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Published byDeirdre Mitchell Modified over 8 years ago
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WEEK IX Malcolm Collins-Sibley Mentor: Shervin Ardeshir
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PROJECT Use information from map and satellite images to project data such as addresses and streets on to the screen that the user is observing Use semantic segmentation to over lay this information in a meaningful way
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FRAMEWORK SO FAR
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STEPS SO FAR Starting Image
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STEPS SO FAR Initial Projections from GIS Data
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STEPS SO FAR GIS Projections with the addition of horizon calculations
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STEPS SO FAR Full visualization of projections, also labeling the center of each building
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STEPS SO FAR Initial Segmentation results
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STEPS SO FAR After 7 iterations of the Projection Segmentation fusion code
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STEPS SO FAR After thresholding the fusion results
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STEPS SO FAR Address for each building in the frame
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STEPS SO FAR Projecting the names of near by streets
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THIS WEEK
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GOALS FROM LAST WEEK Generate new dense grid for the whole data set Generate binary maps for streets in the whole data set Fusion between segmentation, and street projection only Solving fusion optimization between segmentation, building projection, and street projection jointly
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DENSE GRID To save computing time, we only looked at grid points within a radius around each test image
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BINARY MAPS
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STREET FUSION (Initial results)
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STREET FUSION (Initial results)
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BINARY MAPS
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STREET FUSION (Initial results)
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STREET FUSION (Initial results)
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STREET FUSION (Initial results) Shadow breaks up the street into extra pixels Cracks in the street create extra pixels
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STREET FUSION (Initial results)
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STREET FUSION (Initial results)
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STREET FUSION (Initial results) Cross walk interrupts street Person and cars interrupts street
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JOINT OPTIMIZATION
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WAYS TO IMPROVE Two main thins need to be updated: Create more precise binary maps Currently each map is an out line of the outermost points of both streets causing it to include more points than wanted New system of fusion between binary maps and segmentation Handling outlier segments in the optimization
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