Learn More
A nigher-order neural network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the arcnitecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one View of each object class, not numerous(More)
The authors describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096x4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten(More)
The authors demonstrate a second-order neural network that has learned to distinguish between two objects, regardless of their size or translational position, after being trained on only one view of each object. Using an image size of 16*16 pixels, the training took less than 1 min of run time on a Sun 3 workstation. A recognition accuracy of 100% was(More)
  • 1