Image Recognition has been a widely studied topic recently. Researchers and companies are trying to find robust solutions to recognize images (advertisement flyers, movie posters, cards …) and objects using remote servers or directly on the devices. At iplimage, we tried and implemented a few image recognition engines lately, and I would like to share some results.
This article describes how to construct a geometric transformation between two images representing two views of an object.
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.
I’m trying to implement an “hybrid” image tracker. This technique has the objective to detect certain type of images to enable augmented reality. Here are early results!
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