In this thesis, rotation, scale and translation (RST) invariant automatic target recognition (ATR) for satellite imagery is presented. Template matching is used to realize the target recognition. However, unlike most of the studies of template matching in the literature, RST invariance is required in our problem, since most of the time we will have only a small number of templates of each target, while the targets to be recognized in the scenes will have various orientations, scaling and translations. RST invariance is studied in detail and implemented with some of the competing methods in the literature, such as Fourier-Mellin transform and bipectrum combined with log-polar mapping. Phase correlation and normalized cross-correlation are used as similarity metrics. Encountered drawbacks were overcome with additional operations and modifications of the algorithms. ATR using reconstruction of the target image with respect to the template, based on bispectrum, log-polar mapping and phase correlation outperformed the other methods and successful recognition was realized for various target types, especially for targets on relatively simpler backgrounds, i.e. containing little or no other objects.

Image registration, the process of finding the transformation that best maps one image to another, is an important tool in document image processing. Having properly-aligned microfilm images can help in manual and automated content extraction, zoning, and batch compression of images. An image registration algorithm is presented that quickly identifies the global affine transformation (rotation, scale, translation and/or shear) that maps one tabular document image to another, using the Fourier-Mellin Transform. Each component of the affine transform is recovered independantly from the others, dramatically reducing the parameter space of the problem, and improving upon standard Fourier-Mellin Image Registration (FMIR), which only directly separates translation from the other components. FMIR is also extended to handle shear, as well as different scale factors for each document axis. This registration method deals with all transform components in a uniform way, by working in the frequency domain. Registration is limited to foreground pixels (the document form and printed text) through the introduction of a novel, locally adaptive foreground-background segmentation algorithm, based on the median filter. The background removal algorithm is also demonstrated as a useful tool to remove ambient signal noise during correlation. Common problems with FMIR are eliminated by background removal, meaning that apodization (tapering down to zero at the edge of the image) is not needed for accurate recovery of the rotation parameter, allowing the entire image to be used for registration. An effective new optimization to the median filter is presented. Rotation and scale parameter detection is less susceptible to problems arising from the non-commutativity of rotation and "tiling" (periodicity) than for standard FMIR, because only the regions of the frequency domain directly corresponding to tabular features are used in registration. An original method is also presented for automatically obtaining blank document templates from a set of registered document images, by computing the "pointwise median" of a set of registered documents. Finally, registration is demonstrated as an effective tool for predictive image compression. The presented registration algorithm is reliable and robust, and handles a wider range of transformation types than most document image registration systems (which typically only perform deskewing).

Le SLAM, « Simultaneous Localisation And Mapping », représente à l'heure actuelle l'une des principales thématiques investiguées dans le domaine des robots mobiles autonomes. Il permet, à l'aide de capteurs extéroceptifs (laser, caméra, radar, etc.) et proprioceptifs (odomètre, gyromètre, etc.), de trouver l'orientation et la localisation d'un robot dans un environnement extérieur vaste, inconnu ou modifié, avec la possibilité de créer une carte au fur et à mesure des déplacements du véhicule. Les travaux de thèse décrits dans ce manuscrit s'intègrent dans ce courant de recherche. Ils visent à développer un SLAM innovant qui utilise un radar à modulation de fréquence continue « FMCW » comme capteur extéroceptif. Ce capteur est insensible aux conditions climatiques et possède une portée de détection importante. Néanmoins, c'est un capteur tournant qui, dans une utilisation mobile, va fournir des données corrompues par le déplacement du véhicule. Pour mener à bien ces travaux, nous avons proposés différentes contributions : une correction de la distorsion par l'utilisation de capteurs proprioceptifs ; le développement d'une technique de localisation et cartographie simultanées nommée RS-SLAM-FMT qui effectue un scan matching sur les observations et utilise un algorithme estimatif de type EKF-SLAM ; l'utilisation, pour la première fois en SLAM, de la mise en correspondance par Transformée de Fourier-Mellin pour réaliser l'opération de scan matching ; la création d'un outil expérimental pour déterminer la matrice de covariance associée aux observations ; des tests de robustesse de l'algorithme dans des conditions d'utilisation réelles : dans des zones avec un faible nombre de points d'intérêts, sur des parcours effectués à vitesse élevée, dans des environnements péri-urbains avec une forte densité d'objets mobiles ; la réalisation d'une application temps réel pour le test du procédé sur un véhicule d'exploration qui se déplace dans un environnement extérieur vaste. / Simultaneous Localization And Mapping (SLAM) is one of the main topics investigated in the field of autonomous mobile robots. It permits the Localization and mapping of a robot in a large outdoor environment, using exteroceptive (laser, camera, radar, etc.) and proprioceptive (odometer, gyroscope, etc.) sensors. The objective of this PhD thesis is to develop innovative SLAM that uses a radar frequency modulated continuous wave (FMCW) as an exteroceptive sensor. Microwave radar provides an alternative solution for environmental imaging and overcomes the shortcomings of laser, video and sonar sensors such as their high sensitivity to atmospheric conditions. However, data obtained with this rotating range sensor is adversely affected by the vehicle’s own movement. In order to efficiently manage the work, we propose : a correction, on-the-fly, of the rotating distortion with an algorithm that uses the proprioceptive sensors’ measurements ; development of a new technique for simultaneous localization and mapping named RS-SLAM-FMT ; for the first time in SLAM, the use of the Fourier-Mellin Transform provides an accurate and efficient way of computing the rigid transformation between consecutive scans ; creation of an experimental tool to determine the covariance matrix associated with the observations. It is based on an uncertainty analysis of a Fourier-Mellin image registration ; tests of the robustness of the SLAM algorithm in real-life conditions : in an environment containing a small number of points of interest, in real full speed driving conditions, in peri-urban environments with a high density of moving objects etc. ; creation and experiment of a real-time RS-SLAM-FMT implemented on a mobile exploration vehicle in an extensive outdoor environment.