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A tracking system that will be used for Augmented Reality (AR) applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay; occurring because of the delay in estimating the motion of the user and displaying images based on this estimate. This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models (FAMM) was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS-IMU fusion approach due to additional estimates from a cam-

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Abstract-This paper extends the classical segmentation method known as region growing using a new localization method which uses algorithms from computer science and computer vision. Described method was successfully applied to the problem of cell localization and segmentation. The method proves to be useful as part of a whole autonomous segmentation process. Furthermore, methods from spatial statistics were employed in order to model the spatial distribution of the localized objects. In-depth discussion of these methods including density plots and the Ripley's K function is presented along with the results for test set used in the experiments.