发明名称 Point cloud data clustering and classification using implicit geometry representation
摘要 An image processing system comprising a data repository and an image processor. The data repository is configured to store image data. The image processor is configured to place the image data into a three-dimensional mesh. The image processor is further configured to identify vectors of the image data in the three-dimensional mesh. The image processor is further configured to identify a number of clusters in the vectors of the image data in the three-dimensional mesh.
申请公布号 US9245170(B1) 申请公布日期 2016.01.26
申请号 US201213534499 申请日期 2012.06.27
申请人 THE BOEING COMPANY 发明人 Nikic Dejan;Wu Yuan-Jye Jason
分类号 G06K9/00;G06K9/62 主分类号 G06K9/00
代理机构 Yee & Associates, P.C. 代理人 Yee & Associates, P.C.
主权项 1. An image processing system comprising: a data repository, including at least one storage device on a computer system, configured to store image data comprising points in a point cloud; and an image processor and program code, which when executed perform operations on the image data, including: placing the image data into a three-dimensional mesh, comprising: placing points into a number of cells in the three-dimensional mesh based on three-dimensional locations of the points; and generating implicit geometry data for the number of cells using the points placed into the number of cells, implicit geometry data being one of a representation of points relative to each of the number of cells in the three-dimensional mesh and values based on the relationship of points located within the number of cells, wherein the three-dimensional mesh comprises metadata including attributes comprising one or more of population, distance, and validity; identifying vectors of the image data in the three-dimensional mesh, wherein each vector of the vectors comprises a corresponding vertical array of single cells in the three dimensional mesh; identifying clustering data using the vectors, wherein the clustering data comprises a plurality of clusters; assigning a label to each of the plurality of clusters based on a probability that a given cluster represents a given object; thereafter defining classes by one or more of the plurality of clusters, the classes comprising identifications of the given object; extracting feature vectors from the classes and comparing additional vectors to the feature vectors to determine corresponding classes for the additional vectors; and generating an image using the feature vectors and the corresponding classes for the additional vectors.
地址 Chicago IL US