Efficient image matching using concentric sampling features and boosting process

This paper presents a novel template matching method to efficiently match and search image patterns. The method using concentric sampling structures, boosting process, and coarse-to-fine framework, differs from the traditional pattern matching schemes of time-exhausting correlation. The time complexity at searching stage is invariant to the dimension of concerned patterns. The rotation-invariant collection of concentric sub-samples represents as a reliable relaxation process of weak beliefs to efficiently reject the impossible location candidates. The concentric sampling approximation of integral images and the hierarchical scheme enable sifting out the patterns to process with the reduced complexity. Experimental result demonstrates the real-time performance on efficient pattern detection and geometry parameter estimation and the flexibility (on translation-, scaling-, and rotation-variant patterns) for various image analysis applications.