Method and system for implementing N-dimensional object recognition using dynamic adaptive recognition layers

Abstract

In a method and a system for the implementation of multi-layered network object recognition in N-dimensional space, the structure of a neural recognition network is dynamically generated and adapted to recognize an object. The layers of the network are capable of recognizing key features of the input data by using evaluation rules to establish a hierarchical structure that can adapt to data position and orientation, varying data densities, geometrical scaling, and faulty or missing data.

1 . A method for implementing object recognition in N-dimensional space by means of a network having multiple layers, said method comprising the following: 
 The structure of the layers is hierarchical in that the layers are ordered and layers that are higher in the hierarchical order have cells connected by, links representing ownership of cells contained by layers that are lower in the hierarchical order;    The layers are assigned certain key features which the cells in the respective layers are capable of recognizing and representing;    The layers are dynamic in size and in structure in that member cells may be created or destroyed and links between cells of adjoining layers may be created or destroyed so as to adapt to input data to be recognized;    The layers are equipped with a rule for determining whether cells from subordinate layers should be included in receptive fields of cells from higher layers;    The layer cells are equipped with a polarization vector which serves to determine the compatibility of said cells with cells of neighboring layers;    The network is adapted through an iterative process in which cell ownership is modified and cells are created or destroyed to converge the state of the cells to a final persistent stable state of mutual reinforcement which represents a solution to the recognition problem.    
   
   
       2 . A method according to  claim 1 , wherein links representing ownership relationships between cells of differing layers are created not just between adjacent layers but also between non-adjacent layers to assist in the recognition process.  
   
   
       3 . A method according to  claim 1 , wherein the arrangement of the layers is not linear, but is itself a branched network.  
   
   
       4 . A method according to  claim 1 , wherein the implementation of various features is selectively distributed among multiple hardware or software processing systems for improved performance.  
   
   
       5 . A system for implementing object recognition in N-dimensional space by means of a network having multiple layers, said system comprising the following; 
 The structure of the layers is hierarchical in that the layers are ordered and layers that are higher in the hierarchical order have cells connected by links representing ownership of cells contained by layers that are lower in the hierarchical order;    The layers are assigned certain key features which the cells in the respective layers are capable of recognizing and representing;    The layers are dynamic in size and in structure in that member cells may be created or destroyed and links between cells of adjoining layers may be created or destroyed so as to adapt to input data to be recognized;    The layers are equipped with a rule for determining whether cells from subordinate layers should be included in receptive fields of cells from higher layers;    The layer cells are equipped with a polarization vector which serves to determine the compatibility of said cells with cells of neighboring layers;    The network is adapted through an iterative process in which cell ownership is modified and cells are created or destroyed to converge the state of the cells to a final persistent stable state of mutual reinforcement which represents a solution to the recognition problem.    
   
   
       6 . A system according to  claim 5 , wherein links representing ownership relationships between cells of differing layers are created not just between adjacent layers but also between non-adjacent layers to assist in the recognition process.  
   
   
       7 . A system according to  claim 5 , wherein the arrangement of the layers is not linear, but is itself a branched network.  
   
   
       8 . A system according to  claim 5 , wherein the implementation of various features is selectively distributed among multiple hard- or software processing systems for improved performance.