Now an orientation is assigned to each keypoint to achieve invariance to image rotation. A
neighbourhood is taken around the keypoint location depending on the scale, and the gradient
magnitude and direction is calculated in that region. An orientation histogram with 36 bins covering
-360 degrees is created. (It is weighted by gradient magnitude and gaussian-weighted circular window
-with \f$\sigma\f$ equal to 1.5 times the scale of keypoint. The highest peak in the histogram is taken
+360 degrees is created (It is weighted by gradient magnitude and gaussian-weighted circular window
+with \f$\sigma\f$ equal to 1.5 times the scale of keypoint). The highest peak in the histogram is taken
and any peak above 80% of it is also considered to calculate the orientation. It creates keypoints
with same location and scale, but different directions. It contribute to stability of matching.
Keypoints between two images are matched by identifying their nearest neighbours. But in some cases,
the second closest-match may be very near to the first. It may happen due to noise or some other
reasons. In that case, ratio of closest-distance to second-closest distance is taken. If it is
-greater than 0.8, they are rejected. It eliminaters around 90% of false matches while discards only
+greater than 0.8, they are rejected. It eliminates around 90% of false matches while discards only
5% correct matches, as per the paper.
So this is a summary of SIFT algorithm. For more details and understanding, reading the original
If you have an image of background alone, like an image of the room without visitors, image of the road
without vehicles etc, it is an easy job. Just subtract the new image from the background. You get
the foreground objects alone. But in most of the cases, you may not have such an image, so we need
-to extract the background from whatever images we have. It become more complicated when there are
+to extract the background from whatever images we have. It becomes more complicated when there are
shadows of the vehicles. Since shadows also move, simple subtraction will mark that also as
foreground. It complicates things.
and "Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction"
in 2006. One important feature of this algorithm is that it selects the appropriate number of
gaussian distribution for each pixel. (Remember, in last case, we took a K gaussian distributions
-throughout the algorithm). It provides better adaptibility to varying scenes due illumination
+throughout the algorithm). It provides better adaptability to varying scenes due illumination
changes etc.
As in previous case, we have to create a background subtractor object. Here, you have an option of
( Check similarity of inverse matrix with Harris corner detector. It denotes that corners are better
points to be tracked.)
-So from user point of view, idea is simple, we give some points to track, we receive the optical
+So from the user point of view, the idea is simple, we give some points to track, we receive the optical
flow vectors of those points. But again there are some problems. Until now, we were dealing with
-small motions. So it fails when there is large motion. So again we go for pyramids. When we go up in
-the pyramid, small motions are removed and large motions becomes small motions. So applying
+small motions, so it fails when there is a large motion. To deal with this we use pyramids. When we go up in
+the pyramid, small motions are removed and large motions become small motions. So by applying
Lucas-Kanade there, we get optical flow along with the scale.
Lucas-Kanade Optical Flow in OpenCV
projects / platform / upstream / opencv.git / commitdiff