1. REU WEEK IV Malcolm Collins-Sibley Mentor: Shervin Ardeshir

2. 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

3. COMPLETED WORK Error and inaccuracy fixing with the building projection code

4. COMPLETED WORK

6. Small changes to the top view map

7. COMPLETED WORK Probability Mapping Each image has one map with each building section covered by a Gaussian filter

8. COMPLETED WORK Binary map of where there is a high probability of the building being there

9. COMPLETED WORK Multiple building binary maps

10. COMPLETED WORK

11. Data Storage Number four is empty because no buildings were detected

12. 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

13. COMPLETED WORK

15. 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