Binarization of Noisy Images (KSC-12490)

NASA's fuzzy reasoning adaptive thresholding (FRAT) system is ideal for binarizing noisy, cluttered or textured gray-scale images. Using a faster computational technique that improves on previous fuzzy entropy functions, FRAT is faster and more reliable than other current, highly reliable methods. FRAT defines an image as an array of fuzzy singletons corresponding to image pixels. With two classes, background and foreground, the membership function is built based on the average gray level of each class, which is computed using the gray-level histogram as average weight factor. By using unrestricted range and a straightforward triangular-type membership function, FRAT takes advantage of a simple linear function as the basis for its entropy measure. The entropy measure is then used as a cost function for the selection of the optimal image threshold. FRAT is part of a critical NASA system used to identify and track foreign object debris (FOD) during Space Shuttle liftoffs. FRAT is also a key analysis tool used in the current investigation into the Space Shuttle Columbia tragedy.

FRAT feature include:

Exploitation of image pixel value histogram to avoid dealing with the individual pixels Use of entropy measure as the criteria fro selection of optimal threshold value Improvement on membership function to achieve more reliable and faster results

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