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This paper presents a mobile app developed to optimize the drone flight in a precision agriculture scenario. The Android platform was chosen, once it have free tools for development and there are many different API that could be used to solve this problem. For map presentation, as well as geocoding manipulation, Google tools were used. For the optimization, an algorithm based on recursive auctions was used, which has the characteristic of finding feasible solutions even in complex scenarios. The app has been tested and achieved feasible results for large scenarios with over a thousand waypoints in just few minutes, even running on a mobile device. It highlights the mobile app, and the recursive auction algorithm, it is an important solution for drone flight optimization in rural areas, where thereis usually no possibility to run the application on traditional computers, as usually there is no access to the Internet.

This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These features are extracted from the neighborhood of each pixel. The approach also uses the ELEMENT methodology, which consists of creating a pixelclassification controlled by region-growing where the result of the classification affects further classifications of pixels. The Random Forests classifier is used to predict whether the pixel belongs to the vessel structure. The approach achieved the best accuracy in the literature (95.48%) outperforming unsupervised state-of-the-art approaches.

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RESUMOO aplicativo computacional para a análise de