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Proceedings Paper

Sub-pixel registration of moving objects in visible and thermal imagery with adaptive segmentation
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Paper Abstract

Sub-pixel registration is critical in object tracking and image super-resolution. Motion segmentation algorithms using the gradient can be applied prior to image registration to improve its accuracy and computational runtime. This paper proposes a new segmentation method that is adaptive variation segmentation in the form of local variances taken at different block sizes to be applied to the sum of absolute image differences. In this paper, two motion segmentation and four image registration methods are tested to optimize the registration accuracy in visible and thermal imagery. Two motion segmentation methods, flux tensor and adaptive variation segmentation, are quantitatively tested by comparing calculated regions of movement with accepted areas of motion. Four image registration methods, including two optical flow, feature correspondence, and correlation methods, are tested in two steps: gross shift and sub-pixel shift estimations. Gross shift estimation accuracy is assessed by comparing estimated shifts against a ground truth. Sub-pixel shift estimation accuracy is assessed by simulated, down-sampled images. Evaluations show that the best segmentation results are achieved using either the flux tensor or adaptive segmentation methods. For well-defined objects, feature correspondence and correlation registration produce the most accurate gross shift registrations. For not well-defined objects, the correlation method produces the most accurate gross and sub-pixel shift registration.

Paper Details

Date Published: 5 May 2011
PDF: 9 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80501A (5 May 2011); doi: 10.1117/12.883262
Show Author Affiliations
Stephen Won, U.S. Army Research Lab. (United States)
Susan Young, U.S. Army Research Lab. (United States)
Gunasekaran Seetharaman, Air Force Research Lab. (United States)
Kannappan Palaniappan, Univ. of Missouri-Columbia (United States)

Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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