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Numerous approaches to object detection and segmentation have been proposed so far. However, these methods are prone to fail in some general situations due to the proper object nature. For instance, classical approaches of object detection and segmentation obtain good results for some specific object classes (i.e. detection of pedestrians or segmentation of cars). However, these methods have troubles...
Detecting and selecting proper landmarks is a key issue to solve Simultaneous Localization and Mapping (SLAM). In this work, we present a novel approach to perform this landmark detection. Our approach is based on using three sources of information: 1) three-dimensional topological information from SLAM; 2) context information to characterize regions of interest (RoI); and 3) features extracted from...
Different underwater vehicles have been developed in order to explore underwater regions, specially those of difficult access for humans. Autonomous Underwater Vehicles (AUVs) are equipped with on-board sensors, which provide valuable information about the vehicle state and the environment. This information is used to build an approximate map of the area and estimate the position of the vehicle within...
We present in this paper a supervised approach for automatic detection of micro-calcifications. The system is based on learning the different morphology of the micro-calcifications using local features, which are extracted using a bank of filters. Afterwards, this set of features is used to train a pixel-based boosting classifier which at each round automatically selects the most salient one. Therefore,...
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