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Automated 3D Image Conversion and Photo Reconstruction on the Web

This research explores the automated conversion of 2D images into 3D representations using cutting-edge techniques like Stencil Filtering and Recursive Von Neumann Stencil. The study focuses on the application of Depth Maps and innovative algorithms for reconstructing images in a realistic 3D format. By leveraging existing information within images, the process aims to generate detailed 3D renderings with high accuracy and realism. The study also includes the creation of a 3D website featuring the Phaistos Disk, an ancient artifact, and emphasizes international collaboration for advancements in 3D technology.

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Automated 3D Image Conversion and Photo Reconstruction on the Web

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  1. Automated 3D Image Conversion and Photo Reconstruction on the Web Judit Tövissy Judit Tövissy 2015. 04. 21.

  2. Goals • Analysis of Depth Map Generation for 2D Images • Identification of Steps for Reconstruction • Innovation: Stencil Filtering • Create 3D Website on Phaistos Disk • Main Bibliographical References

  3. Attention This research deals with the new exploitation of already existing information inside an image. No new information is added.

  4. Distance Representation with Depth Maps

  5. Depth Map Generation for 2D Images

  6. Resulting 3D Conversion

  7. Depth Map Generation for 2D Images

  8. Resulting 3D Conversion

  9. Depth Map Generation for 2D Images • Issue: • Reverse-engineering of an entire dimension is non-trivial. • Reconstruction needs to be based on agreed upon guidelines • Assumption #1: • Objects in focus are likely to be closer to the camera than others. • Assumption #2: • Objects that are brighter are likely to be in the foreground of an image.

  10. StepsofReconstruction

  11. Image Segmentation Clustering by Mean Shift Algorithm

  12. Qualitative Depth Map (QDM)

  13. Focus Detection Previously Developed by MTA SZTAKI

  14. Depth-Focus Map Combination of a QDM and a Focus Map

  15. Stereoscopic Generation Based on the previous Depth-Focus Map

  16. Innovation: Stencil Filtering 3D Rendering Raytracing Pixel-based 2D Graphics Von Neumann Neighbours • Instead of Von Neumann neighbouring pixels • Initiate a Recursive Ray in each direction • Find the first non-blank pixel

  17. Innovation: Stencil Filtering Recursive Von Neumann Stencil Never documented before Objective: Find relevant pixels Will result in most relevant data

  18. Stencil Filtering Recursive Von Neumann NeighbouringPixels Resulting Pixel Filtering Kernel Original Pixel

  19. Stencil Filtering – Basic Filtering Kernel Result of an averaging process Clearly visible edges

  20. Stencil Filtering – Cross Filtering Kernel Representation of relevance Pixels weighted according to distance Bilinear Interpolation Cross Filtering

  21. Stencil Filtering – Cross Filtering Kernel Conclusion: Distance is less relevant than was believed

  22. Stencil Filtering – Median Filtering Kernel Advantage in Realism: Each new pixel is an instance of already existing values in the image

  23. Stereoscopic 3D Conversion

  24. Phaistos Disk Ancient Greek artifact near Herakleion The writing remained a mystery for nearly a century TEI of Crete has a solution 3D Website International Collaboration with Dennis Gabor College

  25. Phaistos Disk

  26. Main Bibliographical References • S. Battiato, S. Curti, M. La Cascia, M. Tortora and E. Scordato, "Depth map generation by image classification," Three-Dimensional Image Capture and Applications VI, pp. 95-104, April 16, 2004. • L. Kovács and T. Szirányi, "Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest," Pattern Analysis and Machine Intelligence, IEEE Transactions on, pp. 1080-1085, June 2007. • G. Neumann and S. Kopácsi, "Development of 3D Webpages," in CSIT’2013. Proceedings of the 15th international workshop on computer science and information technologies, Vienna-Budapest-Bratislava, 2013. • S. Battiato, A. Capra, S. Curti and M. La Cascia, Writers, 3d Stereoscopic Image Pairs by Depth-Map Generation. [Performance]. 2004.

  27. Automated 3D Image Conversion and Photo Reconstruction on the Web Judit Tövissy Judit Tövissy 2015. 04. 21.

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