Type: Package
Title: Implementation of the Harris Corner Detection for Images
Description: An implementation of the Harris Corner Detection as described in the paper "An Analysis and Implementation of the Harris Corner Detector" by Sánchez J. et al (2018) available at <doi:10.5201/ipol.2018.229>. The package allows to detect relevant points in images which are characteristic to the digital image.
Maintainer: Jan Wijffels <jwijffels@bnosac.be>
License: BSD_2_clause + file LICENSE
Version: 0.1.2
URL: https://github.com/bnosac/image
Imports: Rcpp (≥ 0.12.8)
LinkingTo: Rcpp
Suggests: magick
RoxygenNote: 7.1.2
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2024-01-08 12:55:06 UTC; jwijf
Author: Jan Wijffels [aut, cre, cph] (R wrapper), BNOSAC [cph] (R wrapper), Javier Sánchez Pérez [ctb, cph] (Harris Corner Detector C/C++ code), Pascal Getreuer [ctb, cph] (src/gaussian.cpp)
Repository: CRAN
Date/Publication: 2024-01-08 19:50:02 UTC

Find Corners using Harris Corner Detection

Description

An implementation of the Harris Corner Detection algorithm explained at doi: 10.5201/ipol.2018.229.

Usage

image_harris(
  x,
  k = 0.06,
  sigma_d = 1,
  sigma_i = 2.5,
  threshold = 130,
  gaussian = c("fast Gaussian", "precise Gaussian", "no Gaussian"),
  gradient = c("central differences", "Sobel operator"),
  strategy = c("all corners", "sort all corners", "N corners", "distributed N corners"),
  Nselect = 1L,
  measure = c("Harris", "Shi-Tomasi", "Harmonic Mean"),
  Nscales = 1L,
  precision = c("quadratic approximation", "quartic interpolation", "no subpixel"),
  cells = 10L,
  verbose = FALSE
)

Arguments

x

an object of class magick-image or a greyscale matrix of image pixel values in the 0-255 range where values start at the top left corner.

k

Harris' K parameter. Defaults to 0.06.

sigma_d

Gaussian standard deviation for derivation. Defaults to 1.

sigma_i

Gaussian standard deviation for integration. Defaults to 2.5.

threshold

threshold for eliminating low values. Defaults to 130.

gaussian

smoothing, either one of 'precise Gaussian', 'fast Gaussian' or 'no Gaussian'. Defaults to 'fast Gaussian'.

gradient

calculation of gradient, either one of 'central differences' or 'Sobel operator'. Defaults to 'central differences'.

strategy

strategy for selecting the output corners, either one of 'all corners', 'sort all corners', 'N corners', 'distributed N corners'. Defaults to 'all corners'.

Nselect

number of output corners. Defaults to 1.

measure

either one of 'Harris', 'Shi-Tomasi' or 'Harmonic Mean'. Defaults to 'Harris'.

Nscales

number of scales for filtering out corners. Defaults to 1.

precision

subpixel accuracy, either one of 'no subpixel', 'quadratic approximation', 'quartic interpolation'. Defaults to 'quadratic approximation'

cells

regions for output corners (1x1, 2x2, ..., NxN). Defaults to 10.

verbose

logical, indicating to show the trace of different substeps

Value

as list of the relevant points with the x/y locations as well as the strenght. Note y values start at the top left corner of the image.

Examples



library(magick)
path <- system.file(package = "image.CornerDetectionHarris", 
                    "extdata", "building.png")
x    <- image_read(path)
pts  <- image_harris(x)
pts

plt <- image_draw(x)
points(pts$x, pts$y, col = "red", pch = 20)
dev.off()
plt <- image_draw(x)
points(pts$x, pts$y, 
       col = "red", pch = 20, cex = 5 * pts$strength / max(pts$strength))
dev.off()

## Or pass on a greyscale matrix starting at top left
mat <- image_data(x, channels = "gray")
mat <- as.integer(mat, transpose = FALSE)
mat <- drop(mat)
pts <- image_harris(mat)
plt <- image_draw(x)
points(pts$x, pts$y, col = "red", pch = 20)
dev.off()