Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for different applications such as automated map generation and change detection. Roads are also important to detect other structures such as buildings and urban areas. In this thesis, the road extraction approach is based on Active Contour Models for 1- meter resolution gray level images. Active Contour Models contains Snake Approach. During applications, the road structure was separated as salient-roads, non-salient roads and crossings and extraction of these is provided by using Ribbon Snake and Ziplock Snake methods. These methods are derived from traditional snake model. Finally, various experimental results were presented. Ribbon and Ziplock Snake methods were compared for both salient and non-salient roads. Also these methods were used to extract roads in an image. While Ribbon snake is described for extraction of salient roads in an image, Ziplock snake is applied for extraction of non-salient roads. Beside these, some constant variables in literature were redefined and expressed in a formula as depending on snake approach and a new approach for extraction of crossroads were described and tried.

Dans le cadre de systèmes d’aide à la conduite, nous avons contribué aux approches de stéréovision pour l’extraction de contour, la mise en correspondance des images stéréoscopiques et la détection de véhicules. L’extraction de contour réalisée est basée sur le concept declivity line que nous avons proposé. La declivity line est construite en liant des déclivités selon leur position relative et similarité d’intensité. L’extraction de contour est obtenue en filtrant les declivity lines construites basées sur leurs caractéristiques. Les résultats expérimentaux montrent que la declivity lines méthode extrait plus de l’informations utiles comparées à l’opérateur déclivité qui les a filtrées. Des points de contour sont ensuite mis en correspondance en utilisant la programmation dynamique et les caractéristiques de declivity lines pour réduire le nombre de faux appariements. Dans notre méthode de mise en correspondance, la declivity lines contribue à la reconstruction détaillée de la scène 3D. Finalement, la caractéristique symétrie des véhicules sont exploitées comme critère pour la détection de véhicule. Pour ce faire, nous étendons le concept de carte de symétrie monoculaire à la stéréovision. En conséquence, en effectuant la détection de véhicule sur la carte de disparité, une carte de symétrie (axe; largeur; disparity) est construite au lieu d’une carte de symétrie (axe; largeur). Dans notre concept, des obstacles sont examinés à différentes profondeurs pour éviter la perturbation de la scène complexe dont le concept monoculaire souffre. / In the framework of driving assistance systems, we contributed to stereo vision approaches for edge extraction, matching of stereoscopic pair of images and vehicles detection. Edge extraction is performed based on the concept of declivity line we introduced. Declivity line is constructed by connecting declivities according to their relative position and intensity similarity. Edge extraction is obtained by filtering constructed declivity lines based on their characteristics. Experimental results show that declivity line method extracts additional useful information compared to declivity operator which filtered them out. Edge points of declivity lines are then matched using dynamic programming, and characteristics of declivity line reduce the number of false matching. In our matching method, declivity line contributes to detailed reconstruction of 3D scene. Finally, symmetrical characteristic of vehicles are exploited as a criterion for their detection. To do so, we extend the monocular concept of symmetry map to stereo concept. Consequently, by performing vehicle detection on disparity map, a (axis; width; disparity) symmetry map is constructed instead of an (axis; width) symmetry map. In our stereo concept, obstacles are examined at different depths thus avoiding disturbance of complex scene from which monocular concept suffers.