Chemical color imaging and its advantages by deploying ......and its advantages by deploying...

www.perception-park.com

Chemical color imaging

and its advantages by deploying

hyperspectral cameras

for industrial applications

Machine Vision Technology Forum 2015

STEMMER IMAGING

2015, Nov.

Markus Burgstaller


Perception Park – Who we are

2

Headquarter: Graz, Austria We are focused on:

• Generic hyperspectral data processing

• Intuitive UI concepts and implementation

• Real-time processing

• Industrial validity


Perception Park – Who we are

3

• Motivation

• Instrumentation

• Methodology

• Chemical Color Imaging


Motivation

4

Hyperspectral technology: • No standards available • Missing common „basis“ • Elaborate interdisciplinary

cooperation

Desired Situation: • HW, SW as well as data are provided by

a community (or can be obtained from) • „Common“ operating environment ->

focus to core competence. • Know-how encapsulation possible • Extendable • Dealing with valued data is supported


Instrumentation – Spectroscopy

5

Spectral, temporal investigation (e.g. at a specific point/area)

Spectroscopy and spectrography are terms used to refer to the measurement of radiation intensity as a function of wavelength...

Symmetrical stretching

Antisymmetrical stretching

Scissoring

Source: http://en.wikipedia.org/wiki/Spectroscopy http://www.bruker.com http://jila.colorado.edu/


Instrumentation – Spectroscopy (cont‘d)

6

UV (~0.2-0.4µm)

MWIR (~3-5µm) – InSb

VIS (~0.38-0.75µm) – Si

NIR (1.0-1.7) - InGaAs VNIR (~0.6-1.0µm) – Si

SWIR (~1.0-2.5) – MCT, InSb

SWIR (~1.2-2.2) – Ext.-InGaAs


Instrumentation - Hyperspectral Imaging

7

Spatially, spectrally and e.g. temporally investigation of a region of interest.

Hyperspectral imaging, like other spectral imaging, collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes.

Source: http://en.wikipedia.org/wiki/Hyperspectral_imaging

Wavelength

• >100 wavelengths bands

• Spatially, spectrally and e.g. temporally resolved

• Capable to apply spectroscopic techniques

• Capable to apply image processing techniques

• at least 1D-spatial (spectral image)

http://en.wikipedia.org/wiki/Spectral_imaging

http://en.wikipedia.org/wiki/Electromagnetic_spectrum

http://en.wikipedia.org/wiki/Electromagnetic_spectrum

confidential www.perception-park.com

Instrumentation - Hyperspectral Imaging

8

Spatial scanning:

Line scan: Pushbroom imager

Point scan: Wiskbroom imager

Multi point scan: Multi fiber system

Spectral scanning:

Wavelength scan: Staring imager

Spatio-spectral scanning:

Wavelength and line-scan: wedge-filter imager

Non-scanning:

one cube per time point: snapshot imager

Source: Optical Engineering 51(11), 111702 (November 2012)


Instrumentation – Data Processing

9

• Calibration package • Describes the optical and electrical behavíour of

an instrument (HS camera)

• Instrument standardization (Pushbroom)

• Noise suppression (filtering) • FPN-correction (dark current) • Smile, Keystone correction • Defectpixel correction • Reduction of non-linearities • Image registration (e.g. wavelength calibration) • Multiple ROI, Multiple Binning (Fiberoptic)

• Setup standardization

• Normalization to calibration (white) target • E.g. calibration (absolute) • etc…


Methodology - Processing Networks

10

Data Receiver • Cameras • Files (Harddisk) • Network Streams • etc.

Processing Block • Data Correction • Data Manipulation • Color Models • etc.

Data Transmitter • Camera Link • Network / GigE Vision • Application Link • etc.

Basic Module Types

Camera Manufacturers

Chemometry Engineers

Application Engineers

System Integrators

3rd Party Software

3rd Party Software

3rd Party Software


Methodology – Building Networks

11

Building and extending networks

Module GroupsFactory Modules

Derive

Receiver Transmitter

Pixel Correction

Filter

DenoiseColor

Balancing

Crop

3rd Party Modules

M6

M1 M2 M3

M4 M5

Pixel Correction

Filter G2M4 M6 G4

G1 G2 G3

G4 G5 G6

Color Balancing


Methodology – Complexity Reduction

12

Workflow based User Experience

Complex processing network

Group 2Group 1

Group 3

Group 5

Group 4

Input

Output 1

Complex Networks

Output 2


Methodology - Hardware Acceleration

13

Perception Studio:

• Configure your Hardware

• Examine material properties

• Design the Processing

Network for your application

Perception System:

• Scalable Parallel Computing solution

• Example: GPU accelerated

• Runs the applied processing job

until turned off

Compile processing network for GPU

Group 2Group 1

Group 3

Group 5

Group 4

Input

Output 1

Complex Networks

Output 2

Chemical Color Imaging

www.perception-park.com 15

Chemical Color Imaging

HS Cube Spectra Models Perception Expectation

Select CCI-Modelling Apply to Data Comparison


Example on Wood Recycling

Recycling Stream Spatial vs Spectral Spectra

W = wood M = metal P = plastic U = unclassified


Extract

Reflectance Measurment Preprocessing I (1st Derivative) Preprocessing II (2nd Derivative)



Correlate

Resulting Perception Correlation to Objects


W

W

U

P2

P4

P5

P3

M

M

W

P1


Constrain

Drawing Sketch Resulting Perception



Example: Pharma

Plister of different pills Resulting Perception


Example: Meat

Chop of meat


Example: Plastic sorting

PP flakes green; PE flakes magenta

PE PP


References

NIR NEWS, Volume 25 Issue 6 (2014) Chemical colour imaging and its advantages by deploying hyperspectral cameras for industrial applications Markus Burgstaller, Oct. 2014 http://www.impublications.com/subs/nirn/v25/NIRN25_6.pdf

MAZeT JENCOLOR-ZEISS SPECTROSCOPY-SENSORIK BAYERN SpectroNet Cross-clustering Collaboration Forum Modular Processing of Hyperspectral Data Lukas Daum, Sept. 2015 http://spectronet.de/de/videos_2015/video-modular-processing-of-hyperspectral-data_ie3y2005.html

Fraunhofer IOSB KCM SpectroNet Collaboration Forum 2015 Karlsruhe Multivariate data processing of multidimensional data Markus Burgstaller, Mar. 2015 http://spectronet.de/de/videos_2015/video-multivariate-data-processing-of-multidimensi_i86ber6f.html

Fraunhofer IOSB KCM SpectroNet Collaboration Forum 2015 Karlsruhe Chemical Color Imaging – The next evolution in machine vision Manfred Pail, Mar. 2015 http://spectronet.de/de/videos_2015/video-chemical-color-imaging---the-next-evolution-_i7omx1uw.html

Conference on Hyperspectral Imaging in Industry

June 15th – 16th, 2016 in Graz


…thank you!

Chemical color imaging and its advantages by deploying ......and its advantages by deploying...

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