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Deep Homography

Replacing CV heuristics with neural regression for precision colorimetry.

A 7-module guide on training Convolutional Neural Networks in the browser to solve complex geometric transformations under extreme distortion.

Module 1
📉

The Pathology of Distortion

Why CV moments and integer bounding fail at high perspective tilts.

Centroid Shift · Aliasing · Failure
Module 2
🧠

Neural Concepts

From handcrafted descriptors to 8-DOF regression in CNNs.

Regression · Handcrafted · SIFT
Module 3
🎯

Sub-Pixel Accuracy

Bypassing discrete pixel grids via continuous regression and offset vectors.

LSCCL · Float Output · Metrology
Module 4
🌌

Infinite Synthetic Data

Generating labeled training frames in the browser using the Canvas API.

OffscreenCanvas · Ground Truth · Warping
Module 5
🏗️

CNN Architecture

Building a lightweight VGG-style network with TensorFlow.js.

TFJS · Conv2D · Regression Head
Module 6
🔄

The Training Loop

Managing memory and visualizing loss curves in the browser.

Adam · MSE · tfjs-vis
Module 7
💎

Inference & Mapping

Bridging TFJS and OpenCV for inverse colorimetric sampling.

Prediction · $H^{-1}$ · Inverse Sampling
Module 8
⚙️

Tuning & Strategy

Escaping loss plateaus with Huber loss and learning rate schedules.

Huber Loss · Plateau · $H^{-1}$