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Improving the performance of image classification by Hahn moment invariants
Summary
This study introduces a faster method for computing Hahn's discrete orthogonal moments, crucial for image analysis. The new approach accelerates computation and improves pattern recognition accuracy using block-based image representation and novel invariant moments.
Area of Science:
- Computer Vision
- Image Processing
- Pattern Recognition
Background:
- Discrete orthogonal moments are vital for image analysis but computationally intensive.
- Existing methods for calculating Hahn's moments are time-consuming.
Purpose of the Study:
- To present a novel, fast computation method for Hahn's discrete orthogonal moments.
- To introduce a new set of invariant moments for image analysis.
- To validate the effectiveness of the proposed descriptors.
Main Methods:
- Utilizing recurrence relations and symmetry properties of Hahn's orthogonal polynomials.
- Employing an innovative image representation with homogenous rectangular blocks.
- Developing invariant moments based on image intensity slices and linear combinations.
Main Results:
- Significant acceleration in computation time for Hahn's moments.
- Effective image reconstruction and invariant properties demonstrated.
- Competitive performance in pattern classification compared to existing methods.
Conclusions:
- The proposed method offers a computationally efficient approach to Hahn's discrete orthogonal moments.
- The new set of invariant moments shows promise for image analysis and pattern recognition tasks.
- This work advances the field of image descriptors and pattern classification.
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