[1]吴涛,董肖莉,孟伟,等.基于语义分割的简洁线条肖像画生成方法[J].智能系统学报,2021,16(1):134-141.[doi:10.11992/tis.202101003]
 WU Tao,DONG Xiaoli,MENG Wei,et al.Concise line portrait generation method based on semantic segmentation[J].CAAI Transactions on Intelligent Systems,2021,16(1):134-141.[doi:10.11992/tis.202101003]
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基于语义分割的简洁线条肖像画生成方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年1期
页码:
134-141
栏目:
学术论文—人工智能基础
出版日期:
2021-01-05

文章信息/Info

Title:
Concise line portrait generation method based on semantic segmentation
作者:
吴涛12 董肖莉3 孟伟12 徐健3 覃鸿34 李卫军34
1. 北京林业大学 信息学院,北京 100083;
2. 国家林业草原林业智能信息处理工程技术研究中心,北京 100083;
3. 中国科学院 半导体研究所, 北京 100083;
4. 中国科学院大学 微电子学院,北京 100045
Author(s):
WU Tao12 DONG Xiaoli3 MENG Wei12 XU Jian3 QIN Hong34 LI Weijun34
1. School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China;
2. Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 1000
关键词:
语义分割区域轮廓 切向流场简洁线条肖像画人脸线条提取基于流的高斯差分参数调整
Keywords:
semantic segmentationregion contour edge tangent flowconcise line portraitface line extractionfdogparameter adjustment
分类号:
TP391
DOI:
10.11992/tis.202101003
摘要:
针对目前主流的线条提取算法对于区域对比度不明显的边缘的检测能力较弱,且对于所有区域采用无差别、统一化的处理策略,所生成的线条画往往较复杂,非常不利于机器人机械臂绘图的问题,本文提出了一种基于语义分割的简洁线条肖像画生成方法(concise line portrait generation based on semantic segmentation, CLPG-SS)。首先,对人脸图像进行语义分割,将人脸划分为不同的区域,基于不同区域提取边缘轮廓与五官细节线条,进行边缘切向流优化,从而加强方向信息;在此基础上,利用线条图来生成调和图像,并利用优化后的边缘切向流、人脸语义分割结果以及调和图像,针对不同的分割区域调整线条提取方法的参数,实现对细节无关区域的线条过滤和细节重点区域的线条加强,生成简洁线条肖像画。实验结果表明:本文提出的CLPG-SS方法能够有效提取人脸主轮廓线条,并针对不同区域实现了对细节线条的针对性调节,提高了机器人机械臂的绘制效率。
Abstract:
Currently, mainstream line extraction algorithms have weak detection capabilities for edges with inconspicuous regional contrast, and they use an undifferentiated and unified processing strategy for all regions. The generated line drawings are often complex, which is very unfavorable for robot manipulator drawing. Given this situation, this paper proposes a concise line portrait generation based on the semantic segmentation (CLPG-SS) method. In this method, semantic segmentation is performed on the face image, and the face is divided into different regions. Edge contour and facial detail lines are extracted based on different regions, and edge tangent flow is optimized to enhance the direction information. On this basis, the line image is used to generate the harmonic image, and the optimized edge tangent flow, facial semantic segmentation results, and harmonic image are used to adjust the parameters of the line extraction method for different segmentation regions to realize the line filtering of the detail independent region and line enhancement of the detail focus region, generating a concise line portrait. The experimental results showed that the proposed CLPG-SS method could effectively extract the main contour lines of a human face, adjust the detail lines for different regions, and improve the rendering efficiency of a robot manipulator.

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备注/Memo

备注/Memo:
收稿日期:2021-01-14。
作者简介:吴涛,硕士研究生,主要研究方向为图像处理、机器学习;董肖莉,助理研究员,主要研究方向为图像处理、模式识别、机器视觉。参与国家重大科学仪器设备开发专项项目1项,授权发明专利8项、软件著作权11项。发表学术论文10余篇。;孟伟,副教授,主要研究方向为物联网技术、人工智能。参与或主持“十一五”国家科技支撑计划项目、校级创新团队项目和横向课题多项。发表学术论文10余篇。
通讯作者:孟伟. E-mail:mnancy@bjfu.edu.cn
更新日期/Last Update: 2021-02-25