Hybrid neural- fuzzy analysis Harvey Cohen Achan (Software)

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Presentation transcript:

Hybrid neural- fuzzy analysis Harvey Cohen Achan (Software)

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

Fuzzy V Neural Membership fns = a priori probability Rules for combining Predictions after defuzzification NN with hidden layers Trained on prototypes Sigmoids Outputs perhaps fuzzy

NN: Role of Sigmoid Fns

Binary 3x3 Prototypes 8 non-central locations 2 8 / 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

Harvey A Cohen

Achan (Software) Pty Ltd.

Bezdek Fuzzy- Neural Sobel

Cohen-McKinnon FuzzyNN Sobel

512 (!) 3x3 binary exemplars NN trained 2 min f0r Sobel x5 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 1000 scored ‘natural’ examples

5x5 exemplars for Plessy

Around Harvey

Eclipse over Africa Frames from MeteoSat6, June 21, 2001