On this page, I will publish notes, essays, reports and other writings that have been produced by myself. The contents can be changed without notice or any form of reporting. Comments can be sent to me personally using the contact form.
I discuss the importance of connected components in image processing and computer vision, focusing on their role in image segmentation and analysis. In this text, I explore how connected components, especially within Max-Trees, can be used to derive meaningful information from images by computing various attributes. I also provide GNU Octave implementations of these attributes to facilitate practical application.
I introduce the concept of Max-Trees, an advanced technique in image processing that structures image data beyond simple pixel sequences. I explain the process of constructing a Max-Tree and its importance in image analysis, including a practical GNU Octave implementation. This explanation builds on research from my 2021 master’s thesis on detecting astronomical objects using machine learning.