摘要
复杂背景下,传统显著性检测方法经常遭遇检测结果不稳定和准确率低的问题。针对这些问题,提出一种基于贝叶斯框架融合深度信息的显著性检测方法。首先利用全局对比、局部对比和前景背景对比方法获取颜色显著图,并利用非均质中心-邻居差异的深度对比方法获取深度显著图。其次采用贝叶斯模型融合颜色显著图和深度显著图,获得输出显著图。实验结果表明,本文的方法能有效检测出复杂背景下的显著目标,并在公开的NLPR-RGBD数据集和NJU-DS400数据集上取得较高检测精确度。
In the complex background, the traditional saliency detection methods often encounter the problems of unstable detection results and low accuracy. To address this problem, a saliency detection method fused depth information based on Bayesian framework is proposed. Firstly, the color saliency map is obtained by using a variety of contrast methods which includes global contrast, local contrast and foreground-background contrast, and the depth saliency map is obtained by using the depth contrast method based on the anisotropic center-surround difference. Secondly, using the Bayesian model to fuse the color-based saliency map and the depth-based saliency map. The experimental results show that the proposed method can effectively detect the salient targets under complex background and achieve higher detection accuracy on the published NLPR-RGBD dataset and NJU-DS400 dataset.
作者
赵宏伟
何劲松
Zhao Hongwei, He Jinsong(School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, Chin)
出处
《光电工程》
CAS
CSCD
北大核心
2018年第2期8-15,共8页
Opto-Electronic Engineering
作者简介
赵宏伟（1991-），女，硕士研究生，主要从事模式识别的研究。E-mail：SA023046@mail．ustc．edu．cn
