AUTOMATIC FPGA BASED IMPLEMENTATION OF A CLASSIFICATION TREE

We propose a method of automatic hardware implementation of a decision rule based on the Adaboost algorithm. We review the principles of the classification method and we evaluate its hardware implementation cost in term of FPGA’s slice, using different weak classifiers based on the general concept of hyperrectangle. We show how to combine the weak classifiers in order to find a good trade-off between classification performances and hardware implementation cost. We present results obtained using examples coming from UCI databases

FPGA-DSP BASED IMPLEMENTATION OF A STEREOVISION ALGORITHM APPLIED TO 3D FACE ACQUISITION

Abstract: We propose in this paper an FPGA-DSP based implementation of 3D data acquisition of human face. This technique consistes in acquiring data in three dimensions from two cameras. The aim is to implement an algorithm which makes it possible to obtain a three-dimensional space from two two-dimensional spaces: two images coming from the two cameras. Several implementations have already been considered. We propose a new simple real-time implementation, based on a multiprocessor approach (FPGA-DSP) allowing to consider an embedded and configurable processing. Then we show our method which provides depth map of face, dense and reliable. 1

Automatic determination of aortic compliance based on MRI and adapted curvilinear detector

International audienc

Automatic hardware implementation tool for a discrete adaboost-based decision algorithm

Abstract. We propose a method and a tool for automatic generation of hardware implementation of a decision rule based on the Adaboost algorithm. We review the principles of the classification method and we evaluate its hardware implementation cost in terms of FPGA’s slice, using different weak classifiers based on the general concept of hyperrectangle. The main novelty of our approach is that the tool allows the user to find automatically an appropriate trade-off between classification performances and hardware implementation cost, and that the generated architecture is optimised for each training process. We present results obtained using Gaussian distributions and examples from UCI databases. Finally, we present an example of industrial application of real-time textured image segmentation

classification with SVM, Boosting and Hyperrectangle based method

Real-time flaw detection on complex object: comparison of results usin