Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1410
Title: | A hybrid and hierarchical approach to aerial image registration |
Authors: | 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 |
Keywords: | Image registration;Optical flow;Phase correlation;Gaussian/Laplacian pyramid |
Issue Date: | 1-May-2007 |
Source: | 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 |
Journal: | International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI) |
Abstract: | 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 |
Appears in Collections: | Άρθρα/Articles |
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