Deterministic Vision Pipeline

Where This Is Going

The core idea: take complex visual data and reduce it to discrete, interpretable regions using deterministic math — no black-box models. The same pipeline can apply to satellite imagery, medical scans, industrial inspection, or anything where you need reproducible segmentation at scale.

I thought agriculture was the most interesting place to start. Tracking vegetation as it progresses from green to yellowing to harvest-ready brown, across satellite time-series, to forecast commodity yields. That's what I'm currently building toward.

The Sandbox

Paint-by-Number is the controlled environment where I refine the segmentation logic. Upload any image, and it cleanly reduces it into numbered, contiguous regions based on color and spatial proximity. Same engine, friendlier interface.

How It Works

• Operates entirely in CIELAB color space — mathematical distances match human-perceived color differences
• SLIC superpixel clustering in a joint 5D LAB+XY space for precise control over color adherence vs. shape regularity
• K-means palette reduction with Region Adjacency Graph merging to collapse similar adjacent regions
• No neural networks — deterministic, reproducible, O(N) complexity