BLOB Analysis, which stands for “Binary Large Object Analysis”, is a crucial topic in image processing and computer vision. It involves the grouping of pixels in an image based on connectivity and shared properties, such as intensity or color. A key component of BLOB extraction is Connected Component Analysis (CCA) [1], which refers to the process of identifying and labeling connected components in a binary image. In this post, I will discuss CCA and how to extract objects from an image.

I’ve always been fascinated by making things work efficiently, especially in designing control systems. One area that I want to talk about to day is Linear Programming (LP), which is a key aspect of optimization theory. Optimization techniques are widely used in control systems such as Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC).

When writing functions in C and C++, we often need to pass information into the function to perform specific tasks. This process is known as parameter passing.