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Descripteurs de Fourier inspirés de la structure du cortex visuel primaire humain : Application à la reconnaissance de navires dans le cadre de la surveillance maritime

Abstract : In this thesis, we develop a supervised object recognition method using new global image descriptors inspired by the model of the human primary visual cortex V1. Mathematically speaking, the latter is modeled as the semi-discrete roto-translation group SE (2,N)=R² x ZN semi-direct product between R² and ZN. Therefore, our technique is based on generalized and rotational Fourier descriptors defined in SE (2,N) , and which are invariant to natural geometric transformations (translations, and rotations). Furthermore, we show that such Fourier descriptors are weakly complete, in the sense that they allow to distinguish over an open and dense set of compactly supported functions in L² (SE(2,N)) , hence between real-world images. These descriptors are later used in order to feed a Support Vector Machine (SVM) classifier for object recognition purposes. We have conducted a series of experiments aiming both at evaluating and comparing the performances of our method against existing both local - and global - descriptor based state of the art techniques, using the RL, the CVL, and the ORL face databases, and the COIL-100 image database (containing various types of objects). The obtained results have demonstrated that our approach was able to compete with many existing state of the art object recognition techniques, and to outperform many others. These results have also shown that our method is robust to noise. Finally, we have applied the proposed method on vessels recognition in the framework of maritime surveillance.
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Amine Bohi. Descripteurs de Fourier inspirés de la structure du cortex visuel primaire humain : Application à la reconnaissance de navires dans le cadre de la surveillance maritime. Traitement des images [eess.IV]. Université de Toulon, 2017. Français. ⟨NNT : 2017TOUL0002⟩. ⟨tel-01611384⟩

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