A probabilistic approach to pattern-matching based on non-linear parameter optimization

This paper presents a concept for pattern-matching based on a parameter optimization system for optimization of constraints. The concept uses a non-linear parameter optimization method with an iterative variation of parameters. Boundary conditions and constraints are expressed as rules, managed by a specific rule engine. The method is applicable to a wide range of pattern-matching problems due to its dynamically parametrized restrictions. Pattern-matching is integrated in several applications in various scopes, such as gaming, audio, character recognition or augmented reality.