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Deep Learning-Based Semantic Segmentation of Microscale Objects

2019-07-03Code Available0· sign in to hype

Ekta U. Samani, Wei Guo, Ashis G. Banerjee

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Abstract

Accurate estimation of the positions and shapes of microscale objects is crucial for automated imaging-guided manipulation using a non-contact technique such as optical tweezers. Perception methods that use traditional computer vision algorithms tend to fail when the manipulation environments are crowded. In this paper, we present a deep learning model for semantic segmentation of the images representing such environments. Our model successfully performs segmentation with a high mean Intersection Over Union score of 0.91.

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