Reputation: 39
Can anybody help me to explain what is consistency map (confidence map) in computer vision? and is there any formula or built-in function to calculate it?
Thanks in advance.
Upvotes: 1
Views: 9636
Reputation: 2588
Confidence map is your resultant knowledge, using a priori information & and an iteration of an algorithm. Prediction & measurement update style visual tracking algorithms are great examples. From wiki (mean-shift):
The mean shift algorithm can be used for visual tracking. The simplest such algorithm would create a confidence map in the new image based on the color histogram of the object in the previous image, and use mean shift to find the peak of a confidence map near the object's old position. The confidence map is a probability density function on the new image, assigning each pixel of the new image a probability, which is the probability of the pixel color occurring in the object in the previous image. A few algorithms, such as ensemble tracking, CAMshift, expand on this idea.
As far as I know, the term consistency map, on the other hand, is used in image registration problems, in general. A voxel needs to be "photo-consistent" to be rendered(modeled) in 3d. From wiki again (photo-consistency):
In computer vision photo-consistency determines whether a given voxel is occupied. A voxel is considered to be photo consistent when its color appears to be similar to all the cameras that can see it. Most voxel coloring or space carving techniques require using photo consistency as a check condition in Image-based modeling and rendering applications.
EDIT: OpenCV has built-in kalman filter, mean-shift and camshift algorithms. It also has a 3D rendering API
Upvotes: 2