Simplifying Bézier paths #95
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First draft of new blog post. Closes #94
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| Two implementations are provided: parallel curves of cubic Béziers, and arbitrary Bézier paths for simplification. Implementing the trait is not very difficult, so it should be possible to add more source curves and transformations, taking advantage of powerful, general mechanisms to compute an optimized Bézier path. | ||
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| The core cubic Bézier fitting algorithm is based on measuring the area and moment of the source code. A default implementation is provided by the `ParamCurveFit` trait implementing numerical integration based on derivatives (Green's theorem), but if there's a better way to compute area and moment, source curves can provide their own implementation. |
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| The core cubic Bézier fitting algorithm is based on measuring the area and moment of the source code. A default implementation is provided by the `ParamCurveFit` trait implementing numerical integration based on derivatives (Green's theorem), but if there's a better way to compute area and moment, source curves can provide their own implementation. | |
| The core cubic Bézier fitting algorithm is based on measuring the area and moment of the source curve. A default implementation is provided by the `ParamCurveFit` trait implementing numerical integration based on derivatives (Green's theorem), but if there's a better way to compute area and moment, source curves can provide their own implementation. |
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| However, arc length parametrization is considerably slower (about a factor of 10) because it requires inverse arc length computations. The current approach is to classify whether the source curve is "spicy" (considering the deltas between successive normal angles) and use the more robust computation only in those cases. |
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| However, arc length parametrization is considerably slower (about a factor of 10) because it requires inverse arc length computations. The current approach is to classify whether the source curve is "spicy" (considering the deltas between successive normal angles) and use the more robust computation only in those cases. | |
| However, arc length parametrization is considerably slower (about a factor of 10) because it requires inverse arc length computations. My current approach is to classify whether the source curve is "spicy" (considering the deltas between successive normal angles) and use the more robust computation only in those cases. |
It wasn't clear to me what is actually implemented now.
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First draft of new blog post.
Closes #94