Selective Image Matting with Scalable Variance and Model Rectification  

作　　者：Xiao Chen Fazhi He Yiteng Pan Haojun Ai 

机构地区：[1]School of Computer Science,Wuhan University,Wuhan 430072,China

出　　处：《国际计算机前沿大会会议论文集》2017年第1期134-138,共5页International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）

摘　　要：Bayesian Matting has four limitations.Firstly,Bayesian matting makes strong assumption that the texture distribution of nature image satisfies Gaussian distribution with fixed variance.This assumption will fail for complex texture distribution.In order to extract the nature images with complex texture distribution,we design an information entropy approach to estimate the scalable variance.Secondly,when the opacity is near the boundary of the value range,Bayesian matting method may be failure because of the error computation of opacity.Therefore,a rectification approach is proposed to adjust the computation model and keep the opacity within the valid value range.Thirdly,Bayesian matting is a local sample method which may miss some valid samples of matting.We propose a selection function to integrate valid global sample matting result into above matting framework as a supplement to the local sample matting result.The proposed function is compose of three criteria,that is,the similarity of results,the overlapping degree of samples,and the similarity of neighborhood.Fourthly,in order to obtain a smooth and vivid matte,the result is further refined by considering correlation between neighbouring pixels.Finally,We use online benchmark for image matting to evaluate the proposed method with both qualitative observation and quantitative analysis.The experiments show that our method achieves a competitive advantages over other methods.

关 键 词：Image MATTING Information ENTROPY MODEL RECTIFICATION Constraint optimization Global SAMPLE Selection function 

分 类 号：C5[社会学]