Added superpixels averaging lightfields depth demo

README.md

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+#### Depth based on SLIC Superpixels averaging
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+Superpixel algorithms derive meaningful atomic primitives from dense grid images, which can them be used in a variety of computer vision algorithms.
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+By averaging the depth values derived from the correspondence metric for each superpixel, we can create a piecewise-constant set of depth values that respect the continuous regions in the colours of the lightfield image.
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+<sub>Achanta, Radhakrishna, et al. ["SLIC superpixels compared to state-of-the-art superpixel methods."](https://infoscience.epfl.ch/record/149300/files/SLIC_Superpixels_TR_2.pdf) IEEE transactions on pattern analysis and machine intelligence 34.11 (2012): 2274-2282.</sub>
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+<sub>Tao, Michael W., et al. ["Depth from combining defocus and correspondence using light-field cameras."](https://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Tao_Depth_from_Combining_2013_ICCV_paper.pdf) Proceedings of the IEEE International Conference on Computer Vision. 2013.</sub>
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+<sub>Chuchvara, Aleksandra, Attila Barsi, and Atanas Gotchev. ["Fast and Accurate Depth Estimation from Sparse Light Fields."](https://arxiv.org/pdf/1812.06856.pdf) IEEE Transactions on Image Processing (2019).</sub>
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 ## Tutorials
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