Unsupervised Bayesian Inference (reducing dimensions and unearthing features)
Low-level feature detection using edges
Finally, the moment you have all been waiting for, the next in our unsupervised Bayesian inference series: Our (not so) deep dive into the Sobel Operator.
A truly magical edge detection algorithm, it enables low-level feature extraction and dimensionality reduction, essentially reducing noise in the image. It has been particularly useful in facial recognition applications.
The 1968 love child of Irwin Sobel and Gary Feldman (Stanford Artificial Intelligence Laboratory), this algorithm was the inspiration of many modern edge detection techniques. By convolving two opposing kernels or masks over a given image (e.g. …
Hands-on Tutorials
Unsupervised Bayesian Inference (reducing dimensions and unearthing features)
Aaron Dougherty
Machine Learning Engineer — Cyted Ltd. Building the next generation of AI-based Computational Histopathology diagnostics. Machine Vision, NLP, Bayesian Models.
