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Fourier-domain-based angular correlation for quasiperiodic pattern recognition. Applications to web inspection.

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This study introduces a Fourier-domain technique for recognizing periodic patterns using angular correlation. It accurately detects rotation angles between patterns, aiding in industrial web inspection tasks.

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Area of Science:

Image processing
Pattern recognition
Fourier analysis

Background:

Periodic and quasiperiodic pattern recognition is crucial for industrial quality control.
Existing correlation techniques can be computationally intensive or sensitive to rotation.
A robust method is needed for accurate defect detection and classification in web inspection.

Purpose of the Study:

To propose a novel Fourier-domain technique for periodic and quasiperiodic pattern recognition.
To develop a method for determining the rotation angle between patterns using spectral analysis.
To present and discuss applications in industrial web inspection.

Main Methods:

Utilizes angular correlation of the moduli of sample and reference Fourier spectra.
Centers spectra at the maximum central point for accurate comparison.
Employs correlation peak height for recognition and peak position for rotation angle determination.
Includes optimizations for discrete Fourier-domain angular correlation calculation.

Main Results:

Achieves pattern recognition through high correlation peaks when spectra coincide.
The angular correlation function directly yields the rotation angle between patterns.
Optimized discrete calculations improve computational efficiency.
Demonstrates successful application in web inspection tasks.

Conclusions:

The proposed Fourier-domain angular correlation technique offers an effective method for periodic and quasiperiodic pattern recognition.
This approach accurately determines pattern rotation, valuable for industrial defect detection and classification.
Optimizations enhance the practical applicability of the technique in real-time web inspection systems.