This document describes the evolution of models for simulating ant colony behavior and its application to image processing techniques called pherographia. It begins with the original 1995 ant model by Chialvo and Millonas and describes subsequent extensions by other researchers to apply it to digital images. These include adding terms for pheromone deposition rates and ant reproduction. It also summarizes the development of color pherographia using different color spaces and rules for ant movement. The document concludes by discussing results and potential future work extending the techniques.

1. From Pherographia to Color Pherographia ColorSketchingwith Artificial Ants C.M. Fernandes12 C. Isidoro2 F. Barata2 J.J. Merelo1 A.C. Rosa2 1University of Granada 2Technical UniversityofLisbon

2. Summary 2 Original b/w photo Pheromonedensityafter 100 iterations Antsafter 100 iterations The Ant System: from Chialvo and Millonas’s Ant Model to Pherographia. Artwork created with monochromatic pherographia. Color Pherographia (four variations). Results. Conclusions and future work. CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011

4. A population of ants is randomly distributed in a two-dimensional array.

5. The ants move one step (cell) in each iteration, following simple rules.

6. Global and complex behaviour emerges from the simple rules and from the indirect interaction of the ants via the environment.

7. Self-Organization

8. Stigmergy

10. The Original Model CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 5

11. Ramos and Almeida’s Model 6 Ramos and Almeida, Artificial AntColoniesin Digital Images Habitat, ANTS 2000 - Instead of constant pheromone deposition rate, a term not constant is included: Constant Gives a measure of similarity between two different lattice windows, in terms of grey level spatial arrangement. CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011

12. Final Model 7 Fernandes, Ramos and Rosa, Self-Regulated Artificial AntColonieson Digital Image Habitats, InternationalJournalof Lateral Computing, 2005 An “evolutionary” component is added to the ant system. Each ants has an initial energy that decreases in each time step: the probability of surviving depends on the energy: P = 1-e(a) Each ant is allowed to reproduce in each time step. The reproduction probability depends on the number of neighbouring ants and the pheromone density. W(0) = W(8) =0; W(4) = 1; W(5) = W(3) =0.75; W(6) = W(2) =0.5; W(7) = W(1) = 0.25. CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011

13. Timor Mortis Conturbat Me (2008) “Timor Mortis...” was exhibited at the P4Photography art gallery, in Lisbon. About this work: http://carlosmfernandes.com/index_archivos/Page768.htm CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 8

14. EadweardMuybridge(1830-1904) CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 9

15. The Horse and the Ants (2009) The Horse and the Ants has been exhibited in several art and science shows. CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 10

16. Criatividade Computacional, ISCTE, Abril de 2010 11

17. Studies for a Modern Zoetrope (2011) CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 12

18. Becher’s typologies: analysis and synthesis CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 13 Bernd e HillaBecher Idris Kahn

19. Becher’stipologies: analysis and synthesis CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 14

21. In b/w pherography, ∆ measuresthecontrastintheregionaroundtheant’sposition.

22. In colorpherography:

23. First, RBG isconverted to L, a and b (Labcolorspace)

24. L measuresthelightness

25. a and b measurethecolor.

26. ∆ istheEuclideandistancebetweentheaverageof L, a and b, eachaveragedoverthecellandits 8 neighborigcells, andtheaverageof L, a and b averagedoverthepreviouscellandits 8 neighboringcells. CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011

27. ColorPherography Four variations were tested, each one with different rules for the ants movements and for handling occupied cells Originally, the objective was to remove a bias introduced by constraint of having no more than one ant in each cell. CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 16

28. ColorPherography CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 17 Variation 1: when an ant tries to move to an occupied cell, the ant that occupies that cell is moved in the same direction and its directional vector changes.

29. ColorPherography CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 18 Variation 2: the ants are not allowed to move to occupied cells. If one tries to move to an occupied cell, it will stop, and the direction of the ant occupying that cell changes to that of the vectorial sum between the original direction and the one towards which this ant tried to move.

30. ColorPherography CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 19 Variation 3: ants “bounce off” if they try to move to occupied cells.

31. ColorPherography CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 20 Variation 4: introduces speed and implements ants with variable speed, that is, the speed may change when an ant moves to a cell occupied by another ant.

32. CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 21

33. CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 22

34. Conclusions and Future Work The monochromatic pherographia has been extended to colorpherographia. Colorpherographia also detects the edges of the image. Investigate other local and simple rules that generate different global behaviour. Emergence, memory and readaptation (videos). Implement pherographia (B/W and color) in a digital camera using LUA programming language. CongressonEvolutionaryComputation, CEC’11, NewOrleans, USA, 2011 23