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Etude et prédiction d'attention visuelle avec les outils d'apprentissage profond en vue d'évaluation des patients atteints des maladies neuro-dégénératives

Abstract : This thesis is motivated by the diagnosis and the evaluation of the dementia diseasesand with the aim of predicting if a new recorded gaze presents a complaint of thesediseases. Nevertheless, large-scale population screening is only possible if robust predictionmodels can be constructed. In this context, we are interested in the design and thedevelopment of automatic prediction models for specific visual content to be used in thepsycho-visual experience involving patients with dementia (PwD). The difficulty of sucha prediction lies in a very small amount of training data.Visual saliency models cannot be founded only on bottom-up features, as suggested byfeature integration theory. The top-down component of human visual attention becomesprevalent as human observers explore the visual scene. Visual saliency can be predictedon the basis of seen data. Deep Convolutional Neural Networks (CNN) have proven tobe a powerful tool for prediction of salient areas in static images. In order to constructan automatic prediction model for the salient areas in natural and intentionally degradedvideos, we have designed a specific CNN architecture. To overcome the lack of learningdata we designed a transfer learning scheme derived from bengio’s method. We measureits performances when predicting salient regions. The obtained results are interestingregarding the reaction of normal control subjects against degraded areas in videos. Thepredicted saliency map of intentionally degraded videos gives an interesting results comparedto gaze fixation density maps and other reference models.
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Souad Chaabouni. Etude et prédiction d'attention visuelle avec les outils d'apprentissage profond en vue d'évaluation des patients atteints des maladies neuro-dégénératives. Autre [cs.OH]. Université de Bordeaux; Université de Sfax (Tunisie), 2017. Français. ⟨NNT : 2017BORD0768⟩. ⟨tel-02408326⟩

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