Research Article Open Access

A New Approach for 3D Range Image Segmentation using Gradient Method

Dina A. Hafiz1, Walaa M. Sheta1, Sahar Bayoumi1 and Bayumy A.B. Youssef1
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Journal of Computer Science
Volume 7 No. 4, 2011, 475-487

DOI: https://doi.org/10.3844/jcssp.2011.475.487

Submitted On: 27 December 2010 Published On: 4 April 2011

How to Cite: Hafiz, D. A., Sheta, W. M., Bayoumi, S. & Youssef, B. A. (2011). A New Approach for 3D Range Image Segmentation using Gradient Method. Journal of Computer Science, 7(4), 475-487. https://doi.org/10.3844/jcssp.2011.475.487

Abstract

Problem statement: Segmentation of 3D range images is widely used in computer vision as an essential pre-processing step before the methods of high-level vision can be applied. Segmentation aims to study and recognize the features of range image such as 3D edges, connected surfaces and smooth regions. Approach: This study presents new improvements in segmentation of terrestrial 3D range images based on edge detection technique. The main idea is to apply a gradient edge detector in three different directions of the 3D range images. This 3D gradient detector is a generalization of the classical sobel operator used with 2D images, which is based on the differences of normal vectors or geometric locations in the coordinate directions. The proposed algorithm uses a 3D-grid structure method to handle large amount of unordered sets of points and determine neighborhood points. It segments the 3D range images directly using gradient edge detectors without any further computations like mesh generation. Our algorithm focuses on extracting important linear structures such as doors, stairs and windows from terrestrial 3D range images these structures are common in indoors and outdoors in many environments. Results: Experimental results showed that the proposed algorithm provides a new approach of 3D range image segmentation with the characteristics of low computational complexity and less sensitivity to noise. The algorithm is validated using seven artificially generated datasets and two real world datasets. Conclusion/Recommendations: Experimental results showed that different segmentation accuracy is achieved by using higher Grid resolution and adaptive threshold.

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Keywords

  • Laser scanning
  • point cloud
  • edge detection
  • normal vector estimation
  • Least Square Fitting (LSF)
  • Principal Component Analysis (PCA)
  • gradient method
  • range image segmentation
  • Terrestrial Laser Scanning (TLS)
  • Airborne Laser Scanning (ALS)