goodFeaturesToTrack | R Documentation |
goodFeaturesToTrack
finds the most prominent corners in
an image or in a specified region of the image.
goodFeaturesToTrack(
image,
max_corners,
quality_level,
min_distance,
mask = NULL,
block_size = 3,
gradient_size = 3,
use_harris = FALSE,
k = 0.04
)
image |
An 8-bit (8U) single-channel (GRAY) binary |
max_corners |
The maximum number of corners to return.
|
quality_level |
The minimal accepted quality of the image corners. The
parameter value is multiplied by the best corner quality measure, which is
the minimal eigenvalue (if |
min_distance |
The minimum possible Euclidean distance between the returned corners. |
mask |
A single-channel (GRAY) 8-bit (8U) |
block_size |
Size of an average block for computing a derivative covariation matrix over each pixel neighborhood (default: 3). |
gradient_size |
Aperture parameter for the Sobel operator used for derivatives computation (default: 3). |
use_harris |
A logical indicating whether the corners should be detected using the Harris method or the eigenvalue method (default: FALSE). |
k |
The free parameter of the Harris detector (ignored if
|
A matrix with 2 columns corresponding to the x and y coordinates of the detected points.
Simon Garnier, garnier@njit.edu
Shi, J., & Tomasi. (1994). Good features to track. 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 593–600. https://doi.org/10.1109/CVPR.1994.323794
ORBkeypoints
balloon <- image(system.file("sample_img/balloon1.png", package = "Rvision"))
balloon_gray <- changeColorSpace(balloon, "GRAY")
corners <- goodFeaturesToTrack(balloon_gray, 100, 0.01, 10)
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