A triangulation-based technique for building boundary identification from point cloud data

- Awrangjeb, Mohammad, Lu, Guojun

  • Authors: Awrangjeb, Mohammad , Lu, Guojun
  • Date: 2016
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015; Auckland, New Zealand; 23rd-24th November 2015 Vol. 2016-November, p. 1-6
  • Full Text: false
  • Reviewed:
  • Description: Building boundary identification is an essential prerequisite in building outline generation from point cloud data. In this problem, boundary edges that constitute the building boundary are identified. The existing solutions to the identification of boundary edges from the input point set have one or more of the following problems: ineffective in finding appropriate edges in a concave shape, incapable of determining a 'hole' or 'concavity' inside the shape separately, dependant on additional information such as the scan direction that may be unavailable, and incompetent in determining the boundary of a point set from the boundaries of two or more subsets of the point set. This paper proposes a new solution to the identification of building boundary by using the maximum point-to-point distance in the input data. It properly detects the boundary edges for any type of shape and separately recognises holes, if any, inside the shape. The unique feature of the proposed solution is that it can identify the boundary of a point set from the boundaries of two or more subsets of the point set. It does not require any additional information other than the input point set. Experimental results show that the proposed solution can preserve details along the building boundary and offer high area-based completeness and quality, even in low density input data. © 2015 IEEE.
  • Description: International Conference Image and Vision Computing New Zealand

Extracting road centrelines from binary road images by optimizing geodesic lines

- Zhou, Shaoguang, Lu, Guojun, Teng, Shyh, Zhang, Dengsheng

  • Authors: Zhou, Shaoguang , Lu, Guojun , Teng, Shyh , Zhang, Dengsheng
  • Date: 2016
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015; Auckland, New Zealand; 23rd-24th November 2015 Vol. 2016-November, p. 1-6
  • Full Text: false
  • Reviewed:
  • Description: Binary road images can be obtained from remotely sensed images with the aid of classification and segmentation techniques. Extracting road centrelines from these binary images are crucial to update a Geographic Information System (GIS) database. A current state of art method of centreline extraction needs to remove road junctions and depends on the accuracy of the endpoints, leading to three main limitations: (1) causing small gaps in the roads, (2) wrongly treating short non-road segments as roads, and (3) producing centrelines of low accuracy around the road end regions. To overcome these limitations, we propose to use an iteratively searching scheme to obtain the longest geodesic line in the preprocessed road skeleton images. Several image pixels at each end of the geodesic lines were removed to avoid noise, and the remaining parts were optimized using a dynamic programming snake model. The proposed method is applied to three types of binary road images and compared with the state of art method. It shows that the proposed method is less affected by the end regions of the roads, and is effective in filling the gaps in the roads. It also has an advantage on processing short non-road segments. © 2015 IEEE.
  • Description: International Conference Image and Vision Computing New Zealand
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