Computational Framework for HU's 7 Moment Invariants and RADON's 2nd/3rd Order Moment Invariants

This implementation calculates HU's 7 moment invariants and RADON's 2nd/3rd order moment invariants. The website provides C++ code for RADON transform operations, while a professor from Northwestern Polytechnical University has developed a novel approach using RADON transform to construct these invariant moments, offering reduced image dimensionality and faster computational performance. For detailed algorithm specifications, refer to the professor's published research papers.

To better understand the operational principles of these novel invariant moments, let's examine the computational processes for both HU's 7 moment invariants and RADON's 2nd/3rd order moment invariants. HU moment invariants are shape descriptors that quantitatively analyze geometric characteristics of images through seven standardized calculations implementing central moments normalization. These computations yield critical image information including area, centroid position, and eccentricity measurements through matrix operations on image intensity distributions.

RADON transform serves as a fundamental image processing technique that converts images into projection datasets through line integral computations, enabling effective feature extraction and analysis. The calculation of RADON's 2nd and 3rd order moment invariants involves projection data processing that more accurately characterizes image projection features, facilitating refined image analysis through statistical moment calculations on sinogram data.

The website contains C++ implementation of RADON transform featuring optimized memory management and parallel processing capabilities. However, the Northwestern Polytechnical University professor has advanced this methodology by developing novel invariant moments using RADON transform that incorporate dimensionality reduction algorithms and computational optimizations. For those interested in the algorithm's implementation details, the professor's literature provides comprehensive explanations of the mathematical formulations and code optimization techniques.

By employing these advanced invariant moments, developers can achieve more efficient image analysis and processing workflows, delivering more accurate and reliable solutions across various application domains. These implementations typically involve matrix operations, numerical integration methods, and optimized memory handling for large image datasets. Should you have further technical inquiries, please feel free to seek additional clarification.