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FAninal aco
Computer Science
Presented By
Golam Morshed Maruf
Red Blood Cell Image segmentation based on Ant Colony
Optimization
ACO
Outline
• Introduction.
• Natural behavior of ant.
• ‹Edge detection Model.
• ACO based edge Detection.
• Experimental Result
ACO
Introduction
ACO
Introduction
ACO
Introduction
Introduction
How Ants
Move
Actually ??
ACO
Introduction
Overview:
 In the real world, ants (initially) wander randomly, and upon
finding food return to their colony while laying down
pheromone trails.
 Over time, however, the pheromone trail starts to evaporate,
thus reducing its attractive strength.
 In a short path pheromone density remains high.
 Thus, when one ant finds a good (i.e., short) path from the
colony to a food source, other ants are more likely to follow
that path.
ACO
Natural behavior of ant
Ant Algorithms – (P.Koumoutsakos – based on notes L. Gamberdella (www.idsia.ch)
ACO
Natural behavior of ant
ACO
Edge Detection Model
ACO algorithm
Initialize
SCHEDULE_ACTIVITIES
Construct Ant Solutions
Do Daemon Actions (optional)
Update Pheromones
END_SCHEDULE_ACTIVITIES
ACO
Edge Detection Model
Initialize:
Set the parameters and assigning the initial pheromone value.
Schedule Activities:
1. Construct Ant Solutions:
• Here, τij(t) represents quality of pheromone on the edge.
• ηij represents the heuristic information.
 
   
   









 
otherwise
allowedki
t
t
tp
k
allowedk
ijij
ijij
k
ij
k
0
f
)(
)(




Where
to GO??
ACO
Edge Detection Model
2. Do Daemon Actions:
Performed by multiple ants to improve the solution or search
process.
3. Update Pheromones:
The goal of the pheromone update is to increase the
pheromone values associated with good solutions and
decrease those associated with bad ones.
Update is done by:
τij(t + n) = р × τij(t) + ∆ τij
here p, is pheromone evaporation rate and ∆ τij is the
quantity of pheromone laid on edge.
ACO
ACO-based Image Edge Detection
0,0 1,0 2,0 W-1,0
0,1 1,1 2,1 W-1,1
0,h-1 1,h-1 2,h-1
W-
1,h-1
ACO
ACO-based Image Edge Detection
• A pixel is connected to every pixel that touches one
of its edges or corners.
• An ant cannot move to a pixel if it is not
connected to the pixel where the ant is
currently located.
• An ant can move only to an adjacent pixel.
ACO
ACO-based Image Edge Detection
• Artificial ants are distributed over the image.
• The goal is to construct a final pheromone matrix that reflects
the edge information.
• Each element in the pheromone matrix corresponds to a
pixel in the image and indicates whether a pixel is an edge
or not.
i-1,j-1 i-1,j i-1,j+1
i,j-1 i,j i,j+1
i+1,j-1 i+1,j i+1,j+1
ACO
ACO-based Image Edge Detection
1. Initialization Process :
• K ants are assigned random positions in the M1 X M2 image.
• The initial value of each element in the pheromone matrix
is set to a constant τinit.
• The heuristic information at pixel (i,j) is determined by the
local statistics at that position:
• Here Ii,j is the intensity value at (i,j), and
max
,
,
)(
v
Iv jic
ji 
1,1,1,11,1,1,11,11,1, )(   jijijijijijijijijic IIIIIIIIIv
i-1,j-1 i-1,j i-1,j+1
i,j-1 i,j i,j+1
i+1,j-1 i+1,j i+1,j+1
ACO
ACO-based Image Edge Detection
2. Iterative Construction and Update Process:
• On every iteration, an ant moves from the pixel to an
adjacent pixel according to the pseudorandom proportional
rule.
• Each time an ant visits a pixel, it immediately performs a
local update on the associated pheromone.
• The amount of pheromone on the pixel on the iteration,
is updated based on the equation for ACS local
pheromone update.
init
n
ji
n
ji  .).1( )(
,
)(
, 
ACO
ACO-based Image Edge Detection
2. Iterative Construction and Update Process:
After all the ants finish the construction process, global
pheromone update is performed on pixels that have been
visited by at least one ant:
Here, is the amount of pheromone deposited by each ant
on each pixel.
)(
,
)1(
,
)(
,
1
.).1( K
ji
n
ji
n
ji
K
K
 

 
)(
,
K
ji
ACO
ACO-based Image Edge Detection
3. Decision Process:
• The final pheromone matrix is used to classify each pixel either as an
edge or a non-edge.
• The decision is made by applying a threshold on the final pheromone
matrix.
Do initialization procedures
for each iteration n = 1:N do
for each construction_step l = 1:L do
for each ant k = 1:K do
Select and go to next pixel
Update pixel’s pheromone (local)
end
end
Update visited pixels’ pheromones (global)
end
ACO
Experimental Results
ACO
Experimental Results
Experimental Results
Thank You
CALIC
FAninal aco

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FAninal aco

  • 2. Computer Science Presented By Golam Morshed Maruf Red Blood Cell Image segmentation based on Ant Colony Optimization
  • 3. ACO Outline • Introduction. • Natural behavior of ant. • ‹Edge detection Model. • ACO based edge Detection. • Experimental Result
  • 8. ACO Introduction Overview:  In the real world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails.  Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength.  In a short path pheromone density remains high.  Thus, when one ant finds a good (i.e., short) path from the colony to a food source, other ants are more likely to follow that path.
  • 9. ACO Natural behavior of ant Ant Algorithms – (P.Koumoutsakos – based on notes L. Gamberdella (www.idsia.ch)
  • 11. ACO Edge Detection Model ACO algorithm Initialize SCHEDULE_ACTIVITIES Construct Ant Solutions Do Daemon Actions (optional) Update Pheromones END_SCHEDULE_ACTIVITIES
  • 12. ACO Edge Detection Model Initialize: Set the parameters and assigning the initial pheromone value. Schedule Activities: 1. Construct Ant Solutions: • Here, τij(t) represents quality of pheromone on the edge. • ηij represents the heuristic information.                      otherwise allowedki t t tp k allowedk ijij ijij k ij k 0 f )( )(     Where to GO??
  • 13. ACO Edge Detection Model 2. Do Daemon Actions: Performed by multiple ants to improve the solution or search process. 3. Update Pheromones: The goal of the pheromone update is to increase the pheromone values associated with good solutions and decrease those associated with bad ones. Update is done by: τij(t + n) = р × τij(t) + ∆ τij here p, is pheromone evaporation rate and ∆ τij is the quantity of pheromone laid on edge.
  • 14. ACO ACO-based Image Edge Detection 0,0 1,0 2,0 W-1,0 0,1 1,1 2,1 W-1,1 0,h-1 1,h-1 2,h-1 W- 1,h-1
  • 15. ACO ACO-based Image Edge Detection • A pixel is connected to every pixel that touches one of its edges or corners. • An ant cannot move to a pixel if it is not connected to the pixel where the ant is currently located. • An ant can move only to an adjacent pixel.
  • 16. ACO ACO-based Image Edge Detection • Artificial ants are distributed over the image. • The goal is to construct a final pheromone matrix that reflects the edge information. • Each element in the pheromone matrix corresponds to a pixel in the image and indicates whether a pixel is an edge or not. i-1,j-1 i-1,j i-1,j+1 i,j-1 i,j i,j+1 i+1,j-1 i+1,j i+1,j+1
  • 17. ACO ACO-based Image Edge Detection 1. Initialization Process : • K ants are assigned random positions in the M1 X M2 image. • The initial value of each element in the pheromone matrix is set to a constant τinit. • The heuristic information at pixel (i,j) is determined by the local statistics at that position: • Here Ii,j is the intensity value at (i,j), and max , , )( v Iv jic ji  1,1,1,11,1,1,11,11,1, )(   jijijijijijijijijic IIIIIIIIIv i-1,j-1 i-1,j i-1,j+1 i,j-1 i,j i,j+1 i+1,j-1 i+1,j i+1,j+1
  • 18. ACO ACO-based Image Edge Detection 2. Iterative Construction and Update Process: • On every iteration, an ant moves from the pixel to an adjacent pixel according to the pseudorandom proportional rule. • Each time an ant visits a pixel, it immediately performs a local update on the associated pheromone. • The amount of pheromone on the pixel on the iteration, is updated based on the equation for ACS local pheromone update. init n ji n ji  .).1( )( , )( , 
  • 19. ACO ACO-based Image Edge Detection 2. Iterative Construction and Update Process: After all the ants finish the construction process, global pheromone update is performed on pixels that have been visited by at least one ant: Here, is the amount of pheromone deposited by each ant on each pixel. )( , )1( , )( , 1 .).1( K ji n ji n ji K K      )( , K ji
  • 20. ACO ACO-based Image Edge Detection 3. Decision Process: • The final pheromone matrix is used to classify each pixel either as an edge or a non-edge. • The decision is made by applying a threshold on the final pheromone matrix. Do initialization procedures for each iteration n = 1:N do for each construction_step l = 1:L do for each ant k = 1:K do Select and go to next pixel Update pixel’s pheromone (local) end end Update visited pixels’ pheromones (global) end