The image.ContourDetector package detects contour lines in images

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.

References

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

See Also

image_contour_detector

Unsupervised Smooth Contour Lines Detection in an image

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

image_contour_detector(x, Q = 2, ...)

Arguments

Value

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

• curves: The number of contour lines found

• contourpoints: The number of points defining the contour lines found

• data: A data.frame with columns 'x', 'y' and 'curve' giving the x/y locations for each contour curve

References

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

Examples

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, Q = 2)
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)


##
##  working with a RasterLayer
##


library(raster)
x   <- raster(system.file("extdata", "landscape.tif", package="image.ContourDetector"))

contourlines <- image_contour_detector(x)
image(x)
plot(contourlines, add = TRUE, col = "blue", lwd = 10)

Plot the detected contour lines from the image_contour_detector

Description

Plot the detected contour lines from the image_contour_detector

Usage

## S3 method for class 'cld'
plot(x, ...)

Arguments

Value

invisibly a SpatialLines object with the contour lines

Examples

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