517 research outputs found
Visual Estimation of Fingertip Pressure on Diverse Surfaces using Easily Captured Data
People often use their hands to make contact with the world and apply
pressure. Machine perception of this important human activity could be widely
applied. Prior research has shown that deep models can estimate hand pressure
based on a single RGB image. Yet, evaluations have been limited to controlled
settings, since performance relies on training data with high-resolution
pressure measurements that are difficult to obtain. We present a novel approach
that enables diverse data to be captured with only an RGB camera and a
cooperative participant. Our key insight is that people can be prompted to
perform actions that correspond with categorical labels describing contact
pressure (contact labels), and that the resulting weakly labeled data can be
used to train models that perform well under varied conditions. We demonstrate
the effectiveness of our approach by training on a novel dataset with 51
participants making fingertip contact with instrumented and uninstrumented
objects. Our network, ContactLabelNet, dramatically outperforms prior work,
performs well under diverse conditions, and matched or exceeded the performance
of human annotators
Microbial Fouling of a Reverse Osmosis Municipal Water Treatment System
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146838/1/wer0703.pd
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