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Ondřej RozinekCzech Technical University in Prague
Faculty of Biomedical Engineering
3D Hand Movement Analysis in Parkinson’s Disease
14.5.20081
Outline
Motivation and goalsColor calibrationMarker detectionCamera calibration and 3D reconstructionMovement analysis
Conclusion
Block diagram
14.5.20082
Motivation and goalsTask: Are there any changes in patient‘s conditions
after a drug was administered?
Solution: 3D video analysis of hand movement
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2D trajectory from top view
2D trajectory from side view
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3D trajectory
Color calibration Correction of the image and so compensate different contrast
and brightness conditions
Task of curve fitting
Different color calibration methods are compared:
1. Linear interpolation (LI)2. Cubic Hermite functions (HF) 3. Multiple linear regression model (MLR)
Uncalibrated UncalibratedCalibrated Calibrated14.5.20084
Color calibration – multiple linear regression model
Let Y be the matrix of reference colors (image I) and X the corresponding colors of uncalibrated image J
t - number of terms MLR (linear combination of color components)n - used colors for color calibration t ≤ n - condition
Disadvantage: multicollinearity of colors: white, grayscale, black
JXXXYI Tntnt
Tntnt
1)(
Blu
e
Red Green
3D transfer function with linear terms
3D transfer function with non-linear terms
Blu
e
Red Green5
Color calibration - evaluation
Used color for calibration
c
Ecal
1D transfer functions 3D transfer function MLR(t)LI HF MLR3 MLR7 MLR10 MLR13
K, W 2 40.4 43.6 x x x xR, G, B 3 44.1 41.2 33.6 x x xC, M, Y 3 52.0 57.4 34.0 x x xC, M, Y, K 4 49.0 54.2 33.8 x x xR, G, B, K, W 5 34.0 40.8 33.0 x x xC, M, Y, K, W 5 47.3 52.9 31.5 x x xall 24 colored squares 24 27.3 31.0 29.1 22.8 16.8 15.1without calibration 159.8
n
irefirefirefical BBGGRR
nE
1
2221
black (K), white (W), red (R), green (G), blue (B), cyan (C), magenta (M), yellow (Y), c – number of corresponding colors, t –terms, t ≤ n 14.5.2008
6
Root mean square error: - reference values - calibrated values
refrefref BGR ,,
iii BGR ,,n - all squares on the color
chessboard
Marker detection1.2 seconds; 30 frames 2.0 seconds; 50 frames
14.5.20087
Camera calibration and 3D reconstruction
Pinhole camera model
- image coordinates - world coordinates - camera calibration matrix with intrinsic camera parameters - extrinsic camera parameters
Estimate the camera matrix Direct linear estimation Closed-form solution
Estimate the fundamental matrix relationship between the locations of two cameras using eight point alghoritm for point correspondences (u, v) for m ≥ 8 (i = 1,…m)
XXXx MRR
ofS
ofSfSRR
IKT
TT
yy
xx
T
TT
1
t
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01
t0 1333
00
KXx
M
0
m
i
v
v
u
u
FTm
TiF
,R tChessboard for point correspondences
14.5.20088
Camera calibration and 3D reconstruction
Barrel distortion
dyrararayy
dxrararaxx
d
d
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34
22
1
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1
1
Undistorted
.22
222
45
2254
ddd
ddd
yrayxady
xrayxadx
For measurements is necessery undistorted image
- distorted image coordinates
- tangential distortion
- camera parameters
- new normalized point coordinate
dd yx ,
kadxdy,
yx, 22dd yxr
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Movement analysis
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136.5°
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119.4°
14.5.200810
Movement analysis
Motion a b c d e
S 1.47
2.89 6.53 5.94 3.12
V 0.39
0.66
0.85 0.81 0.50
Rvar 5.50
9.85 22.21 17.57
9.34
Sk0.19
1.04
1.02 0.61 0.09
Ek2.10
2.86 2.63 1.78 1.58
standart deviation (S)
variation coefficient (V)
range (Rvar)
skewness (Sk)
kurtosis (Ek)
14.5.200811
Conclusion
14.5.200812
Blue markers are proposed
3D hand trajectory of patients is obtained
Error is 1-3 mm at rest and for slower motion (camera has only 25 frames per second)
Color calibration to obtain the required brightness and contrast for the segmentation
Hand velocity, angle in wrist and some statistic parameters are evaluated
Future plans
Thank you for your attention
14.5.200813