Building a Vision Detection Algorithm from Scratch Using Linear Algebra and MNIST

Preamble

In the rapidly evolving field of machine learning, understanding the foundational techniques can be incredibly empowering. This blog post will walk you through a novel approach to image processing using classical linear algebra-based methods. We’ll explore edge detection, line detection, and feature detection using the MNIST dataset. By the end, you’ll have a solid grasp of how these algorithms work and how they can be applied to other image datasets.

Introduction

Machine learning and computer vision are fields that heavily rely on mathematics, particularly linear algebra. By leveraging the power of linear algebra, we can develop robust algorithms for various vision detection tasks. In this post, we will use the MNIST dataset to demonstrate how classical image processing techniques like Canny edge detection, Hough Line Transform, and SIFT feature detection can be applied to digit images.

Getting Started

Before diving into the code, make sure you have the necessary libraries installed:

pip install numpy opencv-python tensorflow matplotlib

We’ll be using the following libraries:

• NumPy for numerical…