Internet Vision - Lecture 3

1. Internet Vision - Lecture 3 Tamara Berg Sept 10

2. New Lecture Time Mondays 10:00am-12:30pm in 2311 Monday (9/15) we will have a general Computer Vision & Machine Learning review Please look at papers and decide which one you want to present by Monday • read topic/titles/abstracts to get an idea of which you are interested in

3. Thanks to Lalonde et al for providing slides!

5. Algorithm Outline

6. Inserting objects into images Have an image and want to add realistic looking objects to that image

7. Inserting objects into images User picks a location where they want to insert an object

8. Inserting objects into images Based some properties calculated about the image, possible objects are presented.

9. Inserting objects into images User selects which object to insert and the object is placed in the scene at the correct scale for the location

10. Inserting objects into images – Possible approaches Insert a clip art object with some idea of the environment Insert a clip art object Insert a rendered object with full model of the environment

15. Some objects will be easy to insert because they already “fit” into the scene

16. Collect a large database of objects. Let the computer decide which examples are easy to insert. Allow the user to select only among those.

17. When will an object “fit”? 1.) When the lighting conditions of the scene and object are similar 2.) When the camera pose of the scene & object match

18. 2D vs 3D Use 3d information for: 1.) Annotating objects in the clip-art library with camera pose 2.) Estimating the camera pose in the query image 3.) Computing illumination context in both library & query images

21. Phase 1 - Database Annotation For each object we want: • Estimate of its true size and the camera pose it was captured under • Estimate of the lighting conditions it was captured under

22. Phase 1 - Database AnnotationEstimate object size smaller larger Objects closer to the camera appear larger than objects further from the camera

23. Phase 1 - Database AnnotationEstimate object size *If* you know the camera pose then you can estimate the real height of an object from: location in the image, pixel height

24. Phase 1 - Database AnnotationEstimate object size Annotate objects with their true heights and resize examples to a common reference size

25. Phase 1 - Database AnnotationEstimate object size & camera pose Don’t know camera pose or object heights! Trick - Infer camera pose & object heights across all object classes in the database given only the height distribution for one class

26. Phase 1 - Database AnnotationEstimate object size & camera pose Start with known heights for people

27. Phase 1 - Database AnnotationEstimate object size & camera pose Estimate camera pose for images with multiple people

28. Phase 1 - Database AnnotationEstimate object size & camera pose Use these images to estimate a prior over the distribution of poses How do people usually take pictures? Standing on the ground at eye level.

29. Phase 1 - Database AnnotationEstimate object size & camera pose Use the learned pose distribution to estimate heights of other object categories that appear with people. Iteratively use these categories to learn more categories. Annotate all objects in the database with their true size and originating camera pose.

30. Phase 1 - Database AnnotationEstimate object size & camera pose

31. Phase 1 - Database Annotation For each object we want: • Estimate of its true size and the camera pose it was captured under • Estimate of the lighting conditions it was captured under

32. Phase 1 - Database AnnotationEstimate lighting conditions Ground Vertical Sky Estimate which pixels are ground, sky, vertical Black box for now (we’ll cover this paper later in the course)

33. Phase 1 - Database AnnotationEstimate lighting conditions Distribution of pixel colors

35. Phase 2 – Object Insertion Query Image

36. Phase 2 – Object Insertion User specifies horizon line – use to calculate camera pose with respect to ground plane (lower -> tilted down, higher -> tilted up). Illumination context is calculated in the same way as for the database images.

37. Phase 2 – Object Insertion Insert an object into the scene that has matching lighting, and camera pose to the query image

38. Phase 2 – Object Insertion But wait it still looks funny!

39. Phase 2 – Object Insertion Shadows are important!

40. Phase 2 – Object Insertion

41. Phase 2 – Object Insertion

42. Phase 2 – Object Insertion

43. Phase 2 – Object Insertion Shadow Transfer

44. Categorize images for easy selection in user interface