论文标题
使用预测模块化神经网络不变的3D形状识别
Invariant 3D Shape Recognition using Predictive Modular Neural Networks
论文作者
论文摘要
在本文中，PremOnn（预测模块化神经网络）模型/体系结构被推广到两个变量和非欧几里得空间的函数。它是在3D不变形状识别和纹理识别的上下文中提出的。 Premonn使用局部关系，它是模块化的，并且展示了增量学习。识别过程可以从形状或纹理上的任何点开始，因此不需要参考点。它的局部关系特征即使在遮挡的情况下，它也能够识别形状和纹理。分析主要是数学。但是，我们提出了一些实验结果。本文介绍的方法可以应用于许多问题，例如手势识别，动作识别，动态纹理识别等。
In this paper PREMONN (PREdictive MOdular Neural Networks) model/architecture is generalized to functions of two variables and to non-Euclidean spaces. It is presented in the context of 3D invariant shape recognition and texture recognition. PREMONN uses local relation, it is modular and exhibits incremental learning. The recognition process can start at any point on a shape or texture, so a reference point is not needed. Its local relation characteristic enables it to recognize shape and texture even in presence of occlusion. The analysis is mainly mathematical. However, we present some experimental results. The methods presented in this paper can be applied to many problems such as gesture recognition, action recognition, dynamic texture recognition etc.
