吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (7): 1639-1644.doi: 10.13229/j.cnki.jdxbgxb20210672

• 计算机科学与技术 • 上一篇    

基于空间邻域信息的击剑连续动作图像分割

李娜1(),谭韶生2()   

  1. 1.吉林师范大学 体育学院,吉林 四平 136000
    2.中南大学 计算机学院,长沙 410083
  • 收稿日期:2021-07-16 出版日期:2022-07-01 发布日期:2022-08-08
  • 通讯作者: 谭韶生 E-mail:lina9992021@163.com;ddddaaa202106@163.com
  • 作者简介:李娜(1981-),女,教授.研究方向:体育教育训练.E-mail:lina9992021@163.com
  • 基金资助:
    2018年湖南省教育厅科研项目(18C1333)

Image segmentation of fencing continuous action based on spatial neighborhood information

Na LI1(),Shao-sheng TAN2()   

  1. 1.College of Physical Education,Jilin Normal University,Siping 136000,China
    2.School of Computer Science and Engineering,South Central University,Changsha 410083,China
  • Received:2021-07-16 Online:2022-07-01 Published:2022-08-08
  • Contact: Shao-sheng TAN E-mail:lina9992021@163.com;ddddaaa202106@163.com

摘要:

由于目前已有方法未能提取击剑连续动作图像的邻域特征,导致图像分割结果不理想、抗噪性能较差、分割运算时间增加,针对此问题,提出了一种基于空间邻域信息的击剑连续动作图像分割方法,对人体轮廓线特征点进行自动提取,将组建关于特征点的自适应邻域几何特征协方差矩阵设定为特征点描述子,对各个描述子进行相似性度量,提取击剑连续动作邻域特征。然后,通过邻域特征在目标函数中增加空间约束项函数,利用像素的先验概率设定空间邻域隶属函数,同时加入核函数,优化击剑连续动作图像特征;通过隶属函数修正得到新的加权隶属函数,加强邻域信息聚类比例,完成图像分割。最后,进行了实验测试,经过测试可知,本文方法能够获取理想的分割效果,同时还能够提升抗噪性能,降低分割运算时间。

关键词: 空间邻域信息, 击剑连续动作, 图像分割, 核函数

Abstract:

Because the existing methods fail to extract the neighborhood features of the fencing continuous action image, the image segmentation results are not ideal, the anti-noise performance is poor, and the segmentation operation time increases. A fencing continuous action image segmentation method based on spatial neighborhood information is proposed. The feature points of the body contour were extracted automatically, the adaptive neighborhood geometric feature covariance matrix about the feature points as feature point descriptors was set, the similarity of each descriptor was measured, and the neighborhood features of the continuous fencing action were extracted. Then, through the neighborhood feature, the spatial constraint term function was added to the objective function, the spatial neighborhood membership function was set using the prior probability of the pixel, and the kernel function was added at the same time to optimize the image characteristics of the fencing continuous action. A new weighted membership function was obtained through membership function modification, which strengthens the proportion of neighborhood information clustering and completes image segmentation. Finally, an experimental test was carried out. After testing, it is proved that the proposed method can obtain the ideal segmentation effect, and at the same time can improve the anti-noise performance and reduce the segmentation operation time.

Key words: spatial neighborhood information, continuous fencing action, image segmentation, kernel function

中图分类号: 

  • TP391.4

图1

基于空间邻域信息的击剑连续动作图像分割流程"

图2

不同方法对单人击剑图像的分割结果"

图3

不同方法对双人击剑图像的分割结果"

图4

抗噪性能对比结果"

表1

图像分割运算时间对比结果"

测试对象

编号

图像分割运算时间/s
本文方法文献[3]方法文献[4]方法
001505561
002455057
003606570
004120127134
005125130136
006202430
007364247
008475460
009566671
010747986
011808592
012637076
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