Error Estimation in Digital Image Correlation

Caused by Rigid Particles

By Xiaodan (Danna) Ke

Introduction Simulation Process Particle Extraction Algorithm Particle Translation Algorithm Preliminary Results Future Work

Content

Introduction to Digital Image Correlation (DIC) 1

Speckle patternsDIC is a non-contacting measurement method based on tracking speckle patterns on material surface before and after deformation to determine displacement and strain fields.

Deformed state

Reference state

Force

Working Principles:Calculate cross-correlation

factor of subsets in both states Center locations of subsets at

deformed state will be correlated with those at reference state

Accuracy: ±0.02 pixels

Introduction to Digital Image Correlation 2

Speckle patterns

Deformed state

Reference state

Subset

F

(u,v)

DIC assumes no difference bt. background and particles in pattern

Background and particles deform the same way

Problem caused by rigid particlesError estimation is needed

Introduction to Digital Image Correlation 3

Reference state Deformed state Deformed state

Flexible particles Rigid particlesStretch and translate Only translate

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Simulation Process 1Rigid particles are introduced when applying DIC to biological materialsSimulation method is used to evaluate errors induced by rigid particles

Mouse carotid vessel

0.5 mm

Extract individual particles from reference image

Translate extracted particles according to pre-assigned displacement

Generate deformed image Use Vic-2D (a given DIC package) to

calculate displacement and strain Compare result from DIC with pre-

assigned displacement

Simulation Process 2Challenge part!

Why to extract individual Why to extract individual particlesparticles

Calculate centroid Calculate centroid locationlocation and obtain and obtain accurate displacementaccurate displacement assignmentassignment

Particle Extraction Algorithm 2Particle Extraction Algorithm 2

20 29 41 30

34 51

16 85

105

10 38

40

120 50

99 50

u

x

u1

Centroid

Local neighborhood Local neighborhood searching algorithmsearching algorithm

1.1. Intensity threshold (60)Intensity threshold (60)

2.2. Extract continuous pixels Extract continuous pixels for individual particlesfor individual particles

Particle Extraction Algorithm 3Particle Extraction Algorithm 3

24 32 32 33 41 39 36 29 23 30 38 41 58 74 68 52 34 27 34 44 66 100 124 116 75 38 29 39 44 75 130 158 141 89 46 31 38 40 64 96 124 118 70 38 29 31 39 41 55 62 65 45 32 25

Start

Results

Particle Extraction Algorithm 3Particle Extraction Algorithm 3

Original imageExtracted particles

Calculate centroid in subpixelsCalculate centroid in subpixels Assign displacement to particlesAssign displacement to particles Translate particlesTranslate particles Integration Integration Deform background pixelsDeform background pixels Fill holes based on interpolationFill holes based on interpolation Generate deformed imageGenerate deformed image

Particle Translation Algorithm 1Particle Translation Algorithm 1

Particle Translation Algorithm 2Particle Translation Algorithm 2

Results

Original image Pure rigid motion 10 pixel in width direction

Pure 50% stretch in width direction

Particle Extraction Algorithm 3Particle Extraction Algorithm 3 Results

Pure 50% stretch in width direction

Rigid particles Flexible particles

Preliminary Results of Error EstimationPreliminary Results of Error Estimation

Deformation Ground truth Digital Image Correl. prediction

Flexible particles Rigid particles

Pure translation 10.0 pixels,

0.0%

11.0 pixels,

0.0046%

10.8 pixels,

0.07%

Pure stretch 50.0% 62.57% 61.89%

Trans.+stretch 10.0 pixels,

50.0%

62.54% 61.89%

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Debug codes Debug codes Separate clogged particlesSeparate clogged particles Estimate errors for complicated Estimate errors for complicated

deformationdeformation

Future WorkFuture Work

Thank you!Thank you!