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Τίτλος: A hybrid and hierarchical approach to aerial image registration
Συγγραφείς: Xu, Dongjiang 
Kasparis, Takis 
metadata.dc.contributor.other: Κασπαρής, Τάκης
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Image registration;Optical flow;Phase correlation;Gaussian/Laplacian pyramid
Ημερομηνία Έκδοσης: 1-Μαΐ-2007
Πηγή: International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), 2007, vol.21, no.3, pp. 573-590
Volume: 21
Issue: 3
Start page: 573
End page: 590
Περιοδικό: International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI) 
Περίληψη: This paper proposes a hybrid approach to image registration for inferring the affine transformation that best matches a pair of partially overlapping aerial images. The image registration is formulated as a two-stage hybrid approach combining both phase correlation method (PCME) and optical flow equation (OFE) based estimation algorithm in a coarse-to-fine manner. With PCME applied at the highest level of decomposition, the initial affine parameter model could be first estimated. Subsequently, the OFE-based estimation algorithm is incorporated into the proposed hybrid approach using a multi-resolution mechanism. PCME is characterized by its insensitivity to large geometric transform between images, which can effectively guide the OFE-based registration. For image pairs under salient brightness variations, we propose a nonlinear image representation that emphasizes common intensity information, suppresses the non-common information between an image pair, and is suitable for the proposed coarse-to-fine hierarchical iterative processing. Experimental results demonstrate the accuracy and efficiency of our proposed approach using different types of aerial images.
URI: https://hdl.handle.net/20.500.14279/1410
ISSN: 17936381
DOI: 10.1142/S0218001407005508
Rights: © World Scientific
Type: Article
Affiliation: University of Central Florida 
Affiliation: University of Central Florida 
Publication Type: Peer Reviewed
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