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By leveraging multiple color spaces such as RGB, HSV, and YCbCr, the system provides robust and accurate skin segmentation under various illumination and ethnic conditions.\n\n## Features\n- **Multi-Color Space Analysis**: Implements skin detection using RGB, HSV, and YCbCr color spaces.\n- **Thresholding Techniques**: Uses predefined ranges to classify skin and non-skin pixels.\n- **Robustness to Variations**: Effective under diverse lighting, backgrounds, and skin tones.\n- **Efficient Processing**: Low computational cost, enabling real-time applications.\n\n## Project Structure\n- `src/`: Contains the core implementation scripts.\n- `data/`: Includes test images for skin detection.\n- `notebooks/`: Jupyter notebooks for experimentation and visualization.\n- `results/`: Outputs generated during testing.\n- `README.md`: Documentation for the repository.\n\n## Technologies Used\n- **Python**: Core programming language.\n- **OpenCV**: For image processing.\n- **NumPy**: For numerical operations.\n\n## License\nThis project is licensed under the MIT License\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkasraskari%2Fhuman-skin-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkasraskari%2Fhuman-skin-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkasraskari%2Fhuman-skin-detection/lists"}