计算机应用与软件Issue(12)：245-248,4.DOI:10.3969/j.issn.1000-386x.2014.12.059
基于高斯混合模型聚类的 Kinect深度数据分割
KINECT DEPTH DATA SEGMENTATION BASED ON GAUSSIAN MIXTURE MODEL CLUSTERING
摘要
Abstract
Indoor scene understanding based on depth image is a cutting-edge issue in the field of three-dimensional computer vision.In 3D indoor scenes the planes are quite many, taking this feature into account, we present a Gauss mixture model clustering-based depth data segmentation method, and realise planes extraction from scene data.First, the method converts the depth image data acquired by Kinect into discrete three-dimensional data point cloud, and applies denoising and downsampling treatment on the point cloud data; On this basis, it calculates the normal vectors of all points in entire point cloud, and clusters the normal collection of entire 3D point cloud using Gaussian mixture model;next, it carries out the plane fitting on each clustering with random sampling consensus ( RANSAC) algorithm, gets a couple of planes from each clustering, and eventually segments the whole point cloud data into some sets of planes.Experimental results show that the divided regions using this method have accurate boundaries and the segmentation quality is above normal.The sets of planes extracted from the previous operations will lay a good foundation for the following indoor object recognition and scene understanding.关键词
室内场景理解/深度数据分割/高斯混合模型/随机抽样一致性算法/KinectKey words
Indoor scene understanding/Depth data segmentation/Gauss mixture model/RANSAC algorithm/Kinect分类
信息技术与安全科学引用本文复制引用
杜廷伟,刘波..基于高斯混合模型聚类的 Kinect深度数据分割[J].计算机应用与软件,2014,(12):245-248,4.基金项目
国家自然科学基金项目（61005001）；北京市教委项目（ KM200810005003）。 （）
