GraceSevillano / VIBOT-Scene-Segmentation-Labs

This repository is dedicated to the collection of 10 laboratory reports from the "Scene Segmentation and Interpretation" course, a key component of the Master Degree in Vision and Robotics (VIBOT). Each lab focuses on a specific aspect of scene segmentation and interpretation, employing various techniques from edge detection to image restoration.

VIBOT-Scene-Segmentation-Labs

Overview

This repository is dedicated to the collection of 10 laboratory reports from the "Scene Segmentation and Interpretation" course, a key component of the Master Degree in Vision and Robotics (VIBOT). Each lab, authored by me, focuses on a specific aspect of scene segmentation and interpretation, employing various techniques from edge detection to image restoration.

Labs Summary

1. First Report: Edge Detectors Performance - Evaluation of different edge detectors against noise.
2. Second Report: Region Labelling - Techniques for labelling regions in an image.
3. Third Report: DFT (FFT) Filter Implementation - Utilizing DFT (FFT) for frequency dictionary and filtering.
4. Fourth Report: Canny-Deriche Filter Implementation - Implementing the Canny-Deriche filter for edge detection.
5. Fifth Report: Window Detection in Building Images - Using segment primitives for window detection.
6. Sixth Report: Image Compression Techniques - Comparing FFT and DCT compression techniques via PSNR.
7. Seventh Report: Texture Identification - Implementing a texture identification algorithm.
8. Eighth Report: 2D Motion Estimation - Techniques for implementing 2D motion estimation.
9. Ninth Report: Image Denoising - Multiresolution analysis for denoising using the Haar Wavelet.
10. Tenth Report: Image Restoration - Comparing techniques for noise reduction in corrupted images, including MRF denoising.

Objectives

• To facilitate a practical understanding of scene segmentation and interpretation techniques.
• To provide a structured environment for the application of theoretical knowledge.
• To encourage the exploration of various computer vision methodologies and their implications.

Usage

Each laboratory assignment in this repository includes:

• Images that were analyzed during the lab.
• The Python code or Jupyter notebook that was utilized.
• The final report presented in PDF format.

These materials are intended to offer a comprehensive view of the lab work, from the initial analysis to the final conclusions. Students, educators, and enthusiasts are encouraged to explore these resources to enhance their understanding of scene segmentation and interpretation.