Abstract
The objective of this proposal is to select and analyze, functionally and computationally, a set of algorithms used for the detection of defects by automatic visual inspection, which make use of multiple instance learning and have the potential to be improved. From the analyses, modifications or updates, it is proposed to speed-up the response of the automatic visual inspection systems, allowing thereby, a decrease of the amount of undetected defective products in the production lines.
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Acknowledgments
The authors acknowledge the support to attend DCAI’18 Doctoral Consortium provided by “Convocatoria para la Movilidad Internacional de la Universidad Nacional de Colombia (UNAL) 2016 - 2018”.
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Villegas-Jaramillo, EJ., Orozco-Alzate, M. (2019). Computational Analysis of Multiple Instance Learning-Based Systems for Automatic Visual Inspection: A Doctoral Research Proposal. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_49
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