基于空-频域混合分析的RGB-D数据视觉显著性检测方法

doi:10.13973/j.cnki.robot.2017.0652

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岳娟, 袁夏, 李捷, 徐鹏. 基于空-频域混合分析的RGB-D数据视觉显著性检测方法[J]. 机器人, 2017, 39(5): 652-660.DOI: 10.13973/j.cnki.robot.2017.0652. YUE Juan, YUAN Xia, LI Jie, XU Peng. RGB-D Visual Saliency Detection Method Based on Spatial-Spectral Mixture Analysis. ROBOT, 2017, 39(5): 652-660. DOI: 10.13973/j.cnki.robot.2017.0652.

摘要针对移动机器人面对的真实3维场景数据，提出一种基于频域和空域混合分析的视觉显著性检测方法．首先设计多通道特征融合算法融合RGB-D数据中包含的颜色和深度信息，然后通过超复数傅里叶变换在频域计算得到多尺度视觉显著图，接着利用非均匀超像素分割算法对得到的显著图进行平滑处理，从而消除离散背景噪声干扰，改善频域检测结果．最后，采用元胞自动机对多尺度视觉显著图进行有效融合，提取最终的显著性区域．在公开数据库上进行了多组实验，验证了所提出算法在移动机器人面对的真实复杂场景数据中的有效性．

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关键词 ：视觉显著性, 深度信息, 超复数傅里叶变换, 环境理解

Abstract：To deal with the real 3D scene data faced by the mobile robot, a visual saliency detection method based on spatial-spectral mixture analysis is proposed. Firstly, a multichannel feature fusion algorithm is developed to integrate the color and depth information in RGB-D data. Then, the hypercomplex Fourier transform is applied in frequency domain to getting the multi-scale saliency maps. After that, an uneven superpixel segmentation algorithm is used to smooth each obtained saliency map. In this way, the interference of discrete background noises is eliminated and the detection result in frequency domain is improved. Finally, the cellular automata algorithm is adopted to merge the multi-scale visual saliency maps and extract the final saliency region. Plenty of experiments are conducted on the public database to verify the effectiveness of the proposed method in the real complex scene faced by the mobile robot.

Key words： visual saliency depth information hypercomplex Fourier transform environmental understanding

收稿日期: 2017-03-26 录用日期: 2017-06-22

TP391

基金资助:国家自然科学基金青年基金（61603184）；核高基国家重点专项（2015ZX01041101）；江苏省社会安全图像与视频理解重点实验室创新基金（30916014107）

通讯作者: 袁夏,yuanxia@njust.edu.cn E-mail: yuanxia@njust.edu.cn

作者简介: 岳娟(1993-),女,硕士.研究领域:智能机器人环境理解,计算机视觉,RGB-D图像显著性分析.
袁夏(1981-),男,博士,副教授.研究领域:地面智能机器人环境理解与自主导航技术,3维彩色点云数据分析,RGB-D图像显著性分析.
李捷(1988-),男,博士生.研究领域:智能机器人环境理解,计算机视觉,RGB-D图像显著性分析.

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https://robot.sia.cn/CN/10.13973/j.cnki.robot.2017.0652 或 https://robot.sia.cn/CN/Y2017/V39/I5/652

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