Scale Invariant Object Detection using a Hybrid Genetic Algorithm – Fuzzy Logic Approach Group – 9 Ayesha Farrukh [ ] Junaid Akhtar [ ]

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 =

Tic-Toc This operation took 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 x = 49y = 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

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