A MapReduce framework to improve template matching uncertainty

Normalized cross-correlation template matching is used as a detection method in many scientific domains. To be practical, template matching must scale to large datasets while handling ambiguity, uncertainty, and noisy data. We propose a novel approach based on Dempster-Shafer (DS) Theory and MapReduce parallelism. DS Theory addresses conflicts between data sources, noisy data, and uncertainty, but is traditionally serial. However, we use the commutative and associative nature of Dempster's Combination Rule to perform a parallel computation of DS masses and a logarithmic hierarchical fusion of these DS masses. This parallelism is particularly important because additional data sources allow DS-based template matching to maintain accuracy and refine uncertainty in the face of noisy data. We validate the parallelism, accuracy, and uncertainty of our implementation as a function of the size and noise of the input dataset, finding that it scales linearly and can retain accuracy and improve uncertainty in the face of noise for large datasets.