首页> 外文会议>European Conference on Computer Vision(ECCV 2006) pt.2; 20060507-13; Graz(AT) >Affine-Invariant Multi-reference Shape Priors for Active Contours
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Affine-Invariant Multi-reference Shape Priors for Active Contours

机译:活动轮廓的仿射不变多参考形状先验

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We present a new way of constraining the evolution of a region-based active contour with respect to a set of reference shapes. The approach is based on a description of shapes by the Legendre moments computed from their characteristic function. This provides a region-based representation that can handle arbitrary shape topologies. Moreover, exploiting the properties of moments, it is possible to include intrinsic affine invariance in the descriptor, which solves the issue of shape alignment without increasing the number of d.o.f. of the initial problem and allows introducing geometric shape variabilities. Our new shape prior is based on a distance between the descriptors of the evolving curve and a reference shape. The proposed model naturally extends to the case where multiple reference shapes are simultaneously considered. Minimizing the shape energy, leads to a geometric flow that does not rely on any particular representation of the contour and can be implemented with any contour evolution algorithm. We introduce our prior into a two-class segmentation functional, showing its benefits on segmentation results in presence of severe occlusions and clutter. Examples illustrate the ability of the model to deal with large affine deformation and to take into account a set of reference shapes of different topologies.
机译:我们提出了一种新的方法来约束相对于一组参考形状的基于区域的活动轮廓的演变。该方法基于根据勒让德矩从其特征函数计算得出的形状描述。这提供了可以处理任意形状拓扑的基于区域的表示形式。此外,利用矩的特性,可以在描述符中包含固有仿射不变性,从而解决了形状对齐问题,而无需增加d.f.f的数量。最初的问题,并允许引入几何形状变化。我们的新形状先验是基于演化曲线的描述符与参考形状之间的距离。提议的模型自然扩展到同时考虑多个参考形状的情况。最小化形状能量,导致不依赖轮廓的任何特定表示的几何流,并且可以使用任何轮廓演化算法来实现。我们将我们的Prior引入两类细分功能,以显示在存在严重遮挡和混乱的情况下细分结果的优势。实例说明了该模型处理大仿射变形并考虑一组不同拓扑的参考形状的能力。

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