标题:
基于背景先验点的虚焦抠图方法Defocus Matting on Background Blur Priors
作者:
姚桂林, 姚鸿勋
关键字:
图像抠图, 背景先验, 边缘检测, 模糊扩展Image Matting; Background Prior; Edge Detection; Blur Magnification
期刊名称:
《Artificial Intelligence and Robotics Research》, Vol.1 No.1, 2012-11-23
摘要:
本文针对图像抠图问题中背景虚焦时的情况，将一些模糊的背景预划分成先验点，使之成为绝对背景，进而避免了与前景颜色相近背景的干扰，使得抠图结果更为理想。首先，采用边缘检测器进行边缘检测，进而对每个边缘点进行分析，根据其对二阶算子的响应值与理想响应值之间的拟合误差，以及它与已知前景之间的颜色差异，判断其是否为被模糊的背景边缘点；然后再计算它沿梯度正负方向两侧的像素与理想化边缘之间的拟合误差，根据阈值可以将两侧的若干点扩展成背景先验点集，即将它们划分为绝对背景点。最后利用与trimap扩展相结合的抠图方式，得到最终的透明度图像。实验结果表明，本文提出的背景先验点与抠图算法，可以得到相对于以往的抠图算法更好的结果。In this paper, we present a matting method based on focused foreground and blurred background. We classify some pixels as priors to prevent the similarities between foreground and background colors. Firstly, we design a three-channel edge detector to roughly predict some edge pixels. Secondly, for each edge pixel, we estimate its blur degree by fitting an ideal second derivative filter response on the actual one along the gradient direction, and comparing its color and known foreground one. Thirdly, if this pixel is classified into a blurred background edge, we extend it along the gradient direction as blur priors according to the fitting errors to an ideal blurred edge. Finally, with the back-ground blur priors, we run a general matting algorithm along with a trimap expansion method. The experimental results show that our background blur priors could generate much more precise alpha results than the state-of-art algorithms.