Abstract
In this paper, we describe the components of a novel algorithm for the detection of grasping points from monocular images of previously unseen objects. A basic building block of our approach is the use of a newly devised descriptor, capable of representing grasping point shape and appearance by the use of histograms of oriented gradients in a semi-local manner. Combined with boosting our method learns discriminative grasp point models for new objects from a set of annotated real-world images. The method has been extensively evaluated on challenging images of real scenes, exhibiting largely varying characteristics concerning illumination conditions, scene complexity, and viewpoint. Our experiments show that the method, despite these variations, works in a stable manner and that its performance compares favorably to the state-of-the-art.
This work was partly supported by the European Union project GRASP (FP7-215821).
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Lefakis, L., Wildenauer, H., Garcia-Tubio, M.P., Szumilas, L. (2010). Image-Based Grasping Point Detection Using Boosted Histograms of Oriented Gradients . In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13772-3_21
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DOI: https://doi.org/10.1007/978-3-642-13772-3_21
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