internet vision - lecture 3
DESCRIPTION
Internet Vision - Lecture 3. Tamara Berg Sept 10. 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 - PowerPoint PPT PresentationTRANSCRIPT
Internet Vision - Lecture 3
Tamara BergSept 10
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
Thanks to Lalonde et al for providing slides!
Algorithm Outline
Inserting objects into images
Have an image and want to add realistic looking objects to that image
Inserting objects into images
User picks a location where they want to insert an object
Inserting objects into images
Based some properties calculated about the image, possible objects are presented.
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
Inserting objects into images – Possible approaches
Insert a clip art object Insert a clip art object with some idea of the environment
Insert a rendered object with full model of the environment
Some objects will be easy to insert because they already “fit” into the scene
Collect a large database of objects.Let the computer decide which examples are easy to insert.Allow the user to select only among those.
When will an object “fit”?
1.) When the lighting conditions of the scene and object are similar2.) When the camera pose of the scene & object match
2D vs 3D
Use 3d information for:
1.) Annotating objects in the clip-art library with camera pose2.) Estimating the camera pose in the query image3.) Computing illumination context in both library & query images
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
Phase 1 - Database AnnotationEstimate object size
Objects closer to the camera appear larger than objects further from the camera
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
Phase 1 - Database AnnotationEstimate object size
Annotate objects with their true heights and resize examples to a common reference size
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
Phase 1 - Database AnnotationEstimate object size & camera pose
Start with known heights for people
Phase 1 - Database AnnotationEstimate object size & camera pose
Estimate camera pose for images with multiple people
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.
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.
Phase 1 - Database AnnotationEstimate object size & camera pose
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
Phase 1 - Database AnnotationEstimate lighting conditions
Estimate which pixels are ground, sky, vertical
Black box for now (we’ll cover this paper later in the course)
Ground
Vertical
Sky
Phase 1 - Database AnnotationEstimate lighting conditions
Distribution of pixel colors
Phase 2 – Object Insertion
Query Image
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.
Phase 2 – Object Insertion
Insert an object into the scene that has matching lighting, and camera pose to the query image
Phase 2 – Object Insertion
But wait it still looks funny!
Phase 2 – Object Insertion
Shadows are important!
Phase 2 – Object Insertion
Phase 2 – Object Insertion
Phase 2 – Object Insertion
Phase 2 – Object Insertion
Shadow Transfer
Categorize images for easy selection in user interface
Big Picture
• It’s all about the data!
• Use lots of data to turn a hard problem into an easier one!– Place “my car” in a scene is much harder than
place “some car” in a scene. Allow the computer to choose from among many examples of a class to find the easy ones.