LMA: A generic and efficient implementation of the Levenberg-Marquardt algorithm
The Levenberg-Marquardt algorithm (LMA) is a very popular non-linear optimisation algorithm and hundreds of implementations have already been released. However, none of them offer at the same time a high level of genericity, a friendly syntax and a high computational performance. We propose a solution to gather all those advantages in one library named LMA. LMA uses compile time algorithms to design a code specific to the given optimization problem.
The source code is available here and the corresponding publication in the Journal of Software: Practice and Experience is accessible here. The features of LMA are presented and the performances are compared with the state-of-the-art best alternatives through extensive benchmarks on different kind of problems
Kernel Based Tracking
We present a solution for realtime tracking of a planar pattern. Tracking is seen as the estimation of a parametric function between observations and motion and we propose an extension of the learning based approach presented simultaneously by Cootes and al. and by Jurie and Dhome. We show that the hyperplane classic algorithm is a specific case of a more generic linearly-weighted sum of fixed non-linear basis functions model. The weights associated to the basis functions (kernel functions) of the model are estimated from a training set of perturbations and associated observations generated in a synthetic way. The resulting tracker is then composed by several iterations on trackers learned with coarse to fine magnitude of perturbations.
You can download a zip file containing an exe software of the Kernel Based Tracking Algorithm presented in this publication.
Moreover, a short description of the method is available here.
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NT2 library
The NT2 library used in this software is hosted on GitHub.