Helping Visually Impaired People Take Better Quality Pictures
Author:
Affiliation:
1. Laboratory for Image and Video Engineering, The University of Texas at Austin, Austin, TX, USA
2. Meta AI Research, Menlo Park, CA, USA
3. Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
Funder
Meta AI Research
National Science Foundation AI Institute for Foundations of Machine Learning
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Graphics and Computer-Aided Design,Software
Link
https://ieeexplore.ieee.org/ielam/83/9991910/10179270-aam.pdf
Reference59 articles.
1. An Assisted Photography Framework to Help Visually Impaired Users Properly Aim a Camera
2. DeepLab: Semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs;chen;IEEE Trans Pattern Anal Mach Intell,2018
3. Convolutional Neural Networks for No-Reference Image Quality Assessment
4. Very deep convolutional networks for large-scale image recognition;simonyan;arXiv 1409 1556,2015
5. Patch-VQ: ‘Patching Up’ the Video Quality Problem
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