image_contour_detector: Unsupervised Smooth Contour Lines Detection in an image

Description Usage Arguments Value References Examples

View source: R/contour_detector.R

Description

Unsupervised Smooth Contour Detection.

Following the a contrario approach, the starting point is defining the conditions where contours should not be detected: soft gradient regions contaminated by noise. To achieve this, low frequencies are removed from the input image. Then, contours are validated as the frontiers separating two adjacent regions, one with significantly larger values than the other. Significance is evaluated using the Mann-Whitney U test to determine whether the samples were drawn from the same distribution or not. This test makes no assumption on the distributions. The resulting algorithm is similar to the classic Marr-Hildreth edge detector, with the addition of the statistical validation step. Combined with heuristics based on the Canny and Devernay methods, an efficient algorithm is derived producing sub-pixel contours.

Usage

1

Arguments

x

a matrix of image pixel values in the 0-255 range.

Q

numeric value with the pixel quantization step

Value

an object of class cld which is a list with the following elements

References

Rafael Grompone von Gioi, and Gregory Randall, Unsupervised Smooth Contour Detection, Image Processing On Line, 6 (2016), pp. 233-267. https://doi.org/10.5201/ipol.2016.175

Examples

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library(pixmap)
imagelocation <- system.file("extdata", "image.pgm", package="image.ContourDetector")
image <- read.pnm(file = imagelocation, cellres = 1)
x <- image@grey * 255
contourlines <- image_contour_detector(x)
contourlines
plot(image)
plot(contourlines, add = TRUE, col = "red")

##
## line_segment_detector expects a matrix as input
##  if you have a jpg/png/... convert it to pgm first or take the r/g/b channel
library(magick)
x   <- image_read(system.file("extdata", "atomium.jpg", package="image.ContourDetector"))
x
mat <- image_data(x, channels = "gray")
mat <- as.integer(mat, transpose = TRUE)
mat <- drop(mat)
contourlines <- image_contour_detector(mat)
plot(contourlines)

Example output

Read 262144 items
Contour Lines Detector
  found 192 contour lines
Linking to ImageMagick 6.9.10.23
Enabled features: fontconfig, freetype, fftw, lcms, pango, webp, x11
Disabled features: cairo, ghostscript, rsvg
  format width height colorspace matte filesize density
1   JPEG   750    504       sRGB FALSE    85791   72x72

image.ContourDetector documentation built on July 27, 2020, 5:07 p.m.