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We present a new method to calculate valid end-effector orientations for grasping tasks. A fast and accurate three-layered hierarchical supervised machine learning framework is developed. The algorithm is trained with a human-in-the-loop in a learn-by-demonstration procedure where the robot is shown a set of valid end-effector rotations. Learning is then achieved through a multi-class support vector...
In this paper we propose a method for classifying the vegetation types in an aerial color infra-red (CIR) image. Different vegetation types do not only differ in color, but also in texture. We study the use of four Haralick features (energy, contrast, entropy, homogeneity) for texture analysis, and then perform the classification using the one-against-all (OAA) multi-class support vector machine (SVM),...
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