Awesome

This API will convert your image into a super resolution image.

Providing the low resolution image url to the API will returns the conversion of your image into a super resolution image.

Read these papers to learn more about segmentation technics:

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

@article{DBLP:journals/corr/BadrinarayananK15,
  author    = {Vijay Badrinarayanan and
               Alex Kendall and
               Roberto Cipolla},
  title     = {SegNet: {A} Deep Convolutional Encoder-Decoder Architecture for Image
               Segmentation},
  journal   = {CoRR},
  volume    = {abs/1511.00561},
  year      = {2015},
  url       = {http://arxiv.org/abs/1511.00561},
  archivePrefix = {arXiv},
  eprint    = {1511.00561},
  timestamp = {Mon, 13 Aug 2018 16:46:06 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/BadrinarayananK15},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Fully Convolutional Networks for Semantic Segmentation

@article{DBLP:journals/corr/LongSD14,
  author    = {Jonathan Long and
               Evan Shelhamer and
               Trevor Darrell},
  title     = {Fully Convolutional Networks for Semantic Segmentation},
  journal   = {CoRR},
  volume    = {abs/1411.4038},
  year      = {2014},
  url       = {http://arxiv.org/abs/1411.4038},
  archivePrefix = {arXiv},
  eprint    = {1411.4038},
  timestamp = {Mon, 13 Aug 2018 16:48:17 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/LongSD14},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

U-Net: Convolutional Networks for Biomedical Image Segmentation

@article{DBLP:journals/corr/RonnebergerFB15,
  author    = {Olaf Ronneberger and
               Philipp Fischer and
               Thomas Brox},
  title     = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
  journal   = {CoRR},
  volume    = {abs/1505.04597},
  year      = {2015},
  url       = {http://arxiv.org/abs/1505.04597},
  archivePrefix = {arXiv},
  eprint    = {1505.04597},
  timestamp = {Mon, 13 Aug 2018 16:46:52 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/RonnebergerFB15},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Pyramid Scene Parsing Network

@article{DBLP:journals/corr/ZhaoSQWJ16,
  author    = {Hengshuang Zhao and
               Jianping Shi and
               Xiaojuan Qi and
               Xiaogang Wang and
               Jiaya Jia},
  title     = {Pyramid Scene Parsing Network},
  journal   = {CoRR},
  volume    = {abs/1612.01105},
  year      = {2016},
  url       = {http://arxiv.org/abs/1612.01105},
  archivePrefix = {arXiv},
  eprint    = {1612.01105},
  timestamp = {Mon, 13 Aug 2018 16:47:16 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/ZhaoSQWJ16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

EXAMPLE
example

INPUT

{
  "url": "https://i.ibb.co/6X88r2n/input.png",
  "model": "scene_parsing"
}

EXECUTION

curl -X POST "https://api-market-place.ai.ovh.net/image-segmentation/process" -H "accept: application/json" -H "X-OVH-Api-Key: XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX" -H "Content-Type: application/json" -d '{"url":"https://i.ibb.co/6X88r2n/input.png", "model": "scene_parsing"}'

OUTPUT

output

please refer to swagger documentation for further technical details: swagger documentation