Barcode detection and recognition using the Gabor wavelet.

Motivation. An omnipresent identification standard: the barcode. ->Unattended barcode recognition using a low camera resolution. ->Feature extraction using the wavelet theory.

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

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

Current barcode systems. Hardware: 2 types of barcode readers: Attended: pencil-like: gun-type reader. Unattended: supermarket like.

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 93 Encoding parcel destination.

Encoding standard 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.

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

Proposed solution: A 5 steps methodology: Labelling Grouping Conditioning Extracting Matching.

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)

The mathematical model: The mother wavelet: Parameters: Theta: orientation lambda: preferred wavelength Gamma: eccentricity Sigma: standard deviation, size of the visual field.

Space domain

Frequency domain. =4 pixels.

Frequency domain =6 pixels.

Gabor wavelets are not orthogonal.

Basis restriction

Execution: 2 octave.

Filtering Lamda=2,3,5 pix Threshold Thin bars. Wide bars.

Logical Union of the sub-spaces.

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.

Grouping: Connected component analysis. Detection of the ZOI. Change of logical unit: From pixels, To set of pixels. Index -> belonging to a region.

Connected component analysis. 1 X 1 X 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 2 A

Execution: Delatiion will be explained later on.

Proposed solution. A 5 steps methodology: Labelling Grouping Conditioning Extracting Matching

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.

Convolution filters: Local average Convolution for LTI systems. Study of FIR. separability. Space Domain.ex [1 1 1] Frequency domain.

Convolution filters: gauss No ripple.

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

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:

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

Let be X the binary picture and B the SE. 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

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)

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.

Example Dilatation Erosion. 1 1 1 1 1 1

Execution:

Proposed solution. 5 steps methodology: Labelling Grouping Conditioning Extracting Matching

Parameter extraction. Template matching dependent of: Noise Rotation Scale. Solution: parameter extraction. Ex: for a an elipse, Its center Exentriciy Size

Example: Hough transform 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.

Line detection. Placer les figures

Adopted solution: A simple linear regression was preferred.

Angle correction:

Signature segmentation analysis.

Proposed solution: A 5 steps methodology: Labeling Grouping Conditioning Extracting Matching

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

2 cluster segmentation: Distance between each pair of observations. M observations, N variables. M*(M-1)/2 pairs.

Dendrogram Hierarchical tree. Hight= distance between 2 clusters to be connected.

Conclusion on the advancement. Working order: 256*256 pixels picture or 512*512. Recognition till 64*64.

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

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?

From Coeficients to features Direct computation. Dimmension reduction. Parameter selection Prameter projection.

Conclusion: Large subject: Program in working order. Wavelet Digital filters Classification morphology Program in working order. Optimisation gabor model. Feature extraction possible with wavelet.