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This paper focuses on optimizing the classifier and the used feature space of a semi-automatic approach for water floating agents classification. Our approach proposes a method for pollutant/non pollutant river waste labeling. For this task we consider a soft-margin kernel SVM classifier. The optimization process consists in determining the optimal features for classification with regard to two scores:...
The paper presents different approaches in improving the digital image acquisition with focus on shape recognition. The study is part of a larger project focused on image pre-processing optimization in order to obtain a higher efficiency of automated features recognition. The final target would be to use the results in the field of number plate recognition in road traffic. Structured in two main sections,...
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