Hidden Markov Random Fields and Swarm Particles: a Winning Combination in Image Segmentation

Hidden Markov Random Fields and Swarm Particles: a Winning Combination in Image Segmentation

2014 International Conference on Future

Information Engineering

EL-Hachemi Guerrout,Teacher at ESI

Samy Ait-Aoudia, Professor at ESI

Ramdane Mahiou, Teacher at ESI

The goal of segmentation:

To simplify the representation of an image

The image became more meaningful and easier to analyze

Introduction- Why the segmentation ?

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Medical imagingLocate tumors Diagnosis pathologies…

Object detection Pedestrian detectionFace detectionLocate objects in satellite images (roads, forests, crops, etc.)….

Recognition Tasks Face recognitionFingerprint recognitionIris recognition

…..Video surveillanceCompression (Video, image)Traffic control systems…..

Application Domains

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1 23 4

2C,s

2s ),(2-(1)2ln(2

)²-(yy)(x,

ttsx

Ss x

x xxTs

s

s

y)(x,minarg Xx

x

Y: Observed Image

X: Hidden Image

HMRF provides an elegant way to model the segmentation problem

This elegant model leads to an optimization problem

Our new proposed approach based on PSO

We looking for The Hidden Image where :

Hidden Markov Random Filed

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Particles Swarm Optimization Particle related to bird flocking or fish schooling

what's the strategy to find the food?

A group of Particles are randomly searching food in an area

Each particle has a velocity and position

The next position of each particle is influenced by:

The best position, visited by itselfThe best position, visited by all particles

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Particles Swarm Optimization Advantages

Very efficient global search algorithmSimple implementationEasily parallelized for concurrent processing

Disadvantages

How to choose parameters ?

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HMRF-PSO methodExperimental Results-(1)

Our tests based on Non Destructive Testing (NDT) DataSet

Examples of NDT application:

Ultrasonic inspection of defective aircraft materials such as

carbon fiber reinforced (CFRP) composites

Thermal inspection of glass-fiber reinforced (GFRP)

Eddy current inspection of aircraft wheel fuselage cracks

Inspection of coating depth of steel plates

Observation of surface roughness of metals and ceramics

Defects in printed circuit board images

In textile image7

(a) (b) (c) (d) (e) (f)

Here some NDT images to segment used in our tests

HMRF-PSO methodExperimental Results-(2)

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(a) (b) (c) (d) (e) (f)

Ground truth images (original images) of the images listed before

(a) (b) (c) (d) (e) (f)

Here we list the segmented images (tested images) using HMRF-PSO for the parameters:size=80, c1=0.7, c2=0.8, w=0.7, vmax=5, iteration_number=100 and B=1

HMRF-PSO methodExperimental Results-(3)

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Misclassification Error (ME) ME is used to evaluate the quality of segmentation

ME =0 The best case ME=1 The worst case

F

FF1ME

BO and FO denote the background and foreground of the original (ground-truth) imageBT and FT denote background and foreground of the segmented image

HMRF-PSO methodExperimental Results-(4)

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HMRF-PSO methodExperimental Results-(5)

Method Image (a) Image (b) Image (c) Image (d) Image (e) Image (f)Abutaleb 0.023 0.310 0.023 0.024 0.250 0.620Kittler-Ill. 0.000 0.003 0.037 0.008 0.025 0.028Kapur et al. 0.003 0.004 0.028 0.036 0.220 0.620Tsai 0.240 0.170 0.350 0.290 0.084 0.280Li & Lee 0.490 0.550 0.450 0.710 0.021 0.020Pham 0.460 0.560 0.021 0.760 0.048 0.250SemiV 0.003 0.004 0.026 0.018 0.062 0.160Otsu 0.462 0.513 0.413 0.021 0.037 0.074Median extension 0.462 0.527 0.474 0.608 0.028 0.039MoG 0.000 0.000 0.032 0.010 0.018 0.012MoGG 0.000 0.000 0.028 0.007 0.012 0.016HMRF-PSO 0.000 0.000 0.018 0.001 0.004 0.005

Misclassification errors in NDT segmented images means the minimal error

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Conclusionwe have presented HMRF-PSO method and compared it to thresholding methods

Performance evaluation was conducted on NDT image dataset

Misclassification Error criterion was used as a performance metric

From the results obtained, the HMRF-PSO combination method outperforms thresholding methods

HMRF-PSO method demonstrates its robustness and resistance to noise

Selecting parameters still not obvious task

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Thank you for your attention