Point-based morphological opening with input data retrieval

J. Balado*, P. Van Oosterom, L. Díaz-Vilariño, H. Lorenzo

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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Abstract

Mathematical morphology is a technique recently applied directly for point cloud data. Its working principle is based on the removal and addition of points from an auxiliary point cloud that acts as a structuring element. However, in certain applications within a more complex process, these changes to the original data represent an unacceptable loss of information. The aim of this work is to provide a modification of the morphological opening to retain original points and attributes. The proposed amendment involved in the morphological opening: erosion followed by dilatation. In morphological erosion, the new eroded points are retained. In morphological dilation, the structuring element does not add its points directly, but uses the point positions to search through the previously eroded points and retrieve them for the dilated point cloud. The modification was tested on synthetic and real data, showing a correct performance at the morphological level, and preserving the precision of the original points and their attributes. Furthermore, the conservation is shown to be very relevant in two possible applications such as traffic sign segmentation and occluded edge detection.

Original languageEnglish
Pages (from-to)53-58
Number of pages6
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume8
Issue number4/W2-2021
DOIs
Publication statusPublished - 2021
Event16th 3D GeoInfo Conference, 3D GeoInfo 2021 - New York City, United States
Duration: 11 Oct 202114 Oct 2021

Keywords

  • Detection
  • Image Processing
  • LiDAR
  • Mathematical Morphology
  • Point Cloud Processing
  • Segmentation

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