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

Agenda Edge detection Evolutionary Algorithms My approach Results Conclusions

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

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

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

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

Edge detection – LL detectors 5 8

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

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

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

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

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

Results - Images

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