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In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.

Unmanned Aerial Vehicles (UAV) have been heavily studied in the past decade, where autonomous flights have been a popular subject. More complex applications have led to higher requirements on the autonomous flight algorithms and the absence of performance data complicates the selection of what algorithm to use for various applications. Therefore, this thesis focused in analyzing the performance difference between two methods, Simultaneous Localization AndMapping (SLAM) and Artificial Potential Field Approach (APFA), which are planning and reactive algorithms, respectively. Fundamental dynamics were applied, Feedback Linear Controllers (FBLC)s for stabilization and an odometry position model combined with an inverse dynamics technique that linearizes the non-linear odometry model. The SLAM approach was set up in four steps: landmark extraction which uses a point distance based method for segment separation, combined with a Split-And-Merge algorithm for extracting linear landmarks, data association that validates the landmarks, Extended Kalman Filter (EKF) that uses the landmarks together with the odometry model for estimating the position of the UAV, and a modified TangentBug as the reactive algorithm. The APFA was constructed of two functions, an attractive and a repulsive function. The two methods were implemented on the robotics simulation platform Virtual Robot Experimentation Platform (V-REP), where a quadcopter was used as the model for the UAV. All theory was implemented onto the quadcopter model and embedded scripts were used for communication within V-REP, mainly through internal Application Programming Interface (API)-functions. Furthermore, a script was written that randomly generates three different types of simulation environments. The implementation of both methods was analyzed in reaching an arbitrary goal position in terms of: the most successful, the most time efficient and the safest navigation path. Another thing analyzed was the time- and space-complexity of both implemented methods. The results stated that the implemented APFA and the SLAM approach had approximately equal success rate, SLAM had the safest navigation, was the most time efficient, and had the highest time- and space-complexity for a worst case scenario. One of the conclusions were that improvements could be done in the implementations. Future work includes adding a proper damping method, improving the flaws in the implemented methods as well as to use V-REP as a Robot Operating System (ROS)-node for creating a Software In The Loop (SITL)-simulation, in order to achieve more realistic simulations.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal