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Hybrid neural- fuzzy analysis Harvey Cohen Achan (Software) harveycohen@aanet.com.au. A case study based on edge detection in image processing. continued. What is fuzzy-neural PR ? Approach of Bezdek How to go beyond Thoughts for future. Membership fns = a priori probability
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Hybrid neural- fuzzy analysis Harvey CohenAchan (Software)harveycohen@aanet.com.au
A case study based on edge detection in image processing.continued • What is fuzzy-neural PR ? • Approach of Bezdek • How to go beyond • Thoughts for future
Membership fns = a priori probability Rules for combining Predictions after defuzzification NN with hidden layers Trained on prototypes Sigmoids Outputs perhaps fuzzy Fuzzy V Neural
Binary 3x3 Prototypes 8 non-central locations 28 /2 = 128
Sobel Edge Detector Assigns numeric value 0 -1 to each pixel in image • Usually thresholded at about 0.65 • Natural “edgedness” membership fn
Bezdek et al • Neural-fuzzy edge detector • Train NN to give same values as Sobel for ALL (=128) binary prototypes • Good results
512 (!) 3x3 binary exemplars NN trained 2 min f0r Sobel 225 5x5 binary exemplars NN training will take 45 days no possible application to large scale features as in biology
But worse – have assumed N linearity – On 3x3 Sobel scores have only 4 values, but larger scale operators have many values in range 0 ..1
One idea – in previous paper (DICTA NZ 1997) – score to crisp values: speeds up computation greatly, yet has similar output for fuzzy neural 3x3.
Train on small number of super quality artificial (=binary) exemplars plus 1000scored ‘natural’ examples
Eclipse over Africa Frames from MeteoSat6, June 21, 2001