Computer-Vision-and-Pattern-Recognition
Computer Vision and Pattern Recognition
A repository showcasing projects and implementations in Computer Vision and Pattern Recognition. This repository includes various techniques and algorithms related to image processing, object detection, image segmentation, feature extraction, and more. Whether you’re learning or looking to enhance your computer vision skills, this repo has something for you.
🚀 Overview
Computer Vision (CV) and Pattern Recognition (PR) are fields of artificial intelligence that focus on enabling machines to interpret and understand visual data. This repository provides hands-on projects that cover a range of fundamental and advanced techniques used in these domains.
Key Techniques:
- Image Classification: Using machine learning or deep learning models to classify images.
- Object Detection: Identifying and locating objects in images or video frames.
- Image Segmentation: Dividing an image into segments to simplify or change its representation.
- Feature Extraction: Extracting key features from images for tasks like matching, recognition, and tracking.
- Face Recognition: Identifying or verifying individuals from digital images or videos.
- Optical Character Recognition (OCR): Converting different types of documents, such as scanned paper documents or PDFs, into editable and searchable data.
🔧 Features
- Implementations: Code examples for each technique, ranging from beginner to advanced levels.
- Pretrained Models: If applicable, pretrained models for object detection, classification, etc., to make your work faster and more efficient.
- Datasets: Datasets used for training the models, or guidance on where to find them.
- Visualizations: Interactive Jupyter Notebooks with visualizations for better understanding of the concepts.
🌐 Demo
You can try out the various projects in this repository by running the provided Python scripts or Jupyter notebooks. The projects include:
- Object detection using YOLO (You Only Look Once)
- Image classification with Convolutional Neural Networks (CNN)
- Face detection and recognition with OpenCV
-
Pattern recognition with classic machine learning algorithms like k-NN, SVM, and decision trees
- Make sure you have the following dependencies installed: Python 3.x OpenCV TensorFlow / Keras PyTorch scikit-learn scikit-image NumPy Matplotlib Pandas
🛠️ Technologies Used
- Python 3.x
- OpenCV
- TensorFlow / Keras
- PyTorch
- scikit-learn
- scikit-image
- Dlib (for face recognition and landmarks)
- NumPy
- Matplotlib
- Seaborn
- Pandas
- Jupyter Notebooks
- Tesseract (for OCR)
- YOLO / SSD (for object detection)
📂 Project Structure
Computer-Vision-and-Pattern-Recognition/
├── images/ # Sample images for testing and training
├── notebooks/ # Jupyter notebooks for each project
├── datasets/ # Datasets for training
├── README.md # Project documentation
Running the Code Clone the repository: bash git clone https://github.com/Someshdiwan/Computer-Vision-and-Pattern-Recognition
Open any Jupyter notebook or Python script and run the code cells to see the implementations in action.
🌟 Show Your Support If you like this project, please consider giving it a ⭐ on GitHub! — 🤝 Contributing We welcome contributions to improve the repository! If you have any enhancements, bug fixes, or new project ideas, feel free to fork the repository, make changes, and submit a pull request. —