›› 2017,Vol. 32 ›› Issue (1): 122-138.doi: 10.1007/s11390-017-1709-z

所属专题: 不能删除 Artificial Intelligence and Pattern Recognition

• Special Section on Computer Networks and Distributed Computing • 上一篇    下一篇

图像抠像中的预处理方法综述

Gui-Lin Yao   

  1. School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China
  • 收稿日期:2016-04-18 修回日期:2016-10-18 出版日期:2017-01-05 发布日期:2017-01-05
  • 作者简介:Gui-Lin Yao received his B.E., M.E. and Ph.D. degrees in computer science and technology from Harbin Institute of Technology, Harbin, in 2003, 2005 and 2013, respectively. He is currently an associate professor in the School of Computer and Information Engineering, Harbin University of Commerce, Harbin. His research interests include image processing, computer vision,and video surveillance.
  • 基金资助:

    This work was supported by the Doctoral Scientific Research Start Fund Project of Harbin University of Commerce of China under Grant No. 15KJ06, the Youth Innovation Talent Support Program of Harbin University of Commerce under Grant No. 2016QN054, and the National Basic Research 973 Program of China under Grant No. 2015CB351804.

A Survey on Pre-Processing in Image Matting

Gui-Lin Yao   

  1. School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China
  • Received:2016-04-18 Revised:2016-10-18 Online:2017-01-05 Published:2017-01-05
  • About author:Gui-Lin Yao received his B.E., M.E. and Ph.D. degrees in computer science and technology from Harbin Institute of Technology, Harbin, in 2003, 2005 and 2013, respectively. He is currently an associate professor in the School of Computer and Information Engineering, Harbin University of Commerce, Harbin. His research interests include image processing, computer vision,and video surveillance.
  • Supported by:

    This work was supported by the Doctoral Scientific Research Start Fund Project of Harbin University of Commerce of China under Grant No. 15KJ06, the Youth Innovation Talent Support Program of Harbin University of Commerce under Grant No. 2016QN054, and the National Basic Research 973 Program of China under Grant No. 2015CB351804.

数字图像抠像领域中的预处理技术,是图像抠像中一个非常重要的步骤,目的是正式抠像计算前,在给定的3分模板(Trimap)中预划分出一些绝对前景和背景像素.该步骤完全不采用抠像公式,而仅是利用当前未知像素与已知前景或背景像素的颜色差异作为划分条件.这些被划分的像素能够作为后续抠像计算的样本并改善最终的抠像效果.然而,目前在数字抠像领域上,各算法对预处理步骤的重要性仍然比较模糊.而且,对于预处理步骤缺乏相应的综述类文献,而且对预处理后的Trimap和Alpha抠像效果也缺乏定量的比较.本文首先详细分析了预处理步骤在数字抠像中的必要性与重要性.其次,本文将目前的存在预处理算法分为2类:静态阈值法和动态阈值法.分析及实验表明,静态阈值法、尤其是目前最流行的迭代方法,对一般存在较少未知点的Trimap划分效果较为精确.然而,对于存在较多未知点的Trimap,该类方法局限于固定的颜色及空间的划分阈值,划分效果相对保守.与之相对应,动态阈值法在较为复杂例子中的划分性能较强,但也存在很多噪声和误划分等因素.另外,作为一种提供绝对先验点的方法,本文同时讨论了硬边界检测器及其划分效果.最后,本文综合了已存在各种预处理算法的优点,最终总结出了一个理想的预处理方案.

Abstract: Pre-processing is an important step in digital image matting, which aims to classify more accurate foreground and background pixels from the unknown region of the input three-region mask (Trimap). This step has no relation with the well-known matting equation and only compares color differences between the current unknown pixel and those known pixels. These newly classified pure pixels are then fed to the matting process as samples to improve the quality of the final matte. However, in the research field of image matting, the importance of pre-processing step is still blurry. Moreover, there are no corresponding review articles for this step, and the quantitative comparison of Trimap and alpha mattes after this step still remains unsolved. In this paper, the necessity and the importance of pre-processing step in image matting are firstly discussed in details. Next, current pre-processing methods are introduced by using the following two categories:static thresholding methods and dynamic thresholding methods. Analyses and experimental results show that static thresholding methods, especially the most popular iterative method, can make accurate pixel classifications in those general Trimaps with relatively fewer unknown pixels. However, in a much larger Trimap, there methods are limited by the conservative color and spatial thresholds. In contrast, dynamic thresholding methods can make much aggressive classifications on much difficult cases, but still strongly suffer from noises and false classifications. In addition, the sharp boundary detector is further discussed as a prior of pure pixels. Finally, summaries and a more effective approach are presented for pre-processing compared with the existing methods

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