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 detectors5 8

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

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

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