A neural 3-D object recognition architecture using optimized Gabor filters

Abstract

We present an object recognition architecture based on feature extraction by Gabor filter kernels and feature classification by an artificial neural network. The parameters of the Gabor filters are optimized to the specific problem by minimizing an energy function. Such Gabor filters extract features that can be more easily classified by the neural network. Moreover, the feature space is low-dimensional so feature extraction does not require much computational effort. The object recognition system is implemented on a Datacube and works in real-time.