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作 者:皮华立 李超 PI Huali;LI Chao(School of Mathematics,Tianjin University,Tianjin 300350,China)
机构地区:[1]天津大学数学学院
出 处:《计算机与应用化学》2018年第5期419-426,共8页Computers and Applied Chemistry
摘 要:现有的自然图像抠图算法可以分为三类:基于采样、基于传播和基于机器学习。通常为传播算法设计一个有效的像素特征非常困难,也一直是影响算法结果好坏的重要因素。本文探索将超像素作为特征应用在传播算法上效果,并设计了新的函数来衡量两个超像素之间的相似性。实验结果表明本文提出的方法能更有效地区分前背景像素,建立更准确的全局像素关系,并在标准测试集上取得了领先效果。Existing image matting methods can be included in three main categories: sampling-based, propagation-based, and machine learning-based. While it is difficult to design an effective pixel feature for the propagation-based methods, which has always been an important factor affecting the results of the algorithm. In this paper we investigate the effect of applying superpixels as a feature on the propagation algorithm, and design a new function to measure the similarity between two superpixels. The experimental results show that the method can effectively distinguish the foreground pixels from background pixels, and establish a more accurate global pixel relationship. The final results achieve a leading effect on the standard benchmark datasets.
分 类 号:TQ015.9[化学工程] TP391.9[自动化与计算机技术—计算机应用技术]
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