Statistics – Applications
Scientific paper
Feb 1994
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1994spie.2103...20s&link_type=abstract
Proc. SPIE Vol. 2103, p. 20-29, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs, J. M. Se
Statistics
Applications
Scientific paper
Many digital imaging applications require the detection of subtle localized changes in a sequence of background scenes. Often the principle limitation to the process is uncontrollable pointing changes in an electro-optic sensor, which result in apparent image displacements in the sequence. The interpolation of one of the images followed by subtraction from another has served as a mainstay for change detection. This method is extremely suboptimal within the general context of linear filtering. Conventional registration/subtraction is replaced in this report by dual difference filtering (DDF), a symmetric method that generalizes the concept of interpolation. Over a wide range of images, DDFs have been shown to increase the signal to clutter ratio for small moving targets by an average of 31 dB, compared to older, interpolative methods. A fundamental optimization equation for DDFs is derived, and a solution is presented for a spatial spectrum typical of imagery. DDFs are shown to permit the identification of subtle differences in image sequences that were not detectable with previous methods. It is also shown that, in principle, all first-order aliasing errors can be eliminated by using DDFs. Applications include medical imaging, autonomous and cued surveillance, remote sensing, and astronomy.
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