Computer Vision Projects & Research

A comprehensive collection of computer vision implementations covering classical image processing techniques, deep learning-based object detection, image classification, pose estimation, and specialized applications in medical imaging and biometric systems.

Overview

This repository documents practical explorations and implementations across the computer vision domain, with a focus on real-world applications using state-of-the-art frameworks and methodologies. Projects range from fundamental image processing operations to advanced neural network architectures for specific computer vision tasks.

Key Project Areas

Deep Learning & YOLO Ecosystem

Implementation of YOLO11 and YOLO12 models for various computer vision tasks:

• Object Detection: Real-world scenarios including aerial vehicle detection
• Instance Segmentation: Medical imaging applications (brain tumor segmentation)
• Image Classification: Multi-class classification tasks (bird species classification)
• Pose Estimation: Human activity recognition and safety detection (shoplifting detection)
• Oriented Bounding Box Detection: Advanced object localization with rotation angles

Classical Computer Vision

Foundation work with OpenCV covering:

• Image I/O and video processing (webcam, video files, images)
• Color space transformations and analysis
• Image filtering and blurring techniques
• Threshold operations (global and adaptive)
• Edge detection and contour analysis
• Morphological operations and shape analysis
• Color-based object detection and tracking

Specialized Applications

• Medical Imaging: Skin cancer detection using YOLO12
• Biometric Systems: Bangla sign language letter recognition
• Traffic Analysis: Vehicle speed estimation and tracking

Technologies & Dependencies

Core Libraries:

• OpenCV (4.6.0) - Classical and modern computer vision algorithms
• YOLO11/YOLO12 - State-of-the-art real-time object detection framework
• NumPy (1.23.4) - Numerical computing and array operations
• Pillow (9.2.0) - Image processing utilities
• TensorFlow/PyTorch - Deep learning backends (through YOLO implementations)

Environment: Python 3.7+

Getting Started

Installation

# Clone the repository
git clone <repository-url>
cd computer-vision

# Install dependencies
pip install -r requirements.txt

Running Notebooks

All project implementations are provided as Jupyter notebooks for interactive exploration:

# Launch Jupyter
jupyter notebook

# Open any .ipynb file to explore specific projects

Project Highlights

• Production-Ready Models: Implementations use pre-trained weights from official YOLO repositories
• Real-World Data: Projects tested on diverse datasets including aerial imagery, medical scans, and real-time video feeds
• Modular Design: Reusable components and utility functions for extensibility
• Documentation: Each project includes cell-level documentation and usage examples

Use Cases

✓ Real-time object detection in video streams
✓ Medical image analysis and diagnosis assistance
✓ Biometric recognition systems
✓ Traffic monitoring and analytics
✓ Scene understanding and activity recognition

Future Work

This repository is continuously updated with new methodologies, advanced architectures, and emerging applications in the computer vision space.

Contact & Collaboration

For inquiries regarding specific projects or collaboration opportunities, please refer to the project documentation within each implementation.

---

Note: All code is provided for educational and research purposes. Ensure proper licensing compliance when using pre-trained models in production environments.