Pattern Recognition Process

Overview

This project demonstrates a complete pattern recognition pipeline, applying image processing techniques to analyze and recognize patterns in binary images. The process consists of six key steps, each playing a crucial role in extracting meaningful information from raw data.

Pattern Recognition Steps

The pipeline follows six fundamental steps:

1️⃣ Noise Reduction by Blurring – Smooths out image noise using blurring techniques to enhance pattern clarity.
2️⃣ Histogram Analysis – Generates and analyzes the intensity distribution of the image.
3️⃣ Correlation – Measures the similarity between patterns for feature matching.
4️⃣ Thresholding – Converts grayscale images to binary by applying intensity thresholds.
5️⃣ Connectivity Analysis – Identifies and labels connected regions within the binary image.
6️⃣ Calculating the Properties of Binary Regions – Extracts shape properties such as area, perimeter, centroid, and aspect ratio.

Contents

• Pattern Recognition Process.pdf – Jupyter notebooks with each step explained and outputs in pdf.
• README.md – Documentation of the project.