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Efficient representation of contours using splines implemented with FIR filters

机译:使用FIR滤波器实现的样条曲线有效地表示轮廓

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The interpolation of contour pixels, with splines, of an order greater than or equal to two, usually causes oscillations that do not fit the original shape. To overcome this we propose using a least squares filter before carrying out the interpolation. This results in a good compromise between the smoothness of the curve and the best fit to the original contour. Representing the contour with a continuous model instead of a discrete model has many advantages for carrying out calculations, as for example the contour curvature, which involves first and second order derivatives, as well as operations that are not well defined in the discrete world. We also present a new way of calculating FIR approximations to filters based on B-splines. The great advantage of this approximation in the case of least squares filter is that it does not need downsampling. This property makes it invariant to translations, and this is very important in classification tasks.
机译:用样条线对等高线像素进行的内插量级大于或等于2,通常会导致产生不适合原始形状的振荡。为了克服这个问题,我们建议在执行插值之前使用最小二乘滤波器。这样就可以在曲线的平滑度和与原始轮廓的最佳拟合之间取得良好的折衷。用连续模型而不是离散模型表示轮廓对于执行计算具有许多优势,例如轮廓曲率(涉及一阶和二阶导数)以及在离散世界中定义不明确的操作。我们还提出了一种新的方法来计算基于B样条的FIR近似值。在最小二乘滤波器的情况下,这种近似的最大优点是不需要下采样。此属性使其对翻译不变,这在分类任务中非常重要。

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