Invariant 3D Shape Recognition using Predictive Modular Neural Networks

TL;DR

This paper introduces PREMONN, a modular neural network architecture capable of invariant 3D shape and texture recognition, adaptable to non-Euclidean spaces, with robustness to occlusion and incremental learning.

Contribution

It generalizes predictive modular neural networks to functions of two variables and non-Euclidean spaces, enabling invariant recognition without reference points.

Findings

Effective recognition despite occlusion

Supports incremental learning

Applicable to various recognition tasks

Abstract

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.