Détection d'objets multi-parties par algorithme adaptatif et optimisé
|Abstract:||In this thesis, we propose improvements to an existing unknown shape object detection method that uses simple primitives. Firstly, we eliminate cases where no results were obtained with some images using an adaptive algorithm by removing most of the fixed thresholds, assuring a certain number of primitive groups at each step. Secondly, adding some optimizations and a parallel version of the algorithm make the running time of this new algorithm reasonable. Thirdly, we approach the problem of the redundant solutions by adding a new structuring step that will reduce their number without affecting their variety using hierarchical clustering. Finally, we adjust some parameters and results are produced using three sets of 10 images. We prove in an objective manner that the obtained results are better than those of the previous method.|
|Document Type:||Mémoire de maîtrise|
|Open Access Date:||19 April 2018|
|Collection:||Thèses et mémoires|
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