This repository contains the first homework assignment for the "Fundamentals of Data Science - Winter Semester 2023" course. The homework, titled "Image Filtering and Object Identification," encompasses a range of topics from basic image processing techniques to advanced object identification methods. It's designed to provide hands-on experience in implementing various algorithms and understanding their practical applications in data science.
To run the Jupyter Notebook in this repository:
- Ensure you have Python installed on your system. If not, download and install Python from python.org.
- Install Jupyter Notebook using the following command:
pip install notebook. - Clone this repository to your local machine.
- Navigate to the repository's directory and launch Jupyter Notebook:
jupyter notebook. - Open the
HW1.ipynbfile in Jupyter Notebook to view and run the code.
- Ambar Chatterjee: chatterjee.2103610@studenti.uniroma1.it
- Himel Ghosh: ghosh.2102750@studenti.uniroma1.it
- Paul Jezequel: jezequel.2116613@studenti.uniroma1.it
- Mursal Furqan Kumbhar: kumbhar.2047419@studenti.uniroma1.it
- Alessio Lani: lani.1857003@studenti.uniroma1.it
The homework is divided into three main sections with a bonus question:
- Image Filtering (9 points): Understanding and implementing 1D and 2D image filters.
- Multi-Scale Image Representations (9 points): Exploring various image representation techniques, including edge detection and template matching.
- Object Identification (12 points): Techniques and methods for object identification using color histograms and image retrieval.
- Bonus Question - Performance Evaluation (5 points): An optional section that focuses on evaluating the performance of the implemented methods.
The homework should be submitted as a single Jupyter Notebook file (HW1.ipynb). Ensure that all code sections are complete and error-free, and all written responses are provided in Markdown format. The notebook should be detailed, with clear explanations of the methods used and the results obtained.