Classification Tolerance and Deviation Handling in GRM-Based Shape Analysis

In this application proposal, the Geometric Ratio Model (GRM) is extended with a structured framework for handling imperfect shapes and measurement deviations. While the 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 robust and interpretable method for shape analysis that supports 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 transforms GRM from a static geometry model into a dynamic, tolerant classification system—ready for real-world deployment.

Proposal – Classification Tolerance and Deviation Handling in GRM Downloaden

Published:

version 1.0 May 18, 2025 | Language: English | Pages: 25