大学学报(自然科学版), 2022, 54(1): 113-121. doi: 10.6054/j.jscnun.2022016
LUO Kaiqing, DENG Juncan, CAI Weibo, ZHOU Yubin, ZHANG Jian. Line Segment Extraction Algorithm Optimization Based on Shi-Tomasi Corner Detector[J]. Journal of South China normal University (Natural Science Edition), 2022, 54(1): 113-121. doi: 10.6054/j.jscnun.2022016
Citation: LUO Kaiqing, DENG Juncan, CAI Weibo, ZHOU Yubin, ZHANG Jian. Line Segment Extraction Algorithm Optimization Based on Shi-Tomasi Corner Detector[J]. Journal of South China normal University (Natural Science Edition), 2022, 54(1): 113-121. doi: 10.6054/j.jscnun.2022016

基于Shi-Tomasi角点验证的线段提取算法优化方法

doi: 10.6054/j.jscnun.2022016
基金项目: 

国家自然科学基金委员会-广东大数据科学中心项目 U1911401

华南师范大学大学生创新创业训练项目 202010574050

华南师范大学大学生创新创业训练项目 202110574048

详细信息
    通讯作者:

    张健,Email: jianzhang@m.scnu.edu.cn

  • 中图分类号: TP391.4

Line Segment Extraction Algorithm Optimization Based on Shi-Tomasi Corner Detector

  • 摘要: 针对现有的线段提取算法在图像中的天空、阴影、玻璃以及地板等模糊区域提取出较多的无意义线段的问题,提出了一种基于Shi-Tomasi角点验证的线段提取算法优化方法(ST-Lines算法):首先,使用经典线段提取算法进行线段提取;然后,采用Shi-Tomasi角点检测算法提取角点,并利用滑动窗口对所得的角点进行非极大值抑制;最后,根据线段长度、线段端点圆形框内的角点分布情况以及K最近邻算法对每条线段进行有无意义验证,尽可能多地剔除无意义线段。并利用YorkUrban线段数据集,对ST-Lines算法与原线段提取方法进行测试对比。对比结果表明:ST-Lines算法在平均准确率、F-score、平均线段长度上有所提高,且降低了平均线段数量。
  • 图  1  Shi-Tomasi算法中窗口滑动时灰度值变化较大的方向

    Figure  1.  The gray value changes of sliding windows in each direction in the Shi-Tomasi algorithm

    图  2  通过滑动窗口对角点进行非极大值抑制

    Figure  2.  The non-maximum suppression of corners with the sliding window

    图  3  部分特征模糊区域的线段和角点分布

    Figure  3.  The distribution of line segments and corners in blurred areas

    图  4  线段验证总流程图

    Figure  4.  The block diagram of validation of line segments

    图  5  线段端点圆形框内角点统计方法

    Figure  5.  The corner statistics in circles

    图  6  KNN算法验证方法

    Figure  6.  The validation of the KNN algorithm

    图  7  无意义线段剔除效果

    Figure  7.  The result of removing meaningless line segments

    图  8  数据集中每张图片的结果对比

    Figure  8.  The comparison of the results of all images in the dataset

    图  9  4种算法的室内外场景结果

    Figure  9.  The experimental results of indoor and outdoor scenes with four algorithms

    图  10  不同λarea 下4种算法的F-score的变化曲线

    Figure  10.  The change curves of F-score of four algorithms under different λarea

    表  1  6种算法在数据集的平均结果对比

    Table  1.   The comparison of average results of 6 algorithms in the dataset

    算法 AP AR F-score NUM LENGTH FPS
    EDLines 0.149 1 0.530 0 0.232 8 953.323 5 26.275 9 36.428 5
    LSD 0.167 7 0.432 1 0.241 6 757.578 4 25.173 6 15.887 8
    LSM 0.146 9 0.319 5 0.201 2 690.951 0 28.300 0 0.257 7
    Linelet 0.159 1 0.507 0 0.242 2 940.656 9 23.028 7 0.037 7
    EDLines+Corner 0.178 9 0.512 1 0.265 1 699.509 8 30.485 8 29.059 8
    LSD+Corner 0.181 4 0.409 2 0.251 3 608.156 9 27.609 2 13.298 5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-12
  • 刊出日期:  2022-02-25

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