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Magic Camera

Magic Camera. Master’s Project Defense By Adam Meadows. Project Committee: Dr. Eamonn Keogh Dr. Doug Tolbert. Roadmap. Problem Motivation Background Stepping Through Magic Camera Results Conclusion Future Work. Problem.

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Magic Camera

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  1. Magic Camera Master’s Project Defense By Adam Meadows Project Committee: Dr. Eamonn Keogh Dr. Doug Tolbert

  2. Roadmap • Problem • Motivation • Background • Stepping Through Magic Camera • Results • Conclusion • Future Work

  3. Problem • To organize an image containing a collection of objects in front of a solid background

  4. Motivation • Incorporation into Digital Cameras • Sorting Tables • Insect Boards

  5. Background • Multidimensional Scaling (MDS) • Transforms a dissimilarity matrix into a collection of points in 2d (or 3d) space • Euclidean distances between the points reflect the given dissimilarity matrix • Similar objects are spaced close together, dissimilar objects are spaced farther apart

  6. Stepping Through Magic Camera • Identifying Objects • Calculating Similarities • Creating Resulting Image

  7. Identifying Objects • Convert to black and white image • Threshold: calculated automatically or specified • Each connected comp treated as an object • Each obj. cropped by B-box + 5 pixel border • Edges of adjacent objects filtered out • Objects rotated to “face” same direction

  8. Filtering Adjacent Objects

  9. Object Rotation • Find major axis • Align with image’s major axis • Find centroid • Rotate so centroid is at bottom/left of obj http://www.mathworks.com/access/helpdesk/help/toolbox/images/regionprops.html

  10. Calculating Similarities • Numerical representation of objects • Shape, color, texture • Create dissimilarity matrix • Euclidean dist between each pair of objs

  11. Shape • Each object translated into a time series • Dist from the center of obj to perimeter • Code provided by Dr. Keogh

  12. Shape II

  13. Color • RGB values independently averaged • 1000 random pixels chosen • Pixels not unique (if obj < 1000 pixels)

  14. Texture • Std deviation of 9 pixel neighborhood • averaged over 1,000 random pixels • Pixels not unique (if obj < 1,000 pixels)

  15. Creating New Image • Extracting Background • Finding New Positions • Fixing Overlaps

  16. Extracting Background • Use B&W image to id background • Independently avg RGB values • Create a new solid background image • same dimensions as original image

  17. Finding New Positions • Use MDS to get coordinates for objs • Using dissimilarity matrix • Reverse Y values • Images are indexed top-down

  18. Fixing Overlaps • Start placing objects in given order • Randomly chosen if not specified • If overlap detected • Move object min dist to rectify • In one direction (up, down, left, right)

  19. Fixing Overlaps II Not

  20. Results

  21. Explanation

  22. Explanation II

  23. Conclusion • Input image • Collection of objects on solid background • Output image • Similar objects grouped close to each other • All objects “face” same direction

  24. Future Work • Develop color method • Try it with some real data (butterflies, etc.) • Add combination of similarity measures • Shape & color, color & texture, etc. • Add optional How-To • Display original image • User clicks an object • Line drawn to new location

  25. Questions ?

  26. Resources • Slides available • http://www.cs.ucr.edu/~ameadows/msproject/slides.ppt • http://www.cs.ucr.edu/~ameadows/msproject/slides-handout.pdf • Report available • http://www.cs.ucr.edu/~ameadows/msproject/report.pdf • Code available • http://www.cs.ucr.edu/~ameadows/msproject/magic_camera.zip

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