Itä-Suomen yliopisto – tulevaisuuden yliopisto...

Computational colorLecture 1

Ville Heikkinen

1. Introduction

- Course context- Application examples (UEF research)

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Standard lecture course:

- 2 lectures per week (see schedule from Weboodi)- exercises

Course page (available in Moodle soon…):

- lecture slides- exercises + data

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Course

Color in image processing

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Computations

Measurement devices produce data that are usually

represented with vectors, matrices, and tensors.

Efficient image analysis and processing usually requires

understanding of color/spectral data and suitable

computational methods.

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Biometry, medical diagnosis, security

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Is illumination same in all images?

Are images calibrated accurately?

Source of color data

In this course we assume that measurement devices

include

standard color imaging devices (e.g. in mobile phone)

and

spectral imaging devices

(multispectral- and hyperspectral cameras).

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Color (trichromatic, RGB) imaging devices

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Example: 3-dimensional vectors

(pixelwise RGB vectors corresponding to a digital color image)

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Is color enough?Color representation

Surface reflectance (r)

Wavelength [nm]

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A simple spectral imaging system

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A complex spectral imaging system

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Spectral image as a data structure

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Reflectance spectrum corresponding to a pixel

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Sampling

Images corresponding to spectral bands

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Applications for spectral data and calibrated color

Biomedical imaging

Biometry and security

Color analysis, display, and printing

Cultural heritage imaging

Environmental monitoring

Industrial machine vision

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Course context: Color management and data analysis...

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...where the main phases are

1. Measurement

2. Processing

3. Accurate color representation

4. Data analysis (e.g. machine learning)

The main topics in this course are:

1. Vector space formulations for colorimetry. Relation between spectral spaces and color spaces. Fundamental colorimetry

2. Estimation of standard color values from device responses

3. Estimation of spectral data from device responses

4. Representation of spectral data using subspaces

5. Implementation of computational models using MATLAB orsome other computational environment.

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Course outline

Practical/project work

• Data + small analysis task

• Students write a report of obtained results. The reports are written as a form of (approx. 6 page) ”scientific publication” consisting of

• Abstract

• Introduction

• Methodology

• Experiments

• Discussion

• Conclusions

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Example 1

Computational spectral imaging:

Increasing the availability of spectral data for common people (as well as for experts)

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A (real) hyperspectral imaging system (100k), UV-VIS-NIR

“A Wide Spectral Range Reflectance and Luminescence Imaging System”,T. Hirvonen et al., Sensors, Vol. 13(11), 14500-14510, 2013. 23

Properties of hyperspectral imaging

+ h• Narrow spectral bands• VIS, IR and UV• Estimation of reflectance information can be done easily by using

reference surfaces.

-• jCosts• Measurement speed (slow)• Light source properties (heat)• Spatial properties of obtained images• High-level of expertise• Practicality of measurement setting• Several systems are not mobile

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25“Spectral imaging using consumer-level devices and kernel-basedregression”, V. Heikkinen et al., Journal of the Optical Society of America A,Vol. 33(6), 2016.

Multispectral imaging system (1k) + mathematics + programming

-> Estimated hyperspectral image in visual wavelength range

Properties of multispectral imaging

+ h• Fast imaging (moving objects, video imaging)• Inexpensive (if based on RGB technique)• Large spatial resolution• Low demands for light source• Practical• RGB based systems are in standard use in several applications• Mobile

-• Estimation of reflectance information is not trivial• Possibly broad spectral bands.• Sensitivity of RGB-based system may be restricted to VIS region

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Spectral images

visualized

as sRGB

Estimates

visualized

as sRGB

“Spectral imaging using consumer-level devices and kernel-basedregression”, V. Heikkinen et al., Journal of the Optical Society of America A,Vol. 33(6), 2016.

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Comparison of images using sRGB color representation

3 line scans (Specim V10) RGB + Laptop system usingDigital colorchecker in training Training data :


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Comparison of images using PCA eigenimages

3 line scans (Specim V10) RGB + Laptop system usingDigital colorchecker in training

3 line scans (Specim V10) RGB + Laptop system usingDigital colorchecker in training Training data :

Supplements for “Spectral imaging using consumer-level devices and kernel-based regression”, V. Heikkinen et al., Journal of the Optical Society ofAmerica A, Vol. 33(6), 2016.

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What kind of training data are needed for the imaging system?



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What kind of applications there are?



Example 2:

Spectral imaging as a tool for object analysis and sensor development

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Hyperspectral imaging as tool for tree seeds screening

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Line scan imaging in 400-2500 nm using two cameras.

Tree seeds in three classes

”Thermal and hyperspectral imaging for Norway spruce (Picea abies) seedsscreening”, J. Dumont, T. Hirvonen, V. Heikkinen et al., Computers andElectronics in Agriculture, Vol. 116, pp. 118-124 , 2015.

Hyperspectral imaging and feature extraction…

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…analyzing…feature selection…

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...and classifier construction with two indices

(based on three, narrow spectral bands)

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Example 3. Remote sensing of forest areas

A plot of pine treesin the ground

(10 m x 10 m area)

“Evaluation of simulated bands in airborne optical sensors for tree species identification”, P. Pant et al., Remote Sensing of Environment, Vol. 138, pp 27–37, 2013.

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Supervised classification of tree species in the ground

“Evaluation of simulated bands in airborne optical sensors for tree species identification”, P. Pant et al., Remote Sensing of Environment, Vol. 138, pp 27–37, 2013.

Example 4. Color calibration in remote sensing

Color characterization for aerial cameras. Susanne Scholz. Applied Geoinformatics for Society and Environment (AGSE) proceedings 2009. 39

Example 5.

Pigment mapping using

spectral reflectance data

(image segmentation)

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Sensor changes in 1000 nm

Let vector V be a spectral measurement

(121-dimensional vector).

Task:

Find those pixels that have correlation

coefficient > 0.99 with the vector V.

Example: Segmentation of painting image

using correlation coefficient

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Task:

Find those pixels that have correlation

coefficient > 0.999 with the vector V.

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