自然图像对的联合抠图方法

doi:10.16182/j.issn1004731x.joss.201811016

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (11): 4180-4186.doi: 10.16182/j.issn1004731x.joss.201811016

自然图像对的联合抠图方法

魏毅¹, 商柳², 邱嘉和², 张桂娟³, 朱登明^{2,4, *}, 沈燕飞⁵

1. 武夷学院数学与计算机学院,武夷山 354300;
2. 中国科学院计算技术研究所,北京 100190;
3. 山东师范大学信息科学与工程学院,济南 250014;
4. 太仓中科信息技术研究院,太仓 215400;
5. 北京体育大学信息工程学院,北京 100084

收稿日期:2018-05-05 修回日期:2018-06-10 发布日期:2019-01-04
作者简介:魏毅(1978-),男,福建建瓯,博士,讲师,研究方向为虚拟现实。
基金资助:
国家科技重大专项(2017ZX05019005),国家自然科学基金(61173067, 61379085, 61572299), 国家863计划(2015AA016401),福建省自然科学基金项目(2018J01562)

Co-matting of Natural Image Pairs

Wei Yi¹, Shang Liu², Qiu Jiahe², Zhang Guijuan³, Zhu Dengming^{2,4, *}, Shen Yanfei⁵

1. School of Mathematics and Computer, Wuyi University, Wuyishan 354300, China;
2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
3. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China;
4. Taicang Institute of Information Technology, Taicang 215400, China;
5. School of Information Engineering, Beijing Sport University, Beijing 100084, China

Received:2018-05-05 Revised:2018-06-10 Published:2019-01-04

摘要/Abstract

摘要： 针对自然图像的抠图问题,研究了一种基于闭形式解的图像对联合抠图方法。根据输入的两张图像与其初始掩像,计算图像对之间的相似矩阵;扩展现有描述单幅图像的抠图拉普拉斯矩阵,提出一种联合抠图拉普拉斯矩阵来刻画图像对;基于联合抠图拉普拉斯矩阵构造能量函数,并通过求解其极小值得到图像对的抠图结果。在多个数据集上进行了实验,实验结果表明新方法具有很好的抠图效果,适用于连续图像序列的抠图。

关键词: 自然图像抠图, 联合抠图, 相似性估计, 联合抠图拉普拉斯

Abstract: Aiming at dealing with the problem of natural image matting, this study focused on a closed-form solution to image pairs matting. For given two images and their corresponding trimaps, the affinity matrix between the two images is calculated. The matting Laplacian which characters an image to co-matting Laplacian which characters a pair of images is generalized. The alpha mattes of image pairs were obtained by optimizing the energy function which derived from co-matting Laplacian. To validate the approach, the experiments on several datasets are conducted. The results demonstrate that the method can achieve higher quality and is suitable for matting of a sequence of images.

Key words: natural image matting, co-matting, affinity estimation, co-matting Laplacian

中图分类号:

TP391.9

魏毅, 商柳, 邱嘉和, 张桂娟, 朱登明, 沈燕飞. 自然图像对的联合抠图方法[J]. 系统仿真学报, 2018, 30(11): 4180-4186.

Wei Yi, Shang Liu, Qiu Jiahe, Zhang Guijuan, Zhu Dengming, Shen Yanfei. Co-matting of Natural Image Pairs[J]. Journal of System Simulation, 2018, 30(11): 4180-4186.

参考文献

[1] 沈洋, 林晓, 谢志峰, 等. 交互式前景抠图技术综述[J]. 计算机辅助设计与图形学学报, 2014, 26(4): 511-519.
Shen Yang, Lin Xiao, Xie Zhifeng, et al.A survey on interactive alpha matting technology[J]. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(4): 511-519.
[2] Porter T, Duff T.Compositing digital images[C]//Proc of the 11th Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM Press, 1984: 253-259.
[3] Chuang Y Y, Curless B, Salesin D H, et al.A Bayesian approach to digital matting[C]//Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Kauai: IEEE Computer Society Press, 2001: 264-271.
[4] Guan Y, Chen W, Liang X, et al.Easy matting-a stroke based approach for continuous image matting[J]. Computer Graphics Forum (S1467-8659), 2006, 25(3): 567-576.
[5] Shahrian E, Rajan D, Price B.Improving image matting using comprehensive sampling sets[C]//Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Portland: IEEE Computer Society Press, 2013: 636-643.
[6] Levent K, Aykut, E, Erkut E. Alpha matting with KL-divergence-based sparse sampling[J]. IEEE Transactions on Image Processing (S1057-7149), 2017, 26(9): 4523-4536.
[7] Jubin J, Ehsan S V, Hisham C, et al.Sparse coding for alpha matting[J]. IEEE Transactions on Image Processing (S1057-7149), 2016, 25(7): 3032-3043.
[8] Sun J, Jia J, Tang C K, et al.Poisson matting[J]. ACM Transactions on Graphics (S1557-7368), 2004, 23(3): 315-321.
[9] Grady L, Schiwietz T, Aharon S, et al.Random walks for interactive alpha-matting[C]//Proc of Visualization, Im-aging, and Image Processing. Benidorm: ACTA Press, 2005: 423-429.
[10] Levin A, Lischinski D, Weiss Y.A closed-form solution to natural image matting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2008, 30(2): 228-242.
[11] Wang J, Cohen M F.Optimized color sampling for robust matting[C]//Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Minnesota: IEEE Computer Society Press, 2007: 1-8.
[12] He K, Rhemann C, Rother C, et al.A global sampling method for alpha matting[C]//Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Colorado: IEEE Computer Society Press, 2011: 2049-2056.
[13] Jin M, Kim B, Song W.Adaptive propagation-based color sampling for alpha matting[J]. IEEE Transactions on Circuits and Systems for Video Technology (S1051-8215), 2014, 24(7): 1101-1110.
[14] Rother C, Minka T, Blake A, et al.Cosegmentation of image pairs by histogram matching-incorporating a global constraint into MRFS[C]// Proc of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Computer Society Press, 2006: 993-1000.
[15] Sun J, Li Y, Kang S B, et al.Flash matting[J]. ACM Transactions on Graphics (S1557-7368), 2006, 25(3): 772-778.
[16] Wang L, Xia T, Guo Y, et al.Confidence-driven image co-matting[J]. Computers & Graphics (S0097-8493), 2014, 38(1): 131-139.
[17] Koen E A, Theo G, Cees G M.Evaluating color descriptors for object and scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-882 8), 2010, 32(9): 1582-1596.
[18] Rhemann C, Rother C, Wang J, et al. Alpha matting evaluation website[DB/OL]. [2018-05-05]. http://www.alphamatting. com
[19] Batra D, Kowdle A, Parikh D, et al.iCoseg: interactive co-segmentation with intelligent scribble guidance[C]// Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE Computer Society Press, 2010: 3169-3176.
[20] Ning Xu, Brian Price, Scott Cohen, et al.Deep Image Matting[C]//Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE Computer Society Press, 2017: 311-320.

自然图像对的联合抠图方法

Co-matting of Natural Image Pairs

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李智杰, 石昊琦, 李昌华, 张颉. 基于改进遗传算法的影像中心布局优化方法[J]. 系统仿真学报, 2022, 34(6): 1173-1184.
[2]	陈斌, 刘悦, 杨亚磊. 基于STN的机场航班过站保障时间协同规划建模[J]. 系统仿真学报, 2022, 34(6): 1196-1207.
[3]	杨凯, 陈纯毅, 胡小娟, 于海洋. 蒙卡渲染画面多特征非局部均值滤波降噪算法[J]. 系统仿真学报, 2022, 34(6): 1259-1266.
[4]	陈麒, 崔昊杨. 基于改进鸽群层级的无人机集群视觉巡检模型[J]. 系统仿真学报, 2022, 34(6): 1275-1285.
[5]	王沐晴, 张磊, 范秀敏, 骆晓萌, 朱文敏. VR外设驱动的虚拟人姿态优化仿真方法[J]. 系统仿真学报, 2022, 34(6): 1296-1303.
[6]	陆承, 靳学胜. 基于Steam VR的交互仿真水枪灭火训练系统设计[J]. 系统仿真学报, 2022, 34(6): 1312-1319.
[7]	高宏鼐, 付丽疆, 夏倩, 郭亚. 可观测度在光合作用模型性能评估中的应用[J]. 系统仿真学报, 2022, 34(6): 1330-1342.
[8]	倪凌佳, 黄晓霞, 李红旮, 张子博. 基于协作式深度强化学习的火灾应急疏散仿真研究[J]. 系统仿真学报, 2022, 34(6): 1353-1366.
[9]	蒙盾, 胡卓, 张华军. 基于改进A^*算法的多层邮轮疏散系统仿真[J]. 系统仿真学报, 2022, 34(6): 1375-1382.
[10]	郭宇飞, 赵康, 海永清. 面向有限元分析的三角网格布尔运算方法[J]. 系统仿真学报, 2022, 34(5): 1003-1014.
[11]	吴桐, 王清辉, 徐志佳. 三周期极小曲面多孔材料渗透率尺度特性研究[J]. 系统仿真学报, 2022, 34(5): 1015-1024.
[12]	蒋阳升, 王思琛, 高宽, 刘梦, 姚志洪. 混入智能网联车队的混合交通流元胞自动机模型[J]. 系统仿真学报, 2022, 34(5): 1025-1032.
[13]	梁江涛, 王慧琴. 基于改进蚁群算法的建筑火灾疏散路径规划研究[J]. 系统仿真学报, 2022, 34(5): 1044-1053.
[14]	张其文, 张斌. 基于教学优化算法求解置换流水车间调度问题[J]. 系统仿真学报, 2022, 34(5): 1054-1063.
[15]	邢根上, 鲁芳, 李书山, 罗定提. 基于产品体验性的供应链交货模型与仿真研究[J]. 系统仿真学报, 2022, 34(5): 1064-1075.