Shape Recognition in AI Systems

Measuring and classifying enclosed shapes directly in pixel space

How do you measure a circle without using π, and entirely in pixels?

This proposal presents a practical GRM application approach: measuring and classifying geometric shapes by counting pixels within a square reference frame. Instead of relying on internal parameters such as radius, GRM treats geometry as proportional occupation, which maps naturally to digital imaging and grid-based systems.

This paper explains:

• why classical parameter-first geometry maps imperfectly to pixel environments
• how GRM uses canonical ratio structure to classify enclosed shapes
• how pixel ratios become a scalable and explainable measurement method
• how the method can integrate into existing digital workflows without geometric reconstruction

Who is this for?

Developers working in computer vision, medical imaging, educational tooling, industrial inspection systems, and AI workflows that require interpretable shape logic.

Published

v1.0 | May 4, 2025 | English | 12 pages

Registration: i-Depot (BOIP) Reference no. 151927