In this post, i will remind the reader about the famous Gauss Newton optimization technique. We will see how Jacobian matrices are required to achieve such a kind of optimization and how it applies to our common computer vision problems.
This post reminds quickly how to diagonalize a dense matrix A using the Singular Value Decomposition (SVD).
There are many ways to express affine transformations. In this post are discussed several possibilities and the choice I’m used to make when implementing an application that requires 3D transformations.
For my own comprehension (i hope that it can help others too), here is a short post about the very famous Kalman filter. In 1960, R. E. Kalman published his paper presenting a recursive solution for the discrete-data filtering problem. Continue reading
LU Decomposition consists in rewriting a square matrix $A$ into an unit lower triangular matrix $L$ and an upper triangular matrix $U$. Continue reading