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Evolving Logical-Linear Edge Detector with Evolutionary Algorithms

Evolving Logical-Linear Edge Detector with Evolutionary Algorithms. By Virin Jan. Agenda. Edge detection Evolutionary Algorithms My approach Results Conclusions. Edge detection - Definition.

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Evolving Logical-Linear Edge Detector with Evolutionary Algorithms

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  1. Evolving Logical-Linear Edge Detector with Evolutionary Algorithms By Virin Jan

  2. Agenda • Edge detection • Evolutionary Algorithms • My approach • Results • Conclusions

  3. Edge detection - Definition • The goal of edge detection is to mark the points in a digital image at which the luminous intensity changes sharply • Sharp changes in image properties usually reflect important events and changes in properties of the world

  4. Edge detection - Detectors • Thresholding • Prewitt • Sobel • Canny Many false-positives • LL detectors

  5. Edge detection – LL detectors • Combines linear operator with Boolean logics • Conjunction of linear properties The goal: • More intelligent edge detection

  6. Edge detection – LL detectors • After applying linear operators on the image, use the following ♠ operator in order to enhance the result.

  7. Edge detection – LL detectors 5 8

  8. Evolutionary Algorithms • In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based optimizationalgorithm • An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, natural selection and survival of the fittest

  9. Recombination (Crossover) Selection Mutation (low rate) Evaluation (fitness function) Evolutionary Algorithms Chromosome representation Fitness function

  10. My approach - Individuals • LL operator consists of two linear filters 3x3 • It is encoded in a vector with 18 values (9+9) • Values range [ -7 , 7 ] • Population size is 100 individuals

  11. My approach – Fitness function • Uses the Berkley Segmentation Dataset and Benchmark: http://www.cs.berkeley.edu/projects/vision/grouping/segbench/ • Computes difference between result of applied individual and the benchmark • Less difference – better individual

  12. Results • Execution time = ~11 hours • Consistent improving • The final individual: 5,2,-5,-5,-7,4,-2,2,7,4,3,-5,-4,3,0,2,-3,-2 • The filters which are represented by it: 5 -5 -2 2 -7 2 -5 4 7 4 -4 2 3 3 -3 -5 0 -2

  13. Results - Images

  14. Conclusions • Both filters are something like edge detectors • Each of them detect edges but with false-positives • When the Boolean Logic is applied, the noise is reduced, because there is a small possibility that there is noise in the same pixel in both images • There is future work to do

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