REU WEEK IVMalcolm Collins-Sibley

Mentor: Shervin Ardeshir

GOALS FROM LAST WEEK

• 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

COMPLETED WORK• Error and inaccuracy fixing with the building projection code

COMPLETED WORK

COMPLETED WORK

COMPLETED WORKSmall changes to the top view map

COMPLETED WORK

• Probability Mapping• Each image has

one map with each building section covered by a Gaussian filter

COMPLETED WORK

Binary map of where there is a high probability of the building being there

COMPLETED WORK

Multiple building binary maps

COMPLETED WORK

COMPLETED WORK

• Data Storage

Number four is empty because no buildings were detected

FUSION – PROPAGATION

• We will build a graph on the super-pixels

• Nodes = Super-pixels (Probability of segment I belonging to a building-f(intersection) )

• Edges = Similarity of the super-pixels in terms of color, texture, location, etc

COMPLETED WORK

COMPLETED WORK

THE NEXT STEP

• Tuning building projections in terms of height.

• Generating KML/KMZ files from google earth containing GPS locations of different buildings/roads

• Fusion between building projection and super-pixilation • First with binary mapping• Next with probability mapping

• Initial fusion results (Belief Propagation)

• Run that fusion on the data set