A New Neural Network Architecture for Rotationally Invariant
نویسنده
چکیده
This paper i n d u c e s a new neural network architecture for rotationally invariant object recognition. Second-order neurons are used in combination with polar sampling to obtain invariance without incurring excessive network size. Multiple experiments are presented demonsmting that incorporation of a variable range of rotational invariance results in impved performance over existing methods.
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