Entropic Graphs for Registration [chapter]

Huzefa Neemuchwala, Alfred Hero
2005 Signal Processing and Communications  
In many applications, fusion of images acquired via two or more sensors requires image alignment to an identical pose, a process called image registration. Image registration methods select a sequence of transformations to maximize an image similarity measure. Recently a new class of entropic-graph similarity measures was introduced for image registration, feature clustering and classification. This chapter provides an overview of entropic graphs in image registration and demonstrates their
more » ... ormance advantages relative to conventional similarity measures. In this chapter we introduce : techniques to extend image registration to higher dimension feature spaces using Rényi's generalized «-entropy. The «-entropy is estimated directly through continuous quasi additive power weighted graphs such as the minimal spanning tree (MST) and k-Nearest Neighbor graph (kNN). Entropic graph methods are further used to approximate similarity measures like the « mutual information, «-Jensen divergence, Henze-Penrose affinity and Geometric-Arithmetic mean affinity. These similarity measures offer robust registration benefits in a multisensor environment. Higher dimensional features used for this work include basis functions like multidimensional wavelets and independent component analysis (ICA). Registration is performed on a database of multisensor satellite images. Lastly, we demonstrate the sensitivity of our approach by matching local image regions in a multimodal medical imaging example.
doi:10.1201/9781420026986.ch6 fatcat:z6ib7bttzfdetbtxcuubemm6iq