Structural Image Analysis in Investigation of Concrete Warsaw, October 21-23, 2002

Classification of defectson the surface of black ceramics

Leszek Chmielewski, Mariusz Nieniewski,Marek Skłodowski, Waldemar Cudny

Division of Vision and Measurement Systems (PSWiP)

Institute of Fundamental Technological Reserach, PAS(IPPT PAN)

Adam JóźwikInstitute of Biocybernetics and Biomedical Engineering, PAS

(IBIB PAN)

2/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Schedule

Objects and their defects Detection of defects Classification of defects Training of the classifier Postprocessing Performance of the processes Results

AcknowledgementsThis research was partly supported by the European Commission: COPERNICUS grant CRASH no. COP - 94 00717 (1995-96) INCO-COPERNICUS grant SQUASH no. ERBIC 15CT 96 0742 (1997-98)

3/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

This is not concrete – this is ferrite

Black ceramics: ferrite cores magnets

The material is: milled molded pressed sintered ground transported ...

A large number of various defects can emerge during these processesA pair of ferrite cores

4/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Objects and their defects – called nicely: irregularities

Surfaces:3 important types of defects: crack chip pull-out

Sometimes difficult to classify even for humans

Tiring quality inspection

5/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Special illumination: controlled light

Tangential, multidirectional light amplifies the visibility of defects

Brightness uniform and independent on distance

LED illuminator Fluorescent illuminator

6/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Locating the object in the field of view

Simple morphological operations help to find the region occupied by the object for further processing

Aim: to eliminate bright spots and blobs

In this application a narrow stripe at the edge was excluded from analysis

original thresholded complemented

original complemented

7/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Detection of irregularities (not defects!)

Region of interest is further limited to the irregular part of the surface with the morphological methods

tomorrow’s presentation by prof. Mariusz Nieniewski

original thresholded elongated irregular summed

8/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Classification of irregularities – features (1/2)

Each pixel detected in the detection phase is classified with the pattern recognition methods. Pixel = pattern.

Features are calculated for each pixel: functions on pixel neighbourhoods = masks.

Direction invariance of features is obtained by rotation of the mask according to local directionality of texture.

Pixel & its mask

original mask rotated mask

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9/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Classification of irregularities – features (2/2) brightnesses in the original & rotated mask statistical moments of order up to R in masks gradient modulus 9 classical textural features according to [Law80, Pra91] textural features based on coocurrence relations [WuCh92] relative values of brightness function along the red line

From 30 to 150 features were used for feature selection.

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For example: Features as in [Law80, Pra91]: convolve the mask with A1 – A9 and take standard deviation of the output values 9 features.

10/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Classification: the K Nearest Neighbour (k-NN) method

11/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

K-NN: Enhancements and speed-ups

With full selection of features and K Leave-one-out method

Fuzzy version Fuzzy decisions made crisp in the end

Parallel Distinct classifiers for each pair of classes

Hierarchical Advanced version only where classes overlap

Reference set largely reduced with the modified, bidirectional Hart algorithm

Optimized, low error rate, quick algorithm

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The parallel version of the K Nearest Neighbour method

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K-NN: class overlap as the training criterion (not error)

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Training: the training patterns

Note: artificial, boundary classes introduced better accuracy

2479 training patterns can be obtained quite quickly...

15/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Training: first results

The system has successfully classified thousands of unknown pixels.

Quite satisfactory results can be obtained with just 4 training images.

2479 training patterns

raw classifi-cation results

enhanced by local votong

16/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Training: first results – zoom – results of the enhancement

This was only a convincing example. The error rate estimated with the leave-one-out method was 3.3%.

More training patterns were used in the final system.

pixels used in training classified pixels: all / raw / enhanced

17/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Final training results – error estimates

5903 training patterns a posteriori error probabilities: pixel classified as class "i"

(row) comes in fact from the class "j" (column); in % overall error: 2.56% max error: 9% between classes 8 and 9

cared for by the postprocessing (to some extent)

18/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Classification results – various types of elements (1/4)

blue – chip, yellow – good object

19/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Classification results – various types of elements (2/4)

brown – irregular,

red – crack,

green – pull-out,

blue – chip,

grey – good object.

20/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Classification results – various types of elements (3/4)

blue – chip,

navy – chip near

good,

red – crack.

21/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Classification results – various types of elements (4/4)

blue – chip,

green – pull-out,

red – crack !?

22/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Higher level – discern cracks from grinding grooves

Classify cracks – red betweenall irregular regions – green.

Limits of the method reached.details in other images

23/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Classification results – rotation (in)variance

24/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Technical data & Performance

Resolution

A 512*512 pixel camera. Spatial resolutions: 0.05mm/pixel

(up to 20*larger magnifications can be attained with normal lenses)

Accuracy of results

Classification errors: overall up to 4%, inter-class typically 4%, max 10%;

Stability of results

• Detection phase: repeatability not worse than 2-5% in area.

• Classification phase: repeatability not worse than 10-20% in area, depending of how fast classifier version is used.

Processing time [s] (PC, 1000 MHz)

only software morphological processor

image acquisition 0.05-0.20

detection 2.00 0.0001-0.001

classification 1.00 typically; 10 for v. large defects - 20% of object

decision 0.1

25/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

Conclusion

Irregularities of flat surface in black ceramics – ferrite cores, magnets – can be detected and classififed

Special lighting system has been designed Detection of irregularities:

Irregularities in general – dynamic thresholding Compact irregularities – morphological method Elongated irregularities – morphological method General decision on quality of the tested object

Classification of irregularities: Training by showing examples

Segmentation and measurements Detailed, quantitative final decision

Project www site: http://www.tpo.org.pl/squash

26/25SIAIC 2002, Warsaw L. Chmielewski (PSWiP) et al., Classification of defects on the surface of black ceramics

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[Law80] K. I. Laws, Textured image segmentation, Univ. of Southern California, Image Processing Institute, USCIPI Report 940, Jan 1980

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