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Image Segmentation from RGBD Images by 3D Point Cloud Attributes and High-Level Features
Mehdi Khazaeli, Leili Javadpour, Gerald Knapp
Pages - 1 - 13     |    Revised - 29-02-2016     |    Published - 01-04-2016
Volume - 10   Issue - 1    |    Publication Date - April 2016  Table of Contents
Graph-based Segmentation, Normals, RANSAC, Surface Detection, Occlusion.
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 algorithm is 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.
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Dr. Mehdi Khazaeli
Department of Civil Engineering University of the Pacific Stockton, 95211 - United States of America
Dr. Leili Javadpour
Department of Computer Science University of the Pacific Stockton, 95211 - United States of America
Associate Professor Gerald Knapp
Department of Mechanical and Industrial Engineering Louisiana State University Baton Rouge, 70803 - United States of America