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Recognitive aspects of moment invariants
1Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125.
Summary
This study evaluates moment invariants for pattern recognition, introducing complex moments to derive and characterize them. Findings reveal insights into information loss, redundancy, and noise tolerance for robust feature extraction.
Area of Science:
- Computer Vision
- Pattern Recognition
- Image Analysis
Background:
- Moment invariants are crucial for pattern recognition, offering translation, rotation, and scale invariance.
- Traditional moment invariants can suffer from information loss and redundancy, impacting performance.
- Understanding their properties is key to improving feature extraction techniques.
Purpose of the Study:
- To assess moment invariants as a feature space for pattern recognition.
- To investigate their discrimination power and noise tolerance.
- To introduce and utilize complex moments for deriving and characterizing moment invariants.
Main Methods:
- Evaluation of moment invariants using complex moments as a derivation method.
- Analysis of information loss, suppression, and redundancy within moment invariants.
- Investigation of moment invariant behavior under additive noise conditions.
Main Results:
- Complex moments provide a straightforward method for deriving moment invariants.
- Significant findings were derived regarding information loss, suppression, and redundancy.
- The behavior of moment invariants in the presence of additive noise was characterized.
Conclusions:
- Moment invariants, particularly when derived using complex moments, offer a valuable feature space.
- The study provides a deeper understanding of their inherent properties, including limitations.
- Results indicate potential for improved pattern recognition systems, especially in noisy environments.
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