Associative Memory for Noisy and Structurally Deformed Two-Dimensional Images Using Neural Networks

—This paper studies the problem of understanding noisy and structurally deformed two-dimensional images by means of abstractly defined neural works. First, in the framework of systems theory a neural network defined over a Hilbert space is introduced such that any given vectors in the Hilbert space are assigned to locally asymptotically stable fixed points of the network. Then, introducing structural deformation into images a modified neural network is constructed to remove such structural deformation as well as noise. Finally, the modified neural network is used for implementing associative memory of two-dimensional images corrupted by structural deformation as well as nose, and some numerical examples are presented to illustrate the result.

• Type: Conference
• Year: 2007
• Where: ICNSC
• Authors: Hiroshi Inaba, Tomoki Takahashi, Keylan Alimhan
• Keywords: Computer Networks, ICNSC 2007, Neural Network, Structural Deformation, Two-dimensional Images