Invariant Unsupervised Segmentation of Dismounts in Depth Images

被引：0

作者：

Butler, Nathan S. ^{[1
]}

Tutwiler, Richard L. ^{[1
]}

机构：

[1] Penn State Univ, State Coll, PA 16802 USA

来源：

SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XXII | 2013年 / 8745卷

关键词：

3D LIDAR; Segmentation; Shape Descriptor; Histogram; Edge Detection; Morphological Filter;

D O I：

10.1117/12.2018187

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper will describe a scene invariant method for the unsupervised segmentation of dismounts in depth images. This method can be broken into two parts: ground plane detection and spatial segmentation. The former is accomplished by using RANSAC (RANdom SAmple Consensus) to identify a ground plane in the scene. After performing contrast enhancement the Image is "sliced" into regions. Each classified region is processed by a Robert's edge detector in order to separate each object. Each output is further processed by a block of shape filters that extract the human form.

引用

页数：14