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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