《计算机应用研究》|Application Research of Computers

模糊C-均值聚类引导的Kinect深度图像修复算法

Kinect depth map inpainting under fuzzy C-mean clustering guidance

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作者 万红,钱锐
机构 1.郑州大学 电气工程学院,郑州 450001;2.脑科学与脑机接口技术河南省重点实验室,郑州 450001
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文章编号 1001-3695(2019)05-059-1564-05
DOI 10.19734/j.issn.1001-3695.2017.12.0831
摘要 针对Kinect传感器所采集的深度图像中存在大面积空洞的问题,提出了一种模糊C-均值聚类引导的深度图像修复算法。该算法将同步获取的彩色图像和深度图像作为输入;利用模糊C-均值聚类算法对彩色图像进行聚类,聚类结果作为引导图像;然后对每个深度图像中的大面积空洞区域,利用改进的快速行进算法,从空洞边缘向空洞内部逐层修复空洞区域;最后,利用改进的双边滤波算法去除图像中的散粒噪声。实验表明该算法能有效修复Kinect深度图像中的空洞,修复后的图像在平滑度和边缘强度上优于传统算法。
关键词 深度图像; 空洞修复; 模糊C-均值算法; 聚类; 快速行进法
基金项目 国家自然科学基金面上项目(61673353)
河南省脑科学与脑机接口技术重点实验室开放基金资助项目(HNBBL17005)
本文URL http://www.arocmag.com/article/01-2019-05-059.html
英文标题 Kinect depth map inpainting under fuzzy C-mean clustering guidance
作者英文名 Wan Hong, Qian Rui
机构英文名 1.School of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,China;2.Henan Key Laboratory of Brain Science & Brain-Computer Interface Technology,Zhengzhou University,Zhengzhou 450001,China
英文摘要 Considering the problem that large holes in the depth map captured by Kinect, this paper proposed an algorithm for inpainting holes in depth map under fuzzy C-means clustering guidance. Firstly, it simultaneously obtained color image and depth map as input. Then the proposed method used fuzzy C-means clustering algorithm for color image, image clustering results as a guiding image. For the large holes in the depth map, the proposed method used the improved fast marching algorithm to inpaint the holes from the edge to the internal layer. For the discrete void points, the algorithm inpainted them by using the improved bilateral filtering. Experiments show that the algorithm can effectively inpaint holes in depth map captured by Kinect, and the restored depth map are superior to the depth map restored by the traditional algorithms in smoothness and edge strength.
英文关键词 depth image; hole inpainting; fuzzy C-means algorithm; clustering; fast marching method
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收稿日期 2017/12/5
修回日期 2018/1/22
页码 1564-1568
中图分类号 TP391.41
文献标志码 A