NDLI: Automated gross and sub-pixel registration accuracy of visible and thermal imagery

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Automated gross and sub-pixel registration accuracy of visible and thermal imagery

Content Provider	ACM Digital Library
Author	Won, Stephen Seetharaman, Gunasekaran Young, S. Susan
Abstract	Tracking or improving resolution of images requires that objects of interests are aligned at every frame through registration. Three motion segmentation algorithms utilizing the image gradient and five motion estimation algorithms were tested to see which would produce the best registration process at the gross and subpixel level. To test the motion segmentation, a qualitative study was performed using visible and thermal imagery to see how accurately the area of motion could be identified without producing noise. Gross shift estimation accuracy was accessed by comparing estimated shifts against a ground truth. Sub-pixel shift estimation accuracy was assessed by a series of synthetically downsampled images. The initial evaluation of segmentation revealed that the flux tensor method performed the best with a fixed threshold. For gross-shift estimation accuracy on images with well-defined objects, feature correspondence yielded the most accurate results. For images with objects that are not well-defined, optical flow methods produce more accurate results. The sub-pixel shift estimation test revealed that correlation was the only method out of the four tested that could accurately estimate motion at the sub-pixel level.
Starting Page	209
Ending Page	213
Page Count	5
File Format	PDF
ISBN	9781450302906
DOI	10.1145/2377576.2377615
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-09-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Image registration Horn-schunck Super-resolution Lucas-kanade Sift Flux tensor Optical flow
Content Type	Text
Resource Type	Article

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