Image Segmentation for Surface Material-type Classi.cation using 3D Geometry Information
This paper describes a novel approach for the segmentation of complex images to determine candidates for accurate material-type classification. The proposed approach identifies classification candidates based on image quality calculated from viewing distance and angle information. The required viewing distance and angle information is extracted from 3D fused images constructed from laser range data and image data. This approach sees application in material-type classification of images captured with varying degrees of image quality attributed to geometric uncertainty of the environment typical for autonomous robotic exploration. The proposed segmentation approach is demonstrated on an autonomous bridge maintenance system and validated using gray level cooccurrence matrix (GLCM) features combined with a naive Bayes classifier. Experimental results demonstrate the effects of viewing distance and angle on classification accuracy and the benefits of segmenting images using 3D geometry information to identify candidates for accurate material-type classification.
perspective projection material-type classi cation image classi cation gray level co-occurrence matrix naive Bayes classifier
Andrew Wing Keung To Gavin Paul Dikai Liu
ARC Centre of Excellence for Autonomous Systems University of Technology Sydney NSW,Australia
国际会议
2010 IEEE信息与自动化国际会议(ICIA 2010)
哈尔滨
英文
1-6
2010-06-20(万方平台首次上网日期,不代表论文的发表时间)