Classification Tolerance and Deviation Handling in GRM-Based Shape Analysis

In this application proposal, I extend the Geometric Ratio Model (GRM) with a structured framework for handling imperfect shapes and measurement deviations. While GRM defines precise ratios for ideal forms such as circles, spheres, and hexagons, real-world data often deviates due to noise, asymmetry, or resolution limitations.

This proposal introduces a tolerance-based classification system using confidence bands, deviation indexing, and fuzzy membership logic. The result is a more robust and interpretable method for GRM-based shape analysis, suitable for AI applications, medical imaging, quality control, and educational tools.

Key additions include:

standardized tolerance bands for core GRM shapes
deviation metrics and confidence scoring
integration architecture for AI and pixel-based pipelines
fuzzy classification models and explainability features

This proposal extends GRM from a static geometry model into a tolerant classification framework designed for real-world use.

Published: v1.0 | May 18, 2025 | Language: English | Pages: 25

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