The classification and grasping of randomly placed objects where only a limited number of training images are available, remains a challenging problem. Approaches such as data synthesis have been used to synthetically create larger training data sets from a small set of training data and can be used to improve performance. This paper examines how limited product images for ‘off the shelf’ items can be used to generate a synthetic data set that is used to train a that allows classification of the item, segmentation and grasping. Experiments investigating the effects of data synthesis are presented and the subsequent trained network implemented in a robotic system to perform grasping of objects.
CITATION STYLE
Cheah, M., Hughes, J., & Iida, F. (2018). Data synthesization for classification in autonomous robotic grasping system using ‘Catalogue’-style images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10965 LNAI, pp. 40–51). Springer Verlag. https://doi.org/10.1007/978-3-319-96728-8_4
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