Pattern Axioms

I have been fooling with a new way to locate and recognize objects that assumes a concept of "pattern" where every pattern has 

• a frame of reference attachment method given any point of the data
• one or more measurements made within that frame of reference
• a set of idealized object or models, parametrized and positioned in the data space by those measurements.
• a distance metric between points in the data space

Here is how it works, given a data point. 

1. Attach the frame of reference
2. Make the measurements, use them to find and locate one or more models
3. Find the one model closest to the data. 

In this framework the models become methods of attachment for more detailed measurements, and thereby exist in a hierarchy as nodes in a tree.