V. Martin et al. 1 (18) WFDPVA, ENEA Frascati 28/03/12

3D Scene Calibration for Infrared Image Analysis3D Scene Calibration for Infrared Image Analysis

V. MartinV. Martin, V. Gervaise, V. Moncada, M.H. Aumeunier, M. Firdaouss, J.M. Travere (CEA), V. Gervaise, V. Moncada, M.H. Aumeunier, M. Firdaouss, J.M. Travere (CEA)S. Devaux (IPP), G. Arnoux (CCFE) and JET-EFDA contributorsS. Devaux (IPP), G. Arnoux (CCFE) and JET-EFDA contributors

Workshop on Fusion Data Processing Validation and Analysis, ENEA Frascati, 26-28 March 2012Workshop on Fusion Data Processing Validation and Analysis, ENEA Frascati, 26-28 March 2012

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V. Martin et al. 2 (18) WFDPVA, ENEA Frascati 28/03/12

•Issue: a complex thermal scene

1. Wide angle views with high geometrical effects: depth of field and curvature

2. Many metallic materials (Be, W) with different and changing optical (reflectance) and thermal (emissivity) properties

•Objective: Match each pixel with the 3D

scene model of in-vessel components for:

1. getting the real geometry of the viewed objects

2. reliable linking between viewed objects and their related properties

•Applications

1. Image processing (event characterization)

2. IR data calibration: Tsurf = f(material emissivity)

3D IR Scene Calibration

JET #81313 KL7(images in DL)

Bulk W

Bulk Be

Be coated linconel

W coated

CFC

Bulk Be

Bulk Be

W coated CFC

V. Martin et al. 3 (18) WFDPVA, ENEA Frascati 28/03/12

IR Data Calibration Methodology

Image Stabilization

2D/3D Scene Model

Mapping

Image Processing

Image Correction

Camera

NUCDead pixel Map

Reference image 2D/3D scene models

Knowledge base of the thermal

scene

•Calibration chain

Registered & Calibrated

Image

V. Martin et al. 4 (18) WFDPVA, ENEA Frascati 28/03/12

Illustration of Motion in Images

• Camera vibrations lead to misalignments of ROIs (PFC RT protection) = false alarms or worth missed alarms

• Image stabilization is a mandatory step for analysis based on ΔT(t) estimation (e.g. heat flux computation)

V. Martin et al. 5 (18) WFDPVA, ENEA Frascati 28/03/12

•Important factors for method selection• Deformation type: planar (homothety), non-planar

• Target application: real-time processing, off-line analysis

• Data quality and variability: noise level, pixel intensity changes, image entropy

• Aimed precision level: pixel, sub-pixel

•Applications in tokamaks (non-exhaustive list)

Image Stabilization

Motion cause Deformation type

Target application Aimed precision level

Difficulty

Tore Supra

RF antenna IR

1. antenna positions

2. camera vibrations

1. non-planar

2. planar (Δx, Δy)

RT PFC protection pixel level changes of scene appearance

JET KL7

wide-angle IR

camera vibrations planar (Δx, Δy) offline analysis (heat load, disruptions…)

sub-pixel low image entropy

JET KL9

divertor tiles IR

sensor affected by magnetic field

planar (Δx, Δy) offline analysis (heat load, disruptions…)

sub-pixel low resolution, slow motion, aliasing

JET KL8

fast visible

intensifier affected by magnetic field

planar (Δx, Δy, Ө) physics (transient events)

pixel level noisy, low image entropy, aliasing

V. Martin et al. 6 (18) WFDPVA, ENEA Frascati 28/03/12

•Classical Methodology

1. Feature Detection• Local descriptors: Harris corners, MSER, codebooks, Gabor wavelets (see Craciunescu

talk), SIFT, SURF, FAST…

• Global descriptors: Tsallis entropy (see Murari talk), edge detectors…

• Fourier analysis: spectral magnitude & phase, pixel gradients, log-polar mapping…

2. Feature Matching• Spatial cross-correlation techniques: normalized cross-correlation, Hausdorff distance…

• Fourier domain: normalized cross-spectrum and its extensions

3. Transform Model Estimation

• Shape preserving mapping (rotation, translation and scaling only)

• Elastic mapping: warping techniques…

4. Image transformation

• 2D Interpolation: nearest neighboor, bilinear, bicubic…

Image Stabilization

See Zitova’s survey, Image and Vision Computing, vol. 21(2003), pp. 977-1000

V. Martin et al. 7 (18) WFDPVA, ENEA Frascati 28/03/12

Proposed Algorithm

1. Masked FFT-based image registration [1] Deterministic computing time

Accelerating hardware compatible algorithm (e.g. FFT on GPU) → real time

applications

Local analysis with dynamic intensity-based pixel masking (e.g. mask the

divertor bright region)

2. with sub-pixel precision [2] Slow drift compensation

3. and dynamic update of the reference image Robust to image intensity changes (context awareness)

Evaluation of registration quality over time

[1] D. Padfield, IEEE CVPR’10, pp. 2918-2925, 2010[2] M. Guizar-Sicairos et al., Opt. Lett., vol. 33, no. 2, pp. 156-158, 2008

V. Martin et al. 8 (18) WFDPVA, ENEA Frascati 28/03/12

Principle of Fourier-based Correlation

•Let Iref a reference image, It an image at time t and DFT the Discrete 2D Fourier

transform such as It ( x , y ) = Iref ( x-x0 , y-y0 )

•NCC is the Normalized Cross Correlation figure

(image) and the position of the peak gives the coordinates

of the translation ( x0 , y0 )

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00 maxarg, max (NCC(Iref, It))

NCC(Iref, It)

V. Martin et al. 9 (18) WFDPVA, ENEA Frascati 28/03/12

Sub-pixel Precision

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aliasing)-(anti 0)sfrequenciehigh (

)sfrequencie low(

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1-

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UPNCC

UP

UPNCC

UPNCC

UPNCC

UPUPNCC

kkv

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UP

FDFTNCC

F

FF

NCCDFTF

NCCvuNCC

•Up-sample k times the DFT of NCC (trigonometric interpolation):

•The peak coordinates ( x0 , y0 ) give F the translation with 1/k pixel of precision:

UP

yxNCC

kyx

,00 maxarg

1,

V. Martin et al. 10 (18) WFDPVA, ENEA Frascati 28/03/12

Reference Image Updating

•Use the NCC value to trigger the update of Iref :tref IIthen

TtNCCTif

maxmin )(

High NCC confidence, no Iref

update needed

Tmin

Tmax

NCC failed, no Iref update

update Iref

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Results

• JET #81313, KL7, 480x512 pixels, 50 Hz, 251 frames

k=1/4 pixel

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Results

k=1/2 pixel

• JET #80827, KL7, 128x256 pixels, 540 Hz, 13425 frames

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Results

k=1/16 pixel

• JET #82278, KL9B, 32x96 pixels, 6 kHz, 4828 frames

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Computational Performance

• Influence of image size and precision level on frame rate

•GPU NVidiaTM GTX 580: 512 processor core units 32 threads per processor Up to 1 Tflops!256x256, k=1/4

→ 700 fps

GP

U/C

PU

per

form

ance

gai

n

Number of CPU cores

GPU is at least 15 times faster than

CPU (same generation)

• FFT 256x256 pixels: CPU v/s GPU performance

V. Martin et al. 15 (18) WFDPVA, ENEA Frascati 28/03/12

From 2D to 3D

•Challenge

– transform pixel coordinates into machine coordinates: (x, y) (r, θ, φ)

•Method– Ray-tracing method from simplified CAD files

V. Martin et al. 16 (18) WFDPVA, ENEA Frascati 28/03/12

3D Scene Model for Image Processing

S. Palazzo, A. Murari et al., RSI 81, 083505, 2010

V. Martin et al.

Blobs 1 & 2 must not be

merged!

1

2

1

2

mm

Z Map (depth)

2

12m

7m

V. Martin et al. 17 (18) WFDPVA, ENEA Frascati 28/03/12

Plasma ImagiNg data Understanding Platform (PINUP)

IR Data Calibration Methodology

•An integrated software for IR data registration & calibration

Image Stabilization

2D/3D Scene Model

Mapping

Image Processing

Image Correction

Camera

NUCDead pixel Map

Reference image 2D/3D scene models

Knowledge base of the thermal

scene

Registered & Calibrated

Image

Used for PFC protection

Used for temperature evaluation

Used for event triggering

Set sub-pixel precision factor

Set mask

Load/save translations

V. Martin et al. 18 (18) WFDPVA, ENEA Frascati 28/03/12

Conclusion

• Summary– Complex IR scenes require a new calibration approach including image

stabilization, 3D mapping for reliable data analysis.

– A robust and fast image stabilization algorithm with sub-pixel precision has been proposed.

– A first integration of 3D model for IR data analysis has been performed.

– An integrated software (PINUP) is available for users

• Outlook– Test of the stabilization algorithm on visible imaging data (JET KL8) with

rotation compensation

– Full integration of 3D scene models into PINUP

– Improvement of image processing algorithms (e.g. hot spot detection) with 3D information