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Barcode detection and recognition using the Gabor

wavelet.

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Motivation.

An omnipresent identification standard: the barcode.

->Unattended barcode recognition using a low camera resolution.

->Feature extraction using the wavelet theory.

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Key words:

Convolution filters. Gabor wavelet. Morphology. Hough and Radon transform. Feature extraction. Classification.

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Outline of the presentation, Current barcode systems. The 5 steps of the project:

Labelling Grouping Conditioning Extracting Matching

Evolutions: Feature extraction computed directy from the wavelet coefficient

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Current barcode systems.

Hardware: 2 types of barcode readers:

Attended:• pencil-like:

• gun-type reader.

• Unattended: supermarket like.

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Physical standards:

barcode Charcter set Length application example

EAN 13 Number only 12 data

1 check sum

Retail product, world wide

Code128 ASCII Variable Widely used

PDF 415 ASCII 93 Encoding parcel destination.

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Optional checksum characters are include. Encoding dependent on the standard:

Black and white bars: UPC-A Widths of the bars: code 39.

Barcode used: code 39 style.

Encoding standard

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Conclusion

Effective method for identifying items, Cheap, Durable, Easy to produce.

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Proposed solution:

A 5 steps methodology:

1. Labelling

2. Grouping

3. Conditioning

4. Extracting

5. Matching.

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Detection of the ZOI and Gabor Wavelet:

Barcodes are a grating of oriented bars. Easy localisable by human but not by machine.

solution: model of the primary visual cells located in the cortex, The Gabor Wavelet. Hubel and Wiesel (1962)

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The mathematical model:

The mother wavelet:

Parameters: Theta: orientation lambda: preferred wavelength Gamma: eccentricity Sigma: standard deviation, size of the visual field.

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Space domain

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Frequency domain. =4 pixels.

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Frequency domain =6 pixels.

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Gabor wavelets are not orthogonal.

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Basis restriction

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Execution: 2 octave.

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FilteringLamda=2,3,5 pix Threshold

Thin bars.

Wide bars.

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Logical Union of the sub-spaces.

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Conclusion for Labelling.

Three scales wavelet analysis on one direction. The Gabor wavelet react to parallel oriented

lines. Not orthogonal -> not for Compression purpose Simple operations will detect the ZOI.

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Grouping:Connected component analysis.

Detection of the ZOI. Change of logical unit:

From pixels, To set of pixels.

Index -> belonging to a region.

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Connected component analysis.

What is a neighbhor?

-> Detection of small objects contrasting with the background.

A row of an image of a time, example (Haralick 1981)

Exemple:

1

1 X 1

1

1 1 1

1 X 1

1 1 1

0 1 0 0 1

0 1 0 0 1

0 1 1 0 1

0 1 1 1 1

0 0 0 0 0

0 1 0 0 2

0 1 0 0 2

0 1 1 0 2

0 1 1 1 A

0 0 0 0 0

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Execution:

Delatiion will be explained later on.

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Proposed solution.

A 5 steps methodology:1. Labelling

2. Grouping

3. Conditioning

4. Extracting

5. Matching

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Conditioning: Noise removal.

Convolution filters, smoothing filters. Local average (box filter). Ex: [1 1 1 1 1] Gauss filter. Ex: [1 4 6 4 1]

Order statistic operators. Median filter.

Morphological noise cleaning.

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Convolution filters: Local average

Convolution for LTI systems. Study of FIR. separability. Space Domain.ex [1 1 1]

Frequency domain.

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Convolution filters:gauss

No ripple.

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Example:

Gaussian noise variance=0.1. Defocusing. Filter:1/16 [1 4 6 4 1] Gain=0.65 dB.

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Order statistic filters:The median filter.

Linear filter for gaussian noise but poor for binary noise. Linear combination of the sorted values. K*K neighbourhood. K odd.

Median: Intequartile: threshold:

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Example:

Noise density= 0.1. Median filter 3*3 Gain= 10.42. dB Ideal picture.

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The binary morphology.

Identifying maximal connected sets of pixels participating in the same kind of events

First used by Kirsch(1957),2 basis operations.

Let be X the binary picture and B the SE. Dilatation. (Minkowski addition)

When any point of B with origin x(i,j) are in X

Erosion. (shrink or reduce)When all points of B with the origin x(i,j) are in X

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Complexity.

Let be a L*L binary pixels

a SE of 2^M pixels.

British museum algorithm: L*L*2^M L*L*2*M (Haralick 1986)

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Derived operators.

Opening Closing

Conndition for complet noise removal:

A close under K. Opening with small circle -> remove salt & paper noise. Extract and handle of a shape.

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Example

Dilatation Erosion.

1 1 1 1

1 1

1 1

1 1

1 1

1 1 1

1

1

1

1

1 1

1 1 1 1

1 1

1 1

1 1

1 1

1 1 1

1

1

1

1

1 1

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Execution:

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Proposed solution.

5 steps methodology:1. Labelling

2. Grouping

3. Conditioning

4. Extracting

5. Matching

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Parameter extraction.

Template matching dependent of: Noise Rotation Scale.

Solution: parameter extraction. Ex: for a an elipse,

Its center Exentriciy Size

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Example: Hough transform

Hough 1962, used for pattern recognition in the 80’s. Detect primitive shapes, like line, elipse ... Used on binary image preprocessed with edge

detection technique. Point in space parameter domain.

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Line detection.

Placer les figures

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Adopted solution: A simple linear regression was preferred.

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Angle correction:

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Signature segmentation analysis.

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Proposed solution:

A 5 steps methodology:

1. Labeling

2. Grouping

3. Conditioning

4. Extracting

5. Matching

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Matching.

Classification of the parameters. Sharp clusters Fuzzy cluster Neural network.

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2 cluster segmentation:

Distance between each pair of observations.

M observations,

N variables.

M*(M-1)/2 pairs.

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Dendrogram

Hierarchical tree. Hight= distance

between 2 clusters to be connected.

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Conclusion on the advancement. Working order:

256*256 pixels picture or 512*512. Recognition till 64*64.

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Proposed solution:

Current barcode systems. The 5 steps of the project:

Labelling Grouping Conditioning Extracting Matching

Evolutions: Feature extraction computed directly from the wavelet coefficient

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Feature extraction computed from the wavelet coefficient

Present difficulties: Which wavelet ? Which basis?

compression VS classification

Irrelevant cost fonction minimisation .

Significant differences come from low energy subbands. Best Basis algrithm (Saito)

Which Coeficients?

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From Coeficients to features Direct computation. Dimmension reduction.

Parameter selection Prameter projection.

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Conclusion: Large subject:

Wavelet Digital filters Classification morphology

Program in working order. Optimisation gabor model. Feature extraction possible

with wavelet.