bioRxivBiophysicsPreprint
From Pixel to Wave: A Geometric Complementary Code for Hierarchical Pixel-Based Morphometry
Abstract
This paper introduces a geometric complementary code (GCC) that bridges discrete digital pixel graphics with continuous analog wave mechanics in a geometric morphometrics framework. By oscillating four shaded cubic pixels in a Cartesian grid, an emergent pattern resembling a face-centered cubic (FCC) unit cell lattice appears. This pattern is modeled first in lattice space-- incorporating polarities of the sagittal, transverse, and coronal planes traditionally applied to Cartesian space--and subsequently in Cartesian space as a topographic medium. In the Cartesian model, topographic values divide into rise values, where the grid converges toward elevated features along the Y-axis, and run values, where it flares within terrain dips. This produces a surface grid that undulates like a propagating wave. Across both models, a polar continuum emerges, oscillating between crossed and uncrossed polarities at micro- and macro-grid scales when applied at the topographic tile level. Each tile oscillates to generate counter-oscillatory perspectives across the macro-grid, dynamically shifting between approaching-point and vanishing-point modes. The GCC functions as a hierarchical pixel-based morphometry. It begins with pixel-scale analysis in a single ZX plane, advances to atomic-scale resolution across four ZX planes, and extends to topographic tile-scale across 16 ZX planes. This progression reveals a geometric expansion of FCC patterns within a nested 2x2 matrix processor. Grounded in a consistent Pythagorean grid-count relationship, the framework maps discrete pixel states onto continuous wave-like surface behavior. By addressing limitations in current geometric morphometrics--where 3D scanning and semilandmark methods remain anchored in discrete landmarks or sparse points rather than detailed continuous topographic dimensions--the GCC offers a novel hierarchical bridge between digital and analog domains.
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: deep learning
- approach: statistical model
- Citations
- 0
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