Generalized partial volume: an inferior density estimator to Parzen windows for normalized mutual information
作者： Sune Darkner , Jon Sporring
DOI: 10.1007/978-3-642-22092-0_36
关键词: Mathematics 、 Computational complexity theory 、 Density estimation 、 Pattern recognition 、 Scale space 、 Mutual information 、 Similarity measure 、 Estimator 、 Kernel density estimation 、 Artificial intelligence 、 Image registration
摘要: Mutual Information (MI) and normalized mutual information (NMI) are popular choices as similarity measure for multimodal image registration. Presently, one of two approaches is often used estimating these measures: The Parzen Window (PW) the Generalized Partial Volume (GPV). Their theoretical relation has so far been unexplored. We present direct connection between PW GPV NMI in case rigid non-rigid Through step-by-step derivations we clarify difference show that algorithmically inferior to from a model point view well w.r.t. computational complexity. Finally, algorithms both which comparable speed Sum Squared Differences (SSD), illustrate differences on number registration examples.
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