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Image Processing and Pattern Recognition Jouko Lampinen.

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Presentation on theme: "Image Processing and Pattern Recognition Jouko Lampinen."— Presentation transcript:

1 Image Processing and Pattern Recognition Jouko Lampinen

2 Outline Image Processing Case example Pattern Recognition Pattern recognition problem Classification: statistical and neural methods Case examples

3 Image Processing Image enhancement histogram transforms (equalization / contrast) noise removal (median / averaging / adaptive filters) image restoration (blur removal) Preprocessing for image analysis thresholding and segmentation edge detection morphological filtering (opening / closing / skeleton) Image reconstruction transformations, tomography

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 MLP models

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

6 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)

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 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 Crushed gravel Natural gravel (manufactured by Ice Age) Morphological spectrum

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