Approches parcimonieuses pour la sélection de variables et la classification: application à la spectroscopie IR de déchets de bois

Abstract : In this thesis innovative techniques for sorting wood wastes are developed. The idea is to combine infrared spectrometry techniques with robust data processing methods for classification task. After exposing the context of the work in the first chapter, a state of the art on the spectral data classification is presented in the chapter 2. The third chapter deals with variable selection problem using sparse approaches. In particular we propose to extend some greedy methods for the simultaneous sparse approximation. The simulations performed for the approximation of an observation matrix validate the advantages of the proposed approaches. In the fourth chapter, we develop variable selection methods based on simultaneous sparse and regularized representation, to increase the performances of SVM classifier for the classification of NIR spectra and hyperspectral images of wood wastes. In the final chapter we present the improvements made to the existing sorting systems. The results of the conducted tests using the processing software confirm that significant benefits can be achieved in terms of recycled wood quantities
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Contributor : Leila Belmerhnia <>
Submitted on : Tuesday, September 19, 2017 - 12:01:48 PM
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  • HAL Id : tel-01685018, version 2

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Leila Belmerhnia. Approches parcimonieuses pour la sélection de variables et la classification: application à la spectroscopie IR de déchets de bois. Traitement du signal et de l'image [eess.SP]. Université de Lorraine, 2017. Français. ⟨NNT : 2017LORR0039⟩. ⟨tel-01685018v2⟩

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