Our mission

We provide tools and consultancy for design of pattern recognition algorithms. Our mission is to help our clients to design the best recognition algorithms building on their specific sensor technologies. more about our services...

PRSD Studio

PRSD Studio is a software toolkit facilitating design and deployment of advanced pattern recognition algorithms. It consists of the Matlab-based PRSD Toolbox and the ANSI C execution library libPRSD which can be easily embedded in custom applications.

System Design Traditionally-used scheme PR Sys Design solution

Fast approximated k-NN classifier

k-th nearest neighbor is a robust data driven classifier. However, the more training samples it uses, the slower it gets in execution. This is because distances from each new observation to all stored training examples (prototypes) need to be computed.
We have developed an approximated k-NN computing distances only to potential nearest neighbors and hence significantly speeding the k-NN execution. Although our strategy to localize the nearest neighbor search is similar to the well-known kd-tree approach, it does not employ per-feature splitting but works directly on distances. Therefore, it scales well to higher dimensionalities unlike the kd-tree which becomes inpractical for more than 20D feature spaces.

Advanced Patter Recognition Course October 2008

In the end of October the Advanced Pattern Recognition course took place at Delft Technical University. The APRcourse is organized by TUDelft and PR Sys Design and is specifically tailored for people from industry in need of state of the art pattern recognition solutions. There were 12 participants from all over the world (Singapore, Canada and several European countries). This time we invited the participants to briefly introduce themselves and the type of problems they are interested in or dealing with in their work. This gave the opportunity to learn each other interests and background, and stimulated more interactions with the teaching staff and the other participants. A cheerful and cooperative atmosphere developed during the week, boosted also by common dinners and pub celebrations. We have really enjoyed the week and wold like to thank all participants for their contribution. It has been a pleasure to meet and work with you all! Thanks to our “knee man” Shameem we have a group picture of almost the complete team.