과제를 준비하면서 사용하였던 colorization, google deepdream, style transfer, matting 알고리즘에 대해 간단히 정리해보았습니다. (논문 + 코드 링크 정리입니다.) opencv grabcut도 있음
Deep Learning/resources 2017. 12. 9. 09:33https://m.facebook.com/groups/255834461424286?view=permalink&id=570463146628081
다들 아시는 정보이시겠지만, 과제를 준비하면서 사용하였던 colorization, google deepdream, style transfer, matting 알고리즘에 대해 간단히 정리해보았습니다. (논문 + 코드 링크 정리입니다.)
올라오는 글들을 보면서 공부에 대해 자극도 많이 받고, 많은 정보를 얻어갑니다. 다들 감사합니다. 부족하지만 조금이나마 도움이 되었으면 합니다. (오류가 있다면 말씀부탁드리겠습니다.)
1. Colorization
(1) Colourful Image Colorization (Zhang et al. 2016)
Link: http://richzhang.github.io/colorization/
(2) Automatic Colorization of Grayscale Images from Stanford cs229 class
Description: http://cs229.stanford.edu/proj2013/KabirzadehSousaBlaes-AutomaticColorizationOfGrayscaleImages.pdf
Code: https://github.com/prblaes/ImageColorization
2. Google deepdream
(1) (For Docker user) https://github.com/VISIONAI/clouddream
(2) (For Python user) https://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/
(3) (For IPython Notebook user) https://github.com/google/deepdream
3. Style Transfer
(1) Deep Photo style transfer (2017)
paper: https://arxiv.org/abs/1703.07511
code: https://github.com/luanfujun/deep-photo-styletransfer
4. Matting (Background removal and replacement)
(1) Deep Image Matting (Xu et al. 2017)
paper: https://arxiv.org/abs/1703.03872
code: https://github.com/Joker316701882/Deep-Image-Matting
(2) Scribble method
1) paper: (Wang and Cohen 2005) An Iterative Optimization Approach for Unified Image Segmentation and Matting
2) paper: (Levin et al. 2008) A Closed-Form Solution to Natural
Image Matting
code: http://www.alphamatting.com/code.php
(3) Grabcut Method (Rother et al. 2004)
paper: https://dl.acm.org/citation.cfm?id=1015720
code: (based on opencv) https://docs.opencv.org/3.1.0/d8/d83/tutorial_py_grabcut.html
다들 아시는 정보이시겠지만, 과제를 준비하면서 사용하였던 colorization, google deepdream, style transfer, matting 알고리즘에 대해 간단히 정리해보았습니다. (논문 + 코드 링크 정리입니다.)
올라오는 글들을 보면서 공부에 대해 자극도 많이 받고, 많은 정보를 얻어갑니다. 다들 감사합니다. 부족하지만 조금이나마 도움이 되었으면 합니다. (오류가 있다면 말씀부탁드리겠습니다.)
1. Colorization
(1) Colourful Image Colorization (Zhang et al. 2016)
Link: http://richzhang.github.io/colorization/
(2) Automatic Colorization of Grayscale Images from Stanford cs229 class
Description: http://cs229.stanford.edu/proj2013/KabirzadehSousaBlaes-AutomaticColorizationOfGrayscaleImages.pdf
Code: https://github.com/prblaes/ImageColorization
2. Google deepdream
(1) (For Docker user) https://github.com/VISIONAI/clouddream
(2) (For Python user) https://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/
(3) (For IPython Notebook user) https://github.com/google/deepdream
3. Style Transfer
(1) Deep Photo style transfer (2017)
paper: https://arxiv.org/abs/1703.07511
code: https://github.com/luanfujun/deep-photo-styletransfer
4. Matting (Background removal and replacement)
(1) Deep Image Matting (Xu et al. 2017)
paper: https://arxiv.org/abs/1703.03872
code: https://github.com/Joker316701882/Deep-Image-Matting
(2) Scribble method
1) paper: (Wang and Cohen 2005) An Iterative Optimization Approach for Unified Image Segmentation and Matting
2) paper: (Levin et al. 2008) A Closed-Form Solution to Natural
Image Matting
code: http://www.alphamatting.com/code.php
(3) Grabcut Method (Rother et al. 2004)
paper: https://dl.acm.org/citation.cfm?id=1015720
code: (based on opencv) https://docs.opencv.org/3.1.0/d8/d83/tutorial_py_grabcut.html
