Transcript
Computer Science
Presented ByGolam 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
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Introduction
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Introduction
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Introduction
Introduction
How Ants Move
Actually ??
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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.
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Natural behavior of ant
Ant Algorithms – (P.Koumoutsakos – based on notes L. Gamberdella (www.idsia.ch)
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Edge Detection Model
ACO algorithm
Initialize
SCHEDULE_ACTIVITIESConstruct Ant SolutionsDo Daemon Actions (optional)Update Pheromones
END_SCHEDULE_ACTIVITIES
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Edge Detection ModelInitialize: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
allowedkit
t
tpk
allowedkijij
ijij
kij
k
0
f)(
)(
Where to GO??
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Edge Detection Model2. 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 Detection1. 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 jicji
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.
initnji
nji .).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( Kji
nji
nji K
K
)(,Kji
ACO
ACO-based Image Edge Detection3. 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 proceduresfor each iteration n = 1:N do
for each construction_step l = 1:L dofor each ant k = 1:K doSelect and go to next pixelUpdate pixel’s pheromone (local)
endendUpdate visited pixels’ pheromones (global)
end
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Experimental Results
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Experimental Results
Experimental Results
Thank You
CALIC
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