基于改进最大类间方差法的手势分割方法研究

李擎; 唐欢; 迟健男; 邢永跃; 李华通

doi:10.16383/j.aas.2017.c150862

基于改进最大类间方差法的手势分割方法研究

doi: 10.16383/j.aas.2017.c150862

李擎^1,,
唐欢^1,,
迟健男^1, ,,
邢永跃^1,,
李华通^1,

北京科技大学自动化学院北京 100083

基金项目:

北京市自然科学基金 4122050

详细信息

作者简介:
李擎北京科技大学自动化学院教授.主要研究方向为智能优化理论及其在路径规划、基于数据驱动建模中的应用.E-mail:liqing@ies.ustb.edu.cn

唐欢北京科技大学自动化学院硕士研究生.主要研究方向为图像处理与分析, 手势识别, 视线追踪.E-mail:tanghuanyl@126.com

邢永跃北京科技大学自动化学院硕士研究生.主要研究方向为机器学习, 图像处理与分析, 视线追踪, 计算机视觉.E-mail:xingyongyue@outlook.com

李华通北京科技大学自动化学院硕士研究生.主要研究方向为图像处理与分析, 视觉伺服, 计算机视觉.E-mail:lhuatong@126.com

通讯作者:
迟健男北京科技大学自动化学院副教授.主要研究方向为视线追踪, 人机交互, 车辆辅助驾驶, 嵌入式系统.E-mail:syjnchi@126.com

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出版历程
- 收稿日期: 2015-12-23
- 录用日期: 2016-04-01
- 刊出日期: 2017-04-20

Gesture Segmentation with Improved Maximum Between-clusterVariance Algorithm

LI Qing^1
,,
TANG Huan^1
,,
CHI Jian-Nan^{1
, ,},
XING Yong-Yue^1
,,
LI Hua-Tong^1
,

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083

Funds:

Natural Science Foundation of Beijing 4122050

More Information

Author Bio:
Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interest covers intelligent optimization theory and its application in path planning and data driven modeling

Master student at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interest covers image processing and analysis, gesture recognition and gaze tracking

Master student at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interest covers machine learning, image processing and analysis, gaze tracking and computer vision

Master student at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interest covers image processing and analysis, vision serving and computer vision

Corresponding author: CHI Jian-Nan Associate professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interest covers gaze tracking, human computer interaction, driver assistance system and embedded system. Corresponding author of this paper

摘要

摘要: 针对手势图像中由于噪声和成像干扰造成的手势模糊和边界不清晰的问题，提出了一种基于改进最大类间方差法的手势分割方法.首先建立手势图像的二维灰度直方图，在二维灰度直方图上确定噪声点位置，在原图的相应区域滤除噪声.然后重建二维灰度直方图将内点区的点集投影到45度线，得到投影灰度直方图.接下来在灰度投影直方图上采用全局Otsu确定局部Otsu的左边界，用高斯函数拟合得到局部Otsu右边界，最后采用局部Otsu分割手势.该方法可以有效地对手势图像进行精确分割，实验结果验证了本文算法的有效性.
- 手势分割 /
- 改进最大类间方差法 /
- 二维灰度直方图 /
- 投影灰度直方图
Abstract: In this paper, in order to solve the problem of ambiguity or unclear boundary caused by noise and interference in gesture imaging, a gesture segmentation method based on the improved maximum between-cluster variance algorithm is proposed. Firstly, a two-dimensional gray histogram of gesture image is generated, and positions of noise points are determined on the two-dimensional gray histogram. After filtering noise in the corresponding region of the gesture image, a two-dimensional gray histogram is reconstructed. The point set of the inner point area are projected to the 45 degrees line to generate the gray projection histogram. Then, the global Otsu is used to determine the left boundary of the local Otsu and Gauss function is used to get the right boundary of the local Otsu in the projection gray histogram. Finally, the local Otsu is used to segment the gesture image. This method can effectively segment the gesture image accurately. Experimental results have verified the effectiveness of the proposed algorithm.
- Gesture segment /
- improved Otsu /
- two-dimensional gray histogram /
- projection gray histogram
注释:

1) 本文责任编委贺威

HTML全文

图 1 待精确分割的手势图像

Fig. 1 Gesture images to be segmented accurately

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图 2 手势分割框架图

Fig. 2 Framework of gesture segmentation

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图 3 二维灰度直方图区域划分图

Fig. 3 Region division of two-dimensional gray histogram

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图 4 投影原理图

Fig. 4 Principle of projection

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图 5 投影灰度直方图

Fig. 5 Projection gray histogram

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图 6 实验1处理过程及结果图

Fig. 6 Processing and results of Experiment 1

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图 7 实验2处理过程及结果图

Fig. 7 Processing and results of Experiment 2

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图 8 实验3处理过程及结果图

Fig. 8 Processing and results of Experiment 3

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图 9 实验4处理结果图 (本文算法)

Fig. 9 Processing and results of Experiment 4 (Proposed algorithm)

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表 1 实验样本表

Table 1 Experimental samples

样本名称	样本特点	样本选择目的
实验1	不加噪声, 对比度较高, 边界清晰	验证本文算法对成像质量高的手势分割效果
实验2	不加噪声, 对比度一般, 边界模糊, 存在背景覆盖目标的情况	验证本文算法对边界模糊的手势分割效果
实验3	存在噪声, 对比度一般, 边界模糊, 存在背景覆盖目标的情况	验证本文算法对存在噪声且边界模糊的手势分割效果
实验4	不同个体的不同手势图像, 边界模糊, 存在背景覆盖目标的情况	验证本文算法对不同个体手势分割效果

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表 2 拟合曲线参数表 (本文算法)

Table 2 Parameters of fitting curve (Proposed algorithm)

实验	µ	σ
实验1	42	1.4487
实验2	60	6.7082
实验3	57	4.6043

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表 3 边界灰度表 (本文算法)

Table 3 Edge gray (Proposed algorithm)

实验	k	t₂
实验1	210	252
实验2	142	203
实验3	145	202

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表 4 阈值表 (三种算法)

Table 4 Threshold (Three algorithms)

实验	Otsu	肤色+ Otsu	本文算法
实验1	210	143	248
实验2	142	136	183
实验3	145	136	188

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表 5 不同方法处理的结果评价表

Table 5 Results evaluation of different algorithms

实验	评价指标	Otsu	肤色+ Otsu	本文算法
实验1	指尖	较好	较好	较好
实验1	轮廓	一般	一般	较好
实验1	时间 (ms)	2.75	3.21	31.25
实验2	指尖	较好	较差	较好
实验2	轮廓	一般	较差	较好
实验2	时间 (ms)	2.85	3.82	37.63
实验3	指尖	较差	较差	一般
实验3	轮廓	较差	较差	较好
实验3	抑噪	一般	较差	较好
实验3	时间 (ms)	2.99	3.67	39.50

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