Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Automatic Hardware Implementation Tool for a Discrete Adaboost-Based Decision Algorithm | EURASIP Journal on Applied Signal Processing 2005 7 1035-1046 2005 Hindawi Publishing Corporation Automatic Hardware Implementation Tool for a Discrete Adaboost-Based Decision Algorithm J. Miteran Le2i UMR CNRS 5158 Aile des Sciences de ringenieur Université de Bourgogne BP 47870 21078 Dijon Cedex France Email miteranj@ J. Matas Center for Machine Perception CVUT Karlovo Namesti 13 Prague Czech Republic Email matas@ E. Bourennane Le2i UMR CNRS 5158 Aile des Sciences de l lngenieur Universite de Bourgogne BP 47870 21078 Dijon Cedex France Email ebourenn@ M. Paindavoine Le2i UMR CNRS 5158 Aile des Sciences de l lngenieur Universite de Bourgogne BP 47870 21078 Dijon Cedex France Email paindav@ J. Dubois Le2i UMR CNRS 5158 Aile des Sciences de l lngenieur Universite de Bourgogne BP 47870 21078 Dijon Cedex France Email Received 15 September 2003 Revised 16 July 2004 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 tradeoff between classification performances and hardware implementation cost and that the generated architecture is optimized 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. Keywords and phrases Adaboost FPGA classification hardware image segmentation. 1. INTRODUCTION In this paper we propose a method of automatic generation of hardware implementation of a particular decision rule. This paper focuses mainly on .
