Les techniques de description des éléments caractéristiques d’une image sont omniprésentes dans de nombreuses applications de vision par ordinateur. Nous proposons à travers ce manuscrit une extension, pour décrire (représenter) et apparier les éléments caractéristiques des images. L’extension proposée consiste en une approche originale pour apprendre, ou estimer, la présence sémantique des éléments caractéristiques locaux dans les images. L’information sémantique obtenue est ensuite exploitée, en conjonction avec le paradigme de sac-de-mots, pour construire un descripteur d’image performant. Le descripteur résultant, est la combinaison de deux types d’informations, locale et contextuelle-sémantique. L’approche proposée peut être généralisée et adaptée à n’importe quel descripteur local d’image, pour améliorer fortement ses performances spécialement quand l’image est soumise à des conditions d’imagerie contraintes. La performance de l’approche proposée est évaluée avec des images réelles aussi bien dans les deux domaines, 2D que 3D. Nous avons abordé dans le domaine 2D, un problème lié à l’appariement des éléments caractéristiques dans des images. Dans le domaine 3D, nous avons résolu les problèmes d’appariement et alignement des vues partielles tridimensionnelles. Les résultats obtenus ont montré qu’avec notre approche, les performances sont nettement meilleures par rapport aux autres méthodes existantes. / This manuscript presents an extension of feature description and matching strategies by proposing an original approach to learn the semantic information of local features. This semantic is then exploited, in conjunction with the bag-of-words paradigm, to build a powerful feature descriptor. The approach, ended up by combining local and context information into a single descriptor, is also a generalized method for improving the performance of the local features, in terms of distinctiveness and robustness under geometric image transformations and imaging conditions. The performance of the proposed approach is evaluated on real world data sets as well as in both the 2D and 3D domains. The 2D domain application addresses the problem of image feature matching while in 3D domain, we resolve the issue of matching and alignment of multiple range images. The evaluation results showed our approach performs significantly better than expected results as well as in comparison with other methods.

In this research work, we develop an obstacle detection and emergency exit sign recognition system on a mobile phone by extending the feature from accelerated segment test detector with Harris corner filter. The first step often required for many vision based applications is the detection of objects of interest in an image. Hence, in this research work, we introduce emergency exit sign detection method using colour histogram. The hue and saturation component of an HSV colour model are processed into features to build a 2D colour histogram. We backproject a 2D colour histogram to detect emergency exit sign from a captured image as the first task required before performing emergency exit sign recognition. The result of classification shows that the 2D histogram is fast and can discriminate between objects and background with accuracy. One of the challenges confronting object recognition methods is the type of image feature to compute. In this work therefore, we present two feature detectors and descriptor methods based on the feature from accelerated segment test detector with Harris corner filter. The first method is called Upright FAST-Harris and binary detector (U-FaHB), while the second method Scale Interpolated FAST-Harris and Binary (SIFaHB). In both methods, feature points are extracted using the accelerated segment test detectors and Harris filter to return the strongest corner points as features. However, in the case of SIFaHB, the extraction of feature points is done across the image plane and along the scale-space. The modular design of these detectors allows for the integration of descriptors of any kind. Therefore, we combine these detectors with binary test descriptor like BRIEF to compute feature regions. These detectors and the combined descriptor are evaluated using different images observed under various geometric and photometric transformations and the performance is compared with other detectors and descriptors. The results obtained show that our proposed feature detector and descriptor method is fast and performs better compared with other methods like SIFT, SURF, ORB, BRISK, CenSurE. Based on the potential of U-FaHB detector and descriptor, we extended it for use in optical flow computation, which we termed the Nearest-flow method. This method has the potential of computing flow vectors for use in obstacle detection. Just like any other new methods, we evaluated the Nearest flow method using real and synthetic image sequences. We compare the performance of the Nearest-flow with other methods like the Lucas and Kanade, Farneback and SIFT-flow. The results obtained show that our Nearest-flow method is faster to compute and performs better on real scene images compared with the other methods. In the final part of this research, we demonstrate the application potential of our proposed methods by developing an obstacle detection and exit sign recognition system on a camera phone and the result obtained shows that the methods have the potential to solve this vision based object detection and recognition problem.