Scale Invariant Object Detection using a Hybrid Genetic Algorithm –

Fuzzy Logic Approach

Group – 9Ayesha Farrukh [04030004]

Junaid Akhtar [04030019]

Progress

Matlab Implementation – Brute force Template Matching

Initialize Population Crossover Mutation Fitness Function

Brute Force Results

Reference Image

Template

(145, 171)

Cross Correlation Surface

Correlation Surface [zoomed in]

Peak value at: (145, 171)Value = 0.9666

Tic-Toc

This operation took 152.01 seconds

Other Matlab Functions Explored

DEC2BIN BIN2DEC RAND CORR2

GA Pseudocode StartGA( img, template )

// Initialize a random population of individuals P = initPopulation ( populationSize, imgDimension, scaleDimension );

// Evaluate fitness of all initial individuals of population [fitnessP, fuzzyClassifictaionP ] = calcFitness ( P, img , template );

// test for termination criterion: (Correlation Value > THRESHOLD) WHILE not done do

// select a sub-population for offspring production selectedP = selectparents( P );

// Crossover [newP] = crossover( selectedP );

// Mutate x OR y OR scale value randomly [mutatedP] = mutate( newP );

// Evaluate fitness of new calcFitness( mutatedP );

// select the survivors from actual fitness P = survive( P, mutatedP );

END

Chromosome Definition

000110001 011111100 1001x = 49 y = 252 Index of Scale to

Use

512 x 512 Reference Image

Template Image

Scale

imresize Bicubic Interpolation for resizing the template

image

Initial Population function [initialPopulation, lengthImageDimension, lengthMaxScale] = initialize(populationSize, imageDimension,

maxScale)

binaryImageDimension = dec2bin(imageDimension); binaryMaxScale = dec2bin(maxScale);

lengthImageDimension = length(binaryImageDimension); lengthMaxScale = length(binaryMaxScale);

bitsInitialPopulation = (2 * lengthImageDimension) + lengthMaxScale; initialPopulation = zeros(populationSize, bitsInitialPopulation);

for i = 1:populationSize xLocation = round(imageDimension*rand); xLocation = dec2bin(xLocation); initialPopulation(i,length(binaryImageDimension) - length(xLocation) + 1 :length(binaryImageDimension)) = xLocation -

48; yLocation = round(imageDimension*rand); yLocation = dec2bin(yLocation); initialPopulation(i,(2*length(binaryImageDimension)) - length(yLocation) + 1 :2*length(binaryImageDimension)) =

yLocation - 48; scale = round(maxScale*rand); scale = dec2bin(scale); initialPopulation(i,bitsInitialPopulation - length(scale) + 1 : bitsInitialPopulation) = scale - 48; end

Select Parents

Sort the current Population according to their respective fitness values

Select the best

CalculateFitness

Use corr2 function to calculate fitness for each individual

Classify each individual using Fuzzy classifiers: Negative, Low, & Positive correlation

Crossover function [newPopulation] = crossover(selectedPopulation)

sizeSelectedPopulation = size(selectedPopulation,1); sizeIndividual = size(selectedPopulation,2);

count = 1;

while count < sizeSelectedPopulation crossoverPoint = round(sizeIndividual*rand) newPopulation(count,:) = selectedPopulation(count,:); newPopulation(count+1,:) = selectedPopulation(count+1,:); newPopulation(count,crossoverPoint:sizeIndividual) =

selectedPopulation(count+1,crossoverPoint:sizeIndividual); newPopulation(count+1,1:crossoverPoint-1) =

selectedPopulation(count,1:crossoverPoint-1); count = count + 2; end

Mutate( P )

For each member of the selected population ‘P’

Randomly decide whether or not to mutate

Randomly decide index of bit to mutate

Perform mutation

0011000111111100100100

0011000111111100100100

0011000101111100100100

Next Episode

Integrate the components Compile Results on Different Images Use information generated during fuzzy

classification to improve matching time Compare Tic-Toc Results for time Compare FLOPS for memory