← Back to Articles
Computer Vision
Computer Vision Experiments
Jun 2026•5 min read•By Chetraj Jaishi
OpenCVAlgorithmsMathematics
“Lessons learned from implementing optical flow, spatial filters, and matrix homography by hand.”
It is easy to treat modern deep learning libraries as black boxes that magically ingest camera frames and output bounding boxes. However, delving into the geometric mathematics of classical computer vision builds fundamental engineering intuition.
Working through pinhole camera projections, intrinsic matrix calibration (focal lengths and principal points), and lens distortion correction unveils how 3D spatial points project onto a 2D sensor array.
Implementing Lucas-Kanade differential optical flow highlights the intensity constancy assumption and spatial coherence constraint. When tracking high-speed motion, multi-scale Gaussian pyramids are essential to handle large pixel displacements without losing lock.
Mastering these mathematical primitives enables engineers to debug why AI models fail under lighting changes, motion blur, and perspective warps in robotics deployments.