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Experiments on pattern recognition using invariant Fourier-Mellin descriptors.

Y Sheng, H H Arsenault

Journal of the Optical Society of America. A, Optics and Image Science

|June 1, 1986

Summary

This study introduces circular-Fourier-radial-Mellin transform descriptors for invariant multiclass pattern recognition. Efficient image description and satisfactory recognition performance were achieved using specific radial moments and circular harmonics.

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Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Multiclass pattern recognition requires robust image descriptors.
Invariance to position, rotation, scale, and intensity is crucial for real-world applications.

Purpose of the Study:

To develop and evaluate image descriptors based on the circular-Fourier-radial-Mellin transform.
To achieve position-, rotation-, scale-, and intensity-invariant multiclass pattern recognition.

Main Methods:

Utilizing circular-Fourier-radial-Mellin transform for image description.
Selecting orders of radial moments and circular harmonics for efficient description.
Investigating the influence of additive noise on recognition performance.

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Main Results:

The proposed descriptors provide invariance to position, rotation, scale, and intensity.
Efficient image description was achieved by optimizing radial moments and circular harmonics.
First-order radial moments of three circular harmonics yielded satisfactory recognition performance.

Conclusions:

Circular-Fourier-radial-Mellin transform descriptors are effective for invariant multiclass pattern recognition.
The choice of radial moments and circular harmonics significantly impacts recognition efficiency.
The method demonstrates robustness against additive noise.