https://github.com/abdelrahman-amen/centroid-in-pattern_recognition
This repository implements centroid-based pattern recognition, extracting features from images using grid cell centroids for classification in computer vision and image processing.
https://github.com/abdelrahman-amen/centroid-in-pattern_recognition
centroid decision-trees image-processing keras pattern-recognition python random-forest-classifier
Last synced: 24 days ago
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This repository implements centroid-based pattern recognition, extracting features from images using grid cell centroids for classification in computer vision and image processing.
- Host: GitHub
- URL: https://github.com/abdelrahman-amen/centroid-in-pattern_recognition
- Owner: Abdelrahman-Amen
- Created: 2024-04-08T14:56:12.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-08T15:07:44.000Z (about 2 years ago)
- Last Synced: 2025-04-05T15:46:49.484Z (about 1 year ago)
- Topics: centroid, decision-trees, image-processing, keras, pattern-recognition, python, random-forest-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 7.81 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Centroid-Based Pattern Recognition🏝
## Introduction
Centroid-based pattern recognition is a technique used in computer vision and image processing for feature extraction and classification. This repository contains Python code that implements a centroid-based pattern recognition system. The system extracts features from images based on the centroids of grid cells and uses a Decision Tree Classifier or Random Forest Classifier for image classification.
## Features
•Extracts features from images based on centroids of grid cells.
•Utilizes a Decision Tree Classifier or Random Forest Classifier for image classification.
•Allows for customization of grid dimensions.
This project aims to demonstrate the application of centroid-based feature extraction in pattern recognition tasks, particularly in image classification. By dividing the image into a grid and calculating the centroids of each grid cell, we can extract meaningful features for classification.
In initial experiments, a Decision Tree Classifier was used for classification. However, further experimentation revealed that utilizing a Random Forest Classifier yielded higher accuracy. This highlights the importance of exploring different classifiers and parameters to achieve optimal performance in pattern recognition tasks.
## Note
•This implementation is for educational purposes and may require customization for specific applications.
•Ensure that images used for classification are appropriately preprocessed and labeled.