1. Image Processing and Pattern Recognition
Jouko Lampinen

2. About this presentation
In this set of slides we illustrate a bigger problem which uses both morphological operations and other operations that will be introduced soon.
In most cases we use morphological operations together with other operations.
The most important reason of using them is speed and non-linear processing.

3. Problem
Various types of grain materials are used in concrete production
We want to characterize features of grains
We want to characterize sets of grains and classify them
We want to look to similarities and dissimilarities
We use morphological, other shape based methods and spectral methods as well as Machine Learning software

4. Image analysis of grain material in concrete production
Images captured by standard
1200 dpi color scanner
Grain shape inputs
angularity, flakiness
Grain texture inputs
Boundary & surface texture
FFT based texture features
Image Analysis Tool:
Matlab standalone application
Quality Control Tool:
Excel macro package for running
and analyzing the Bayesian models (to be discussed)

5. Example of grains (1.6-2.0 mm sieve fraction)

6. Most of these parameters will be presented in next lectures
Grain Features Measured from the Image
Area
Major Axis
Minor Axis
Eccentricity
Convex Area
Equivalent Diameter
Solidity
Perimeter
Compactness
Borderline FFT (5 features related to roughness)
Texture 2D FFT (5 features related to surface structure)
Morphological Spectrum (roundness)
Most of these parameters will be presented in next lectures

7. Object size and shape characterization
Bounding box (rotated along principal axes)
Ellipsoid determined by the principal axes
Convex hull

8. Original sand grain image (natural sand)

9. Thresholded image (natural grains)

10. Objects filled

11. Morphological opening (yellow pixels removed)

12. Labelled objects

13. Bounding boxes and minor/major axes

14. Original sand grain image (crushed)

15. Thresholded image (crushed)

16. Objects filled

17. Morphological opening (yellow pixels removed)

18. Labelled objects

19. Bounding boxes and minor/major axes

20. Grain shape analysis: angularity
Feature space!!
Grain shape analysis: angularity
Sharp angles in grains break under compression
Measurement: simulate the erosion due to Ice Age
by morphological erosion
Morphological spectrum:
S(r)
Amount of material removed by circular structure element of radius r

21. Example of Morphological Spectra and Angularity
Natural gravel
(manufactured by Ice Age)
Crushed gravel
We can scientifically compare various gravels
Morphological spectrum