Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Level Features

International Journal of Image Processing

Mehdi Khazaeli¹  Gerald Knapp¹  Leili Javadpour¹ 

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关键词: Graph-based Segmentation;    Normals;    RANSAC;    Surface Detection;    Occlusion.;   

DOI  :  

来源: Computer Science Journals

【 摘 要 】

In this paper, an approach is developed for segmenting an image into major surfaces and potential objects using RGBD images and 3D point cloud data retrieved from a Kinect sensor. In the proposed segmentation algorithm, depth and RGB data are mapped together. Color, texture, XYZ world coordinates, and normal-, surface-, and graph-based segmentation index features are then generated for each pixel point. These attributes are used to cluster similar points together and segment the image. The inclusion of new depth-related features provided improved segmentation performance over RGB-only algorithms by resolving illumination and occlusion problems that cannot be handled using graph-based segmentation algorithms, as well as accurately identifying pixels associated with the main structure components of rooms (walls, ceilings, floors). Since each segment is a potential object or structure, the output of this algorithmis intended to be used for object recognition. The algorithm has been tested on commercial building images and results show the usability of the algorithm in real time applications.

【 授权许可】

Unknown