. La-méthode-'et-', consiste à ne garder que les résultats pour lesquels chacun des MLP donne un score supérieur à 0. Autrement dit, un visage est détecté s'il l'est pour l'ensemble des classifieurs

. La-méthode-'ou, consiste à garder tous les résultats pour lesquels au moins un des classifieurs donne un résultat supérieur à 0. C'est à dire, garder toutes les détections qu

. La-méthode-du, vote' consiste à utiliser trois classifieurs et à classer une image comme 'visage', si au moins deux des trois classifieurs classent l'image dans la catégorie 'visage'. Cette méthode peu être considérée comme une solution intermédiaire entre le 'ET' et le 'OU

. Dans-le-cas-du-système-de-rowley, Ainsi, les résultats de chaque classifieur ne se distinguent que par les variations des conditions initiales de chacun des MLP. Malgré cela, l'association de classifieurs permet une nette diminution du nombre des fausses détections, particulièrement pour les méthodes 'ET' et 'vote'. Un seul classifieur permet d'obtenir un taux de détection de 91, 7% mais pour un total de Détection par mesures de similarité discriminatives 484 fausses détections avec la base de test CMU. Deux classifieurs similaires associés selon la méthode 'ET' permettent d'obtenir un Rappel de 86, 6% avec 79 fausses détections . La méthode de 'vote' avec trois classifieurs permet d'atteindre 88, 4% avec 99 fausses détections. La méthode 'OU' semble moins efficace et atteint un Rappel assez élevé de 90, 3% mais pour un nombre assez important de 185 fausses détections. Dans le chapitre précédent, nous avons associé les résultats de plusieurs corrélations selon une méthode basée sur le 'ET'. En effet, pour qu'une détection soit valide, il est nécessaire que chaque corrélation de l'association renvoie un score supérieur à un seuil s f (section 3.6.1) Nous avons fait ce choix pour plusieurs raisons : ? La première est que la probabilité que la rétine testée dans une image ne soit pas un objet est 'infiniment' plus grande que le contraire, Ainsi, un système de détection doit favoriser en priorité l'élimination des fausses détections. L'association selon la méthode 'ET' permet de limiter au maximum ces fausses détections puisque, si une seule corrélation de l'association renvoie un score indiquant que la rétine testée n'appartient pas à la catégorie 'objet', le système considère que l'image testée appartient alors à la catégorie 'non objet

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