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Keypoint Detection with ORB

Source: R/feature.R
ORBkeypoints.Rd

ORBkeypoints finds and describes keypoints in an image using the ORB method. Keypoints are prominent features that can be used to quickly match images.

Usage

ORBkeypoints(
  image,
  mask = NULL,
  n_features = 500,
  scale_factor = 1.2,
  n_levels = 8,
  edge_threshold = 31,
  first_level = 0,
  WTA_K = 2,
  score_type = "HARRIS",
  patch_size = 31,
  fast_threshold = 20
)

Arguments

image

An Image object.

mask

A binary Image object with the same dimensions as image. This can be used to mask out pixels that should not be considered when searching for keypoints (pixels set to 0 in the mask will be ignored during the search).

n_features

The maximum number of features to retain.

scale_factor

The pyramid decimation ratio, always greater than 1 (default: 1.2). scaleFactor = 2 uses a "classical" pyramid, where each level has 4 times less pixels than the previous one. Such a large scale factor will degrade feature matching scores dramatically. On the other hand, a scale factor too close to 1 will require longer computation times.

n_levels

The number of pyramid decimation levels (default: 8).

edge_threshold

The size of the border where the features are not detected. It should roughly match the patch_size parameter below (default: 31).

first_level

The level of the pyramid to put the source image into (default: 0). Previous levels are filled with upscaled versions of the source image.

WTA_K

The number of points that produce each element of the oriented BRIEF descriptor for a keypoint. WTA_K = 2 (the default) takes a random pair of points and compare their brightness, yielding a binary response. WTA_K = 3 takes 3 random points, finds the point of maximum brightness, and output the index of the winner (0, 1 or 2). WTA_K = 4 perform the operation but with 4 random points , and output the index of the winner (0, 1, 2, or 3). With WTA_K = 3 and WTA_K = 4, the output will require 2 bits for storage and, therefore, will need a special variant of the Hamming distance for keypoint matching ("BruteForce-Hamming(2)" in matchKeypoints).

score_type

A character string indicating the the scoring method to use. "HARRIS" (the default) uses the Harrisalgorithm to rank the detected features. "FAST" is an alternative method that produces slightly less stable keypoints but is a little faster to compute.

patch_size

The size of the patch used to compute the the oriented BRIEF descriptor (default: 31).

fast_threshold

A threshold for selecting "good enough" keypoints (default: 20)

Value

A list with two elements:

  • keypoints: a matrix containing the following information about each keypoint:

    • angle: the keypoint orientation in degrees, between 0 and 360, measured relative to the image coordinate system, i.e., clockwise.

    • octave: the pyramid layer from which the keypoint was extracted.

    • x: the x coordinate of the keypoint.

    • y: the y coordinate of the keypoint.

    • response: the response by which the keypoint have been selected. This can be used for the further sorting or subsampling.

    • size: the diameter of the keypoint neighborhood.

  • descriptors: a single-channel Image with each row corresponding to the BRIEF descriptor of a single keypoint.

See also

matchKeypoints, goodFeaturesToTrack, findTransformORB

Author

Simon Garnier, garnier@njit.edu

Examples

dots <- image(system.file("sample_img/dots.jpg", package = "Rvision"))
kp <- ORBkeypoints(dots, n_features = 40000)
plot(dots)
points(kp$keypoints[, c("x", "y")], pch = 19, col = "red")