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18 May 2006 One-dimensional fractal error for motion detection in an image sequence
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Abstract
A novel approach to motion detection in an image sequence is presented. This new approach computes a one-dimensional version of the fractal error metric applied temporally across each pixel. The original fractal error algorithm was developed by Cooper et al. as a two-dimensional metric for detecting man-made features in a single image using only spatial information. The fractal error metric is based on the observed propensity of natural image features to fit a fractional Brownian motion (fBm) model well, thus producing a small fractal error. On the other hand, man-made features do not fit the fBm model well and therefore produce a larger fractal error. Jansing et al. showed that spatial edges typically do not fit the fBm model due to their irregularity. The one-dimensional implementation of the algorithm presented in this paper exploits the irregularity of edges in a temporal signal, which are typically caused by moving objects. Emphasis is placed on moving target detection in the presence of noise and clutter-induced motion. Results are demonstrated using mid-wave infrared (MWIR) image sequences.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian S. Allen and E. David Jansing "One-dimensional fractal error for motion detection in an image sequence", Proc. SPIE 6234, Automatic Target Recognition XVI, 623412 (18 May 2006); https://doi.org/10.1117/12.665503
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