Advanced Search
Volume 37 Issue 11
Nov.  2015
Turn off MathJax
Article Contents
YANG Lijun, LI Minghang, LU Haitao, GUO Lin. Spoofing Attack Detection Scheme Based on Channel Fingerprint for Millimeter Wave MIMO System[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4228-4234. doi: 10.11999/JEIT220934
Citation: Chen Qiu-feng, Shen Qun-tai, Liu Peng-fei. Cost Filtered Matting with Radom Texture Features[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2578-2586. doi: 10.11999/JEIT150143

Cost Filtered Matting with Radom Texture Features

doi: 10.11999/JEIT150143
Funds:

The National Natural Science Foundation of China (61473318, 60974048)

  • Received Date: 2015-01-27
  • Rev Recd Date: 2015-06-29
  • Publish Date: 2015-11-19
  • In order to deal with the color overlap problem in matting, a fast random projection method is proposed to complement the color information. First, the raw texture matrix is obtained through dense abstraction from color image. The random projection is performed and the best three texture channels are chosen by the foreground and background overlap factors. Combining the texture image, the new cost function takes into account texture, color, and spatial information. Second, the filtering process is carried out to the sample selection cost, including the effect of the local and nonlocal neighbors. Finally, the relationship between iterative filter and global energy smooth is proven, and the post filter formula is obtained. Experiments show that the cost filtered matting with random texture features produces both visually and quantitatively better results when the color distributions of the foreground and background are similar.
  • Levin A, Lischinski D, and Weiss Y. A closed form solution to natural image matting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 30(2): 61-68.
    Shi Y, Au O C, Pang J, et al.. Color clustering matting[C]. IEEE International Conference on Multimedia and Expo, California, USA, 2013, 7: 1-6.
    Wang J and Cohen M F. Optimized color sampling for robust matting[C]. IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007, 6: 281-288.
    He B,Wang G J, and Zhang C. Iterative transductive learning for automatic image segmentation and?matting?with RGB-D data[J]. Journal of Visual Communication and Image Representation, 2014, 25(5): 1031-1043.
    Shahrian E, Rajan D, Price B, et al.. Improving image matting using comprehensive sampling sets[C]. Conference on Computer Vision and Pattern Recognition, Oregon, Portland, USA, 2013, 6: 636-643.
    Shahrian E and Rajan D. Weighted color and texture sample selection for image matting[C]. IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, USA, 2012, 6: 718-725.
    Rhemann C, Rother C, and Gelautz M. Improving color modeling for alpha matting[C]. The British Machine Vision Conference, Leeds, UK, 2008, 9: 1155-1164.
    He K, Rhemann C, Rother C, et al.. A global sampling method for alpha matting[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado, USA, 2011, 6: 2049-2056.
    Gastal E S and Oliveira M M. Shared sampling for real‐time alpha matting[J]. Eurographics, 2010, 29(2): 575-584.
    Jubin J, Deepu R, and Hisham C. Sparse codes as alpha mattes[C]. The British Machine Vision Conference, Nottingham, England, 2014, 9: 1-11.
    Varma M and Zisserman A. A statistical approach to material classification using image patches[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(11): 2032-2047.
    Liu L and Paul W. Texture classification from random features[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3): 574-586.
    Hosini A, Bleyer M, Rother C, et al.. Fast cost-volume filtering for visual correspondence and beyond[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(2): 504-511.
    Dasgupta S and Gupta A. An elementary proof of a theorem of Johnson and Lindenstrauss[J]. Random Structures and Algorithms, 2003, 22(1): 60-65.
    Sural S, Qian G, and Pramanik S. Segmentation and histogram generation using the HSV color space for image retrieval[C]. International Conference on Image Processing, New York, USA, 2002, 2: 589-592.
    Marius M and David GL. Fast approximate nearest neighbors with automatic algorithm configuration[C]. International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, 2009, 2: 331-340.
  • Cited by

    Periodical cited type(13)

    1. 张亚邦,李佳悦,王满利. 基于HSV空间的煤矿井下低光照图像增强方法. 红外技术. 2024(01): 74-83 .
    2. 孔二伟,张亚邦,李佳悦,王满利. 面向煤矿井下低光照图像的增强方法. 工矿自动化. 2023(04): 62-69+85 .
    3. 孔凡芝,李金龙,吴冬梅. 基于DWT-DCT和Zernike矩的鲁棒视频水印算法. 计算机应用与软件. 2020(04): 309-315 .
    4. 朱浩然,刘云清,张文颖. 基于灰度变换与两尺度分解的夜视图像融合. 电子与信息学报. 2019(03): 640-648 . 本站查看
    5. 朱浩然,刘云清,张文颖. 基于迭代导向滤波与多视觉权重信息的红外与可见光图像融合. 光子学报. 2019(03): 190-200 .
    6. 施文娟,孙彦景,左海维,曹起. 基于视频自然统计特性的无参考移动终端视频质量评价. 电子与信息学报. 2018(01): 143-150 . 本站查看
    7. 朱浩然,刘云清,张文颖. 基于对比度增强与多尺度边缘保持分解的红外与可见光图像融合. 电子与信息学报. 2018(06): 1294-1300 . 本站查看
    8. 邹良涛,蒋刚毅,郁梅,彭宗举,陈芬. 基于张量域感知特征的无参考高动态范围图像质量评价. 计算机辅助设计与图形学学报. 2018(10): 1850-1858 .
    9. 方小艳. 基于清晰度探测与人机交互的图像质量评价算法. 国外电子测量技术. 2017(04): 32-35 .
    10. 张治远. 基于三维图像的运动员起跑动作误差预测仿真. 计算机仿真. 2017(08): 412-416 .
    11. 施文娟,孙彦景,李松,曹起,谭泽富,代妮娜. 基于SSIM的无线视频码率变化聚类识别算法. 计算机工程与设计. 2017(09): 2302-2306+2313 .
    12. 杨陶,田怀文,刘晓敏,邢鹏举,马梦婕,高松松. 基于双截距直方图的Otsu图像分割法. 小型微型计算机系统. 2017(06): 1409-1414 .
    13. 闻新,谢天夏,闫钧华,张寅,黄伟. 改进结构相似度的红外两波段图像目标配准. 仪器仪表学报. 2017(12): 3112-3120 .

    Other cited types(9)

  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (1548) PDF downloads(394) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return