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Image Understanding / Photogrammetry Course - Homeworks & Lab Notebooks

This repository contains the homeworks and lab notebooks I completed during my master's degree course in "Image Understanding" (equivalent to Photogrammetry). The course, taught by Assoc. Professor Mustafa Özuysal from Izmir Institute of Technology, focuses on image processing techniques and methodologies for analyzing image content, as well as key geometric and mathematical concepts relevant to computer vision.

Course Overview

The "Image Understanding" course covers the following topics:

  • Image Representation: Memory structures for image data, pixel-based representations.
  • Basic Image Processing: Image filtering, enhancement, and transformations.
  • Advanced Image Processing: Algorithms for noise reduction, edge detection, and feature extraction.
  • Keypoint Detection and Matching: Techniques such as SIFT, SURF, and ORB for feature extraction and matching between images.
  • Planar Projective Geometry: Understanding image transformations in planar spaces.
  • 3D Projective Geometry: Representation of 3D scenes and transformations between different views.
  • Basic Camera Geometry: Fundamentals of camera models and image formation.
  • Epipolar Geometry: Understanding camera relations and constraints between two views.
  • Optimization Techniques: Techniques for refining camera models and improving feature matches.
  • Multiview Geometry: Handling multiple views for 3D scene reconstruction.
  • Dense Image Features: Algorithms for computing dense features in images.
  • Object Category Detection: Techniques for detecting object categories from image data.

Structure of the Repository

The repository is organized into folders for homeworks and lab notebooks based on the main topics of the course:

  • Homeworks/: Contains the assignments I completed, covering theoretical questions and practical tasks.
  • Lab Notebooks/: Includes Jupyter notebooks (or other format) used during the course labs for hands-on experiments in image processing and analysis.

Each folder contains a detailed description of the tasks, algorithms implemented, and results.

Getting Started

  1. Clone the repository:

    git clone https://github.com/mstftmk/Image-Understanding-Photogrammetry.git
cd Image-Understanding-Photogrammetry

  2. Navigate to the relevant homework or lab notebook folder for the desired topic.

  3. Follow the instructions within each notebook or homework folder for setup and execution details.

Prerequisites

To run the lab notebooks and homeworks, the following libraries and tools are required:

  • Python 3.x
  • OpenCV
  • NumPy
  • Matplotlib
  • Jupyter Notebook

License

This repository is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or clarifications, feel free to contact me.