A non-parametric approach to shape reconstruction from planar point sets through Delaunay filtering

Peethambaran, Jiju; Muthuganapathy, Ramanathan

doi:10.1016/j.cad.2014.12.002

Cited by 32 publications

(27 citation statements)

References 13 publications

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“…Boundary points should be used to increase density according to the Gestalt law of proximity (Figure 8a). Triangle filtration using Peethambaran and Muthuganapathy [54] was subsequently employed to extract the boundaries of the city centres (Figure 8c). This method was selected because it can derive an acute and unique shape without any external parameters.…”

Section: Extracting the City Centre Areamentioning

confidence: 99%

Building an Urban Spatial Structure from Urban Land Use Data: An Example Using Automated Recognition of the City Centre

Zhu

Sun

2017

IJGI

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It has been suggested that the method of constructing an urban spatial structure typically follows a forward process from planning and design up to expression, as reflected in both graphic and text descriptions of urban planning. Although unorthodox, the original status structures can be extracted and constructed from an existing urban land-use map. This approach not only provides the methodological foundation for urban spatial structure evolution and allows for a comparative and quantitative analysis between the existing and planned conditions, but also lays a theoretical basis for failure in scientific decision making during the planning phase. This study attempts to achieve this by identifying the city centre (a typical element of the urban spatial structure) from urban land use data. The city centre is a special region consisting of several units with particular spatial information, including geometric attributes, topological attributes, and thematic attributes. In this paper, we develop a methodology to support the delineation of the city centre, considering these factors. First, using commercial land data, we characterise the city centre as units based on a series of indicators, including geometric and thematic attributes, and integrate them into a composite index of "urban centrality"; Second, a graph-based spatial clustering method that considers both topological proximity and attribute similarity is designed and used to identify the city centre. The precise boundary of the city centre is subsequently delimited using a shape reconstruction method based on the cluster results. Finally, we present a case study to demonstrate the effectiveness and practicability of the methodology.

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Section: Extracting the City Centre Areamentioning

confidence: 99%

Building an Urban Spatial Structure from Urban Land Use Data: An Example Using Automated Recognition of the City Centre

Zhu

Sun

2017

IJGI

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“…Another one (though in 3D) is the sculpting algorithm by Boissonnat [6]. In [7], sculpting strategy is based on the length of the boundary edge of tetrahedron whereas in [4], it is based on the circumcircle of an exterior triangle. The reconstruction in [8] is done by a greedy simplification of Delaunay Triangulation using a series of half-edge collapse operations that minimises the increase of total transport between the input point set and the triangulation.…”

Section: Introductionmentioning

confidence: 99%

“…We use circle (three types of circles) and regularity constraints as the strategy whereas in [4] it is based on a combination of circumcenter and circumradius of Delaunay triangle. An optimal transport-driven approach is proposed in [8].…”

Section: Introductionmentioning

confidence: 99%

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A unified approach towards reconstruction of a planar point set

Methirumangalath

Parakkat

Muthuganapathy

2015

Computers & Graphics

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“…Dorninger and Pfeifer (2008) used an α value which was twice the mean point-to-point distance in the input point set. The non-parametric approach in Peethambaran and Muthuganapathy (2015) is dependent on the homogeneity of the input data and thus does not perform well in random point cloud data. Researchers have used the minimum spanning tree and/or a depth-first search algorithm to obtain the boundary edge sequence from the discrete edges (Wang and Shan 2009).…”

Section: Direct Contour Extractionmentioning

confidence: 99%

Using point cloud data to identify, trace, and regularize the outlines of buildings

Awrangjeb

2016

International Journal of Remote Sensing

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Rectilinear building outline generation from the point set of a building usually works in three steps. Boundary edges that constitute the building outline are first identified. A sequence of points is then traced from the edges to define the building boundary. Finally, lines are generated from the sequence of points and adjusted to form a regular building outline. Existing solutions have shortcomings in one or more of the following cases: identifying details along a concave shape, separate identification of a 'hole' inside the shape, proper boundary tracing, and preservation of detailed information along a regularized building outline. This article proposes new solutions to all three steps. By using the maximum point-to-point distance in the input data, the solution to the identification step properly detects the boundary edges for any type of shape and separately recognizes holes, if any, inside the shape. The proposed tracing algorithm divides boundary edges into segments, accurately obtains the sequence of points for each segment and then merges them, if necessary, to produce a single boundary for each shape. The regularization step proposes an improved corner and line extraction algorithm and adjusts the extracted lines with respect to the automatically determined principal directions of buildings. In order to evaluate the performance, an evaluation system that makes corner correspondences between an extracted building outline and its reference outline is also proposed. Experimental results show that the proposed solutions can preserve detail along the building boundary and offer high pixel-based completeness and geometric accuracy, even in low-density input data. ARTICLE HISTORY

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A non-parametric approach to shape reconstruction from planar point sets through Delaunay filtering

Cited by 32 publications

References 13 publications

Building an Urban Spatial Structure from Urban Land Use Data: An Example Using Automated Recognition of the City Centre

Building an Urban Spatial Structure from Urban Land Use Data: An Example Using Automated Recognition of the City Centre

A unified approach towards reconstruction of a planar point set

Using point cloud data to identify, trace, and regularize the outlines of buildings

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