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首页> 外文期刊>International journal of computational vision and robotics >Original strategy for avoiding over-smoothing in SFS problem resolution
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Original strategy for avoiding over-smoothing in SFS problem resolution

机译:避免SFS问题解决中过度平滑的原始策略

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With the aim of retrieving 3D surfaces starting from single shaded images, i.e. for solving the widely known shape from shading problem, an important class of methods is based on minimisation techniques where the expected surface to be retrieved is supposed to be coincident with the one that minimise a properly developed functional, consisting of several contributions. Despite several different contributes that can be explored to define a functional, the so called 'smoothness constraint' is a cornerstone since it is the most relevant contribute to guide the convergence of the minimisation process towards a more accurate solution. Unfortunately, in case input shaded image is characterised by areas where actual brightness changes rapidly, such a constraint introduces an undesired over-smoothing effect for the retrieved surface. The present work proposes an original strategy for avoiding such a typical over-smoothing effect, with regard to the image regions in which this is particularly undesired such as, for instance, zones where surface details are to be preserved in the reconstruction. The proposed strategy is tested against a set of case studies and compared with other traditional SFS-based methods to prove its effectiveness.
机译:为了从单一阴影图像开始检索3D表面,即从阴影问题解决广为人知的形状,一类重要的方法是基于最小化技术,其中应将要检索的预期表面与最小化适当开发的功能,其中包括几个方面。尽管可以探索定义功能的各种不同贡献,但是所谓的“平滑性约束”是基石,因为它是引导最小化过程向更精确解决方案收敛的最相关贡献。不幸的是,如果输入阴影图像的特征在于实际亮度快速变化的区域,则这种限制会为检索的表面带来不希望的过度平滑效果。对于特别不希望这样的图像区域,例如在重建中要保留表面细节的区域,本发明提出了一种避免这种典型的过度平滑效果的原始策略。通过一系列案例研究对提出的策略进行了测试,并将其与其他基于SFS的传统方法进行了比较,以证明其有效性。

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