Storage-efficient method for generating contours focusing on roundness

Goto, Hiroyuki; Shimakawa, Yoichi

doi:10.1080/13658816.2015.1080830

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The reconstructed curve forms an oriented contour, the area and perimeter of which can be calculated using Green's theorem (Stroud and Booth 2005). Goto and Shimakawa (2013) introduced a framework based on a first-order approximation, while Goto and Shimakawa (2016) managed to reduce the number of control points by a piecewise arc approximation. An oriented contour has another benefit by which the contour which is a part of a mountain or a basin can be judged; along with a visualization technique, a hill and punchbowl can be distinguished.…”

Section: Open Accessmentioning

confidence: 99%

“…In previous study by Goto and Shimakawa (2016), a method to partition ∂D based on roundness is proposed. The interpolation is performed by piecewise arc approximation, by which the exact area and length can be calculated.…”

Section: Arc Splinementioning

confidence: 99%

“…We now set (a, b) = (2, 1∕2) for the ellipse and (1, 1/0.999) for the Cassini oval, the profiles of which are depicted in Figure 10. We encode the curves in the form of Equation (2) using a piecewise arc algorithm (Goto and Shimakawa 2016) with varying roundness parameters = 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, 0.002, 0.001. In calculating the perimeter of the Hermite spline, we set the number of divisions in Equation 31…”

Section: Computation Accuracy and Storage Efficiencymentioning

confidence: 99%

“…This corresponds to approximately 60.1 × 46.2 (m) around the peak. We detect and encode contours using a piecewise arc algorithm (Goto and Shimakawa 2016).…”

Section: Computation Accuracy and Storage Efficiencymentioning

confidence: 99%

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Storage-efficient reconstruction framework for planar contours

Goto

Shimakawa

2016

Geo-spatial Information Science

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A storage-efficient reconstruction framework for cartographic planar contours is developed. With a smaller number of control points, we aim to calculate the area and perimeter as well as to reconstruct a smooth curve. The input data forms an oriented contour, each control point of which consists of three values: the Cartesian coordinates (x, y) and tangent angle θ. Two types of interpolation methods are developed, one of which is based on an arc spline while the other one is on a cubic Hermite spline. The arc spline-based method reconstructs a G 1 continuous curve, with which the exact area and perimeter can be calculated. The benefit of using the Hermite spline-based method is that it can achieve G 2 continuity on most control points and can obtain the exact area, whereas the resulting perimeter is approximate. In a numerical experiment for analytically defined curves, more accurate computation of the area and perimeter was achieved with a smaller number of control points. In another experiment using a digital elevation model data, the reconstructed contours were smoother than those by a conventional method.

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Section: Open Accessmentioning

confidence: 99%

Section: Arc Splinementioning

confidence: 99%

Section: Computation Accuracy and Storage Efficiencymentioning

confidence: 99%

“…This corresponds to approximately 60.1 × 46.2 (m) around the peak. We detect and encode contours using a piecewise arc algorithm (Goto and Shimakawa 2016).…”

Section: Computation Accuracy and Storage Efficiencymentioning

confidence: 99%

See 2 more Smart Citations