1 / 14

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.

veronicaw
Download Presentation

Evolving Logical-Linear Edge Detector with Evolutionary Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related