ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume VIII-4/W2-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4/W2-2021, 53–58, 2021
https://doi.org/10.5194/isprs-annals-VIII-4-W2-2021-53-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4/W2-2021, 53–58, 2021
https://doi.org/10.5194/isprs-annals-VIII-4-W2-2021-53-2021

  07 Oct 2021

07 Oct 2021

POINT-BASED MORPHOLOGICAL OPENING WITH INPUT DATA RETRIEVAL

J. Balado1,2, P. van Oosterom2, L. Díaz-Vilariño1,2, and H. Lorenzo1,2 J. Balado et al.
  • 1CINTECX, Universidade de Vigo, GeoTECH Group, 36310 Vigo, Spain
  • 2Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, The Netherlands

Keywords: LiDAR, Mathematical Morphology, Point Cloud Processing, Image Processing, Detection, Segmentation

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.