CarsonScott/Adaptive-Template-Model

Traditionally, template-matching algorithms have been used for things like digital image processing and visual pattern recognition. One simple example of this deals with taking a (typically very small) two-dimensional filter and sliding it across an image in order to detect low-level patterns of black-and-white pixels. Pattern recognition through template-matching is currently restricted in that it is only useful when dealing with vector spaces. However, problems of high complexity tend to deal with conceptually abstract relations and not with patterns dependent on space-time. In the following framework, I propose a feasible solution for extending template-matching methods to topological space, like graphs and networks.