Building Better Formlet Codes for Planar Shape

A. Yakubovich, J. Elder
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引用次数: 2

Abstract

The GRID/formlet representation of planar shape has a number of nice properties [4], [10], [3], but there are also limitations: it is slow to converge for shapes with elongated parts, and it can be sensitive to parameterization as well as grossly ill-conditioned. Here we describe a number of innovations on the GRID/formlet model that address these problems: 1) By generalizing the formlet basis to include oriented deformations we achieve faster convergence for elongated parts. 2) By introducing a modest regularizing term that penalizes the total energy of each deformation we limit redundancy in formlet parameters and improve identifiability of the model. 3) By applying a recent contour remapping method [9] we eliminate problems due to drift of the model parameterization during matching pursuit. These innovations are shown to both speed convergence and to improve performance on a shape completion task.
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为平面形状构建更好的模板代码
平面形状的GRID/formlet表示具有许多很好的特性[4],[10],[3],但也存在局限性:对于具有细长部分的形状,它收敛速度很慢,并且对参数化和严重病态很敏感。在这里,我们描述了解决这些问题的网格/模板模型上的一些创新:1)通过将模板基础推广到包括定向变形,我们实现了细长零件的更快收敛。2)通过引入一个适度的正则化项来惩罚每个变形的总能量,我们限制了形式参数的冗余,提高了模型的可识别性。3)通过应用一种最新的轮廓重映射方法[9],我们消除了匹配追踪过程中由于模型参数化漂移造成的问题。这些创新既加快了收敛速度,又提高了形状完成任务的性能。
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