Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
IEEE Trans Pattern Anal Mach Intell. 2010 May;32(5):799-814. doi: 10.1109/TPAMI.2009.70.
A novel methodology is introduced here that exploits 2D images of arbitrary elastic body deformation instances so as to quantify mechanoelastic characteristics that are deformation invariant. Determination of such characteristics allows for developing methods offering an image of the undeformed body. General assumptions about the mechanoelastic properties of the bodies are stated which lead to two different approaches for obtaining bodies' deformation invariants. One was developed to spot a deformed body's neutral line and its cross sections, while the other solves deformation PDEs by performing a set of equivalent image operations on the deformed body images. Both of these processes may furnish a body-undeformed version from its deformed image. This was confirmed by obtaining the undeformed shape of deformed parasites, cells (protozoa), fibers, and human lips. In addition, the method has been applied to the important problem of parasite automatic classification from their microscopic images. To achieve this, we first apply the previous method to straighten the highly deformed parasites, and then, apply a dedicated curve classification method to the straightened parasite contours. It is demonstrated that essentially different deformations of the same parasite give rise to practically the same undeformed shape, thus confirming the consistency of the introduced methodology. Finally, the developed pattern recognition method classifies the unwrapped parasites into six families, with an accuracy rate of 97.6 percent.
这里介绍了一种新的方法，该方法利用任意弹性体变形实例的 2D 图像来量化具有变形不变性的力学特性。确定这些特性可以开发出提供未变形体图像的方法。本文陈述了关于物体的力学特性的一般假设，这些假设导致了两种获得物体变形不变量的不同方法。一种方法用于识别变形体的中性线及其横截面，而另一种方法则通过对变形体图像执行一组等效的图像操作来求解变形偏微分方程。这两种方法都可以从变形图像中提供未变形的物体。这一点通过获得变形寄生虫、细胞（原生动物）、纤维和人类嘴唇的未变形形状得到了证实。此外，该方法还应用于寄生虫自动分类这一重要问题，从它们的微观图像中进行分类。为此，我们首先应用前面的方法对高度变形的寄生虫进行拉直，然后对拉直的寄生虫轮廓应用专门的曲线分类方法。结果表明，同一寄生虫的不同变形实际上会产生相同的未变形形状，从而验证了所提出的方法的一致性。最后，所开发的模式识别方法将未包裹的寄生虫分为六个家族，准确率为 97.6%。
