Simplification of Polygonal Chains by Enforcing Few Distinctive Edge Directions

Pohl, Melanie; Meidow, Jochen; Bulatov, Dimitri

doi:10.1007/978-3-319-59129-2_1

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…The approaches used for building outline regularisation can be grouped into three categories: (i) model-driven (Sohn et al, 2012;Br edif et al, 2013); (ii) data-driven (Pohl et al, 2017); or (iii) a combination of both models (He et al, 2014). The goal of these methods is to reduce the number of building boundary points to the minimum required to describe the shape of the building.…”

Section: Introduction and Previous Researchmentioning

confidence: 99%

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Building detection and regularisation using DSM and imagery information

Mousa

Helmholz

Belton

et al. 2019

The Photogrammetric Record

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An automatic method for the regularisation of building outlines is presented, utilising a combination of data‐ and model‐driven approaches to provide a robust solution. The core part of the method includes a novel data‐driven approach to generate approximate building polygons from a list of given boundary points. The algorithm iteratively calculates and stores likelihood values between an arbitrary starting boundary point and each of the following boundary points using a function derived from the geometrical properties of a building. As a preprocessing step, building segments have to be identified using a robust algorithm for the extraction of a digital elevation model. Evaluation results on a challenging dataset achieved an average correctness of 96·3% and 95·7% for building detection and regularisation, respectively.

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Section: Introduction and Previous Researchmentioning

confidence: 99%

“…The generated building map can be either building regions (such as a binary image) or segmented laser points. In both cases, a list of boundary points can be obtained using the Moore contour‐tracing algorithm (Gonzalez et al., ) or convex‐hull‐based procedures (Pohl et al., ).…”

Section: Introduction and Previous Researchmentioning

confidence: 99%

Building detection and regularisation using DSM and imagery information

Mousa

Helmholz

Belton

et al. 2019

The Photogrammetric Record

Self Cite

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“…This allows the design of better assessment functions for the similarity-in-orientation law. This idea has been proposed before for the determination of dominant orientations by fitting parametric distributions to the data (Pohl et al, 2017). However, here we assume the offset parameter of such distributions to be fixed, because for the Gestalt law parallelism the offset must be 0, and for orthogonality it must be π.…”

Section: Introductionmentioning

confidence: 99%

Design of Orientation Assessment Functions for Gestalt-Grouping Utilizing Labeled Sample-Data

Michaelsen

Meidow

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Psychological evidence is given that perceptual grouping is an important help for various visual tasks. Object recognition and land use classification from remotely sensed imagery is an example. In machine vision, such a grouping process can be implemented by coding Gestalt laws such as proximity, symmetry, or good continuation. Since geometric relations are rarely fulfilled exactly, soft membership functions are utilized called Gestalt assessments. Hierarchical grouping is possible on increasing scales. Such an approach to hierarchical Gestalt grouping is modified in this paper. In its original form, the approach uses rather heuristic default assessment functions, which are a possible choice as long as no labeled example data are given. The assessment functions can be parameterized so as to improve the perceptual grouping, guiding it by the Gestalten salient to human perception. To this end, we use orientation statistics from the publicly available data set given for the ICCV symmetry recognition competition 2017. Also, with a particular recognition task at hand, labeled example data can serve as the desired foreground. Here we use the ground-truth layer for buildings of the Vaihingen benchmark of the ISPRS. A mixture distribution containing two von Mises-distributions and the uniform component for the clutter in the background is fitted using expectation maximization.

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“…Many approaches have been used to conduct the first step, including intersecting horizontal lines in 3D space [46], rectangle fitting [43], weighted Remote Sens. 2018, 10, 1996 4 of 22 line-segment lengths analysis [19,45,47], longest fitted line detection in Euclidian space [26,48] or Hough space [40], vanishing points detection in image space [18], and histogram analysis [49][50][51]. Furthermore, some methods first divide all of the line segments into two orthogonal groups, then refit the dominant orientation by taking all of the original points into consideration [44,52].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Regularization of Building Boundaries in Noisy Aerial Laser Scanning and Photogrammetric Point Clouds

et al. 2018

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Aerial laser scanning or photogrammetric point clouds are often noisy at building boundaries. In order to produce regularized polygons from such noisy point clouds, this study proposes a hierarchical regularization method for the boundary points. Beginning with detected planar structures from raw point clouds, two stages of regularization are employed. In the first stage, the boundary points of an individual plane are consolidated locally by shifting them along their refined normal vector to resist noise, and then grouped into piecewise smooth segments. In the second stage, global regularities among different segments from different planes are softly enforced through a labeling process, in which the same label represents parallel or orthogonal segments. This is formulated as a Markov random field and solved efficiently via graph cut. The performance of the proposed method is evaluated for extracting 2D footprints and 3D polygons of buildings in metropolitan area. The results reveal that the proposed method is superior to the state-of-art methods both qualitatively and quantitatively in compactness. The simplified polygons could fit the original boundary points with an average residuals of 0.2 m, and in the meantime reduce up to 90% complexities of the edges. The satisfactory performances of the proposed method show a promising potential for 3D reconstruction of polygonal models from noisy point clouds.industrial standard datasets for various applications [10,11], including visualization, spatial analysis, urban planning, and navigation [12][13][14][15]. Although the extraction of points on the exterior building boundaries is gracefully handled by a standard convex hull or alpha-shapes for non-convex boundaries, simplification and regularization of the noisy boundaries are still non-trivial tasks and well-established cartographic algorithms do not produce acceptable results when applied to point cloud datasets.The inherent deficiencies of point clouds data, such as data anisotropy, insufficient sampling, and especially noise, make it challenging to retrieve compact 2D/3D polygons of buildings that have satisfactory geometric quality. For ALS data, which have high altimetric accuracy but relatively low point density [16], the initial boundary points of building plans are often jagged [17] and small structures are not well sampled. Meanwhile, DIM point clouds of aerial oblique images are generally inferior to those created from laser scanning in terms of noise level and the preservation of sharp features [9]. The forward intersected point clouds suffer from inaccurate positioning at the edge of building planes due to disparity discontinuities, and thus sharp features may degenerate at corners.In the boundary simplification process, traditional edge collapse-based methods are likely to eliminate sharp features, whereas regularization methods that adopt the Manhattan rule based on the dominant orientation tend to be too strict in many real-world applications, leading to large distortions when applied to polygons with ...

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Simplification of Polygonal Chains by Enforcing Few Distinctive Edge Directions

Cited by 8 publications

References 13 publications

Building detection and regularisation using DSM and imagery information

Building detection and regularisation using DSM and imagery information

Design of Orientation Assessment Functions for Gestalt-Grouping Utilizing Labeled Sample-Data

Hierarchical Regularization of Building Boundaries in Noisy Aerial Laser Scanning and Photogrammetric Point Clouds

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