Structural Methods of Pattern Recognition

Overview

This repository contains implementations and resources related to structural methods of pattern recognition. It focuses on algorithms and techniques used for recognizing patterns in data, particularly those based on structural characteristics.

List of Labs (will be expanded)

1. Simple methods for processing full-color and grayscale images
2. Color balancing of images
3. Linear Image Filtering. Object Edge Detection
4. Feature detection in the image. Detection of borders/vertices of regions
5. Segmentation of images. Compression/encoding of images
6. Machine Learning in Image Recognition. Detection and Tracking
7. Neural networks in image recognition. Part 1
8. Harris Corner Detector
9. Neural networks in image recognition. Part 2
10. Blending image elements. Application of the Poisson equation for image restoration and seamless insertion
11. Development of a classifier for user keystroke analysis with a user interface
12. Designing a system for assessing the adequacy of textual data using machine learning techniques
13. Document image rectifying

Table of Contents

1. Introduction
2. Installation

Introduction

Pattern recognition is a crucial aspect of machine learning and artificial intelligence, with applications ranging from image and speech recognition to bioinformatics and data mining. Structural methods, which analyze the structural characteristics of patterns, offer unique insights and solutions to complex recognition tasks.

This repository aims to provide:

• Implementations of various structural pattern recognition algorithms.
• Example datasets and demonstrations to facilitate learning and experimentation.

Installation

# Install all needed packages for this repository
pip install -r requirements.txt