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Abstract:

Computer vision today still relies on conventional camera capture that was invented decades ago to accommodate the human visual system.  Yet computers and humans see the world very differently.  What works for human vision does not correlate for computer vision, and vice versa.  This is especially true for computer vision based on deep convolutional networks. We need better vision for computer vision.  Using GPUs and deep learning, we're able to reverse the resolution degrading effects of conventional visual capture, then reconstruct on demand to radically improve the accuracy and processing efficiency of computer vision applications.

Computer vision today still relies on conventional camera capture that was invented decades ago to accommodate the human visual system.  Yet computers and humans see the world very differently.  What works for human vision does not correlate for computer vision, and vice versa.  This is especially true for computer vision based on deep convolutional networks. We need better vision for computer vision.  Using GPUs and deep learning, we're able to reverse the resolution degrading effects of conventional visual capture, then reconstruct on demand to radically improve the accuracy and processing efficiency of computer vision applications.

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Topics:
Intelligent Video Analytics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91054
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Abstract:

We'll showcase both the technology and use-cases for applying convolutional neural networks and GPUs to reverse the resolution-degrading effects of optical blur and sensor sampling, in order to reconstruct color video to nine times its captured pixel density.

We'll showcase both the technology and use-cases for applying convolutional neural networks and GPUs to reverse the resolution-degrading effects of optical blur and sensor sampling, in order to reconstruct color video to nine times its captured pixel density.

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Topics:
Intelligent Video Analytics
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7250
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