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Enhancing Image Registration and Segmentation through Geometric Parsing Techniques

This project focuses on cross-view image registration and semantic segmentation by utilizing map and satellite imagery to create overlays that enhance user observation. Key components include geometric image parsing, superpixel segmentation, and occlusion handling to accurately depict environments. We successfully developed an output mockup, completed various readings on image parsing methodologies, and implemented code for geometric image parsing and superpixel segmentation. Current work involves refining occlusion handling and analyzing superpixel similarities in a structured dataset.

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Enhancing Image Registration and Segmentation through Geometric Parsing Techniques

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  1. REU Week III Malcolm Collins-Sibley Mentor: ShervinArdeshir

  2. Project • Cross-View Image Registration and Semantic Segmentation • The goal is to use information from map and satellite images, and project them on the screen which the user is observing, in a way that the user can see semantic segments overlaid on the scene.

  3. project • Output Mockup

  4. Completed work • Readings: • “Geometric Image Parsing in Man-Made Environments” • Olga Barinova et al • “Recovering Surface Layout from an Image” • Derek Hoiem et al • “Recovering Occlusion Boundaries from a Single Image” • Derek Hoeim et al • “Entropy Rate Superpixel Segmentation” • MY Liu et al

  5. Completed work • Geometric Image Parsing Code

  6. Completed work • Geometric Image Parsing Code

  7. Completed work • Super-pixel Segmentation With 8 super-pixels

  8. Completed work • Super-pixel Segmentation With 20 super-pixels

  9. Completed work • Building Projection

  10. Completed work • Building Projection

  11. Current work • Within the Building Projection code: • Building occlusion and self-occlusion • Works whena building occludes another, but not when a building is occluding itself

  12. Current work • Occlusion Handling Before After

  13. Current work • Occlusion Handling Before After

  14. Current work • Occlusion Handling Before After

  15. Current work • Occlusion Handling Before After

  16. The next step • Understanding the occlusion handling code • Making sure it is handling self-occlusions accurately • Understanding the format of the output data in the line segments/horizon code • Running the line segmentation code for all of the images in our dataset and saving all of the output variables in a structure • Extracting the super pixels from images in the dataset and saving it in a structure • Computing their pairwise similarities of the super pixels in terms of color and texture

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