Using radial basis functions to set thresholds for segmentation of signal from background on matched filter correlation surfaces

Richard Edmondson; Michael Rodgers

doi:10.1117/12.778083

14 April 2008 Using radial basis functions to set thresholds for segmentation of signal from background on matched filter correlation surfaces

Richard Edmondson, Michael Rodgers

Author Affiliations +

Proceedings Volume 6967, Automatic Target Recognition XVIII; 69670N (2008) https://doi.org/10.1117/12.778083
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Using matched filters to find targets in cluttered images is an old idea. Human operators can interactively find threshold values to be applied to the correlation surface that will do a good job of binarizing it into signal/non-signal pixel regions. Automating the thresholding process with nine measured image statistics is the goal of this paper. The nine values are the mean, maximum, and standard deviation of three images: the input image presumed to have some signal, an NxN matched filter kernel in the shape of the signal, and the correlation surface generated by convolving the input image with the matched filter kernel. Several thousand input images with known target locations and reference images were run through a correlator with kernels that resembled the targets. The nine numbers referred to above were calculated in addition to a threshold found with a time consuming brutal algorithm. Multidimensional radial basis functions were associated with each nine number set. The bump height corresponded to the threshold value. The bump location was within a nine dimensional hypercube corresponding to the nine numbers scaled so that all the data fell within the interval 0 to 1 on each axis. The sigma (sharpness of the radial basis function) was calculated as a fraction of the squared distance to the closest neighboring bump. A new threshold is calculated as a weighted sum of all the Gaussian bumps in the vicinity of the input 9D vector. The paper will conclude with a table of results using this method compared to other methods.

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Richard Edmondson and Michael Rodgers "Using radial basis functions to set thresholds for segmentation of signal from background on matched filter correlation surfaces", Proc. SPIE 6967, Automatic Target Recognition XVIII, 69670N (14 April 2008); https://doi.org/10.1117/12.778083

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KEYWORDS

Signal to noise ratio

Image filtering

Signal detection

Electronic filtering

Image segmentation

Neural networks

Image processing

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