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REU Week III. Malcolm Collins-Sibley Mentor: Shervin Ardeshir. Project. Cross-View Image Registration and Semantic Segmentation

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reu week iii

REU Week III

Malcolm Collins-Sibley

Mentor: ShervinArdeshir

project
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.
project1
project
  • Output Mockup
completed work
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
completed work1
Completed work
  • Geometric Image Parsing Code
completed work2
Completed work
  • Geometric Image Parsing Code
completed work3
Completed work
  • Super-pixel Segmentation

With 8 super-pixels

completed work4
Completed work
  • Super-pixel Segmentation

With 20 super-pixels

completed work5
Completed work
  • Building Projection
completed work6
Completed work
  • Building Projection
current work
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

current work1
Current work
  • Occlusion Handling

Before

After

current work2
Current work
  • Occlusion Handling

Before

After

current work3
Current work
  • Occlusion Handling

Before

After

current work4
Current work
  • Occlusion Handling

Before

After

the next step
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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