A robust technique for the estimation of the deformable hyperquadrics from images

Senthil Kumar, Dmitry Goldgof
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引用次数: 4

Abstract

We present a robust technique for the estimation of deformable hyperquadrics from images. Hyperquadrics are volumetric shape models that include superquadrics as a special case. Recovering hyperquadric parameters is difficult not only due to the existence of many local minima in the error function but also due to the existence of an infinite number of global minima (with zero error) that do not correspond to any meaningful shape. An algorithm that minimizes the error-of-fit function without using techniques similar to those presented here will often find itself stuck in "meaningless" minima, even with good initialization. Our algorithm exhibits good convergence behavior and is largely insensitive to initialization.
一种从图像中估计可变形超二次曲面的鲁棒技术
我们提出了一种从图像中估计可变形超二次曲面的鲁棒技术。超二次曲面是包含超二次曲面作为特殊情况的体积形状模型。恢复超二次参数是困难的,这不仅是因为误差函数中存在许多局部极小值,而且还因为存在无数个不对应于任何有意义形状的全局极小值(误差为零)。一个最小化拟合误差函数而不使用类似于这里介绍的技术的算法经常会发现自己陷入“无意义的”最小值,即使有良好的初始化。该算法具有良好的收敛性,且对初始化不敏感。
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