- Canny edge detector.
, edge detection
i.edge [-n] input=name output=name [angles_map=name] [low_threshold=float] [high_threshold=float] [sigma=float] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
- Create empty output if input map is empty
- Default: no output and ERROR
- Allow output files to overwrite existing files
- Print usage summary
- Verbose module output
- Quiet module output
- Force launching GUI dialog
- input=name [required]
- Name of input raster map
- output=name [required]
- Name for output raster map
- Map with angles
- Low treshold for edges in Canny
- Default: 3
- High treshold for edges in Canny
- Default: 10
- Kernel radius
- Default: 2
i.edge is an edge detector based on the Canny edge detection
algorithm [Canny1986]. The Canny edge detector is a filter which detects
a wide range of edges in raster maps and produces thin edges as a raster map.
The computational region shall be set to the input map.
The module can work only on small images since the data are loaded entirely into memory.
An edge is considered as a change in gradient which is computed from image digital
values. There are two main noticeable differences between
Canny filter and other edge detectors. First, the others algorithm usually
output broad lines (edges) while Canny filter outputs one-pixel-wide line(s)
which represents the most probable edge position [Russ2011].
Second, the Canny filter combines several steps together while
other filters have only one step and often require some pre- or post-processing to get
results which allows further processing. However, it must be noted that by applying
subsequent filters, thresholding and edge thinning one can get similar results also
from other edge detectors.
The implementation used for i.edge module is based on code from [Gibara2010].
Canny edge detector is considered as optimal edge detector according to
these three criteria [Sonka1999]:
- important edges cannot be omitted and only actual edges can be detected as
edges (no false positives);
- difference in position of the real edge and the detected edge is minimal;
- there is only one detected edge for an edge in original image.
The algorithm consists of a few steps.
Firstly, the noise is reduced by a Gaussian filter (based on normal distribution); the
result is smoothed image. Secondly, two orthogonal gradient images are computed.
These images are combined, so the final gradient can be defined by an angle and
a magnitude (value). Next step is non-maximum suppression which preserves only
pixels with magnitude higher than magnitude of other pixels in the direction (and the
opposite direction) of gradient. Finally, only relevant or significant edges extracted
by thresholding with hysteresis. This thresholding uses two constants; if a pixel
magnitude is above the higher one (hT), it is kept.
Pixels with the magnitude under the lower threshold (lT) are removed.
Pixels with magnitude values between these two constants
are kept only when the pixels has some neighbor pixels with magnitude higher than
the high threshold [Sonka1999].
The input is a gray scale image (a raster map). Usually, this gray scale
image is an intensity channel obtained by RGB to HIS conversion. Some other
possibilities include color edges (obtained from RGB color channels) which
may give slightly better results [Zimmermann2000]. In theory, i.edge
module can be applied not only to images but also to digital elevation models
and other data with abrupt changes in raster values.
The output is a binary raster where ones denote edges and zeros denote
everything else. There are also possible byproducts or intermediate products which
can be part of the output, namely edge angles (gradient orientations).
By changing parameters of the module one can easily achieve different levels of
detail. There are 3 parameters which affect the result. A sigma
value and two threshold values, low_threshold
). It is recommended to use
threshold values in ratio (computed
) between 2 and 3 [Sonka1999].
# set the region (resolution) to Landsat image
# set the region to experimental area
g.region n=224016 s=220981 w=637589 e=641223
# compute PCA for all Landsat maps for year 2002
i.pca input=`g.list pattern="lsat7_2002*" type=rast sep=,` output_prefix=lsat_pca
# run edge detection on first component
i.edge input=lsat_pca.1 output=lsat_pca_1_edges
# set no edges areas to NULL (for visualization)
r.null map=lsat_pca_1_edges setnull=0
Computational region shall be set to input map. The module can work only
on small images since map is loaded into memory.
Edge strengths (gradient values) are not provided as an output but might be added in the future.
- J. Canny. A Computational Approach to Edge Detection. In: IEEE Trans.
Pattern Anal. Mach. Intell. 8.6 (June 1986), pp. 679–698. issn: 0162-8828.
- J.C. Russ. The image processing handbook. CRC, 2011.
- Tom Gibara. Canny Edge Detector Implementation. [Online; accessed 20-June-
2012]. 2010. URL:
- M. Sonka, V. Hlavac, and R. Boyle. Image processing, analysis, and machine
vision. PWS Pub. Pacific Grove (1999).
- P. Zimmermann. A new framework for automatic building detection analysing
multiple cue data. In: International Archives of Photogrammetry and Remote
Sensing 33.B3/2; PART 3 (2000), pp. 1063–1070.
i.edge source code
Latest change: Wednesday Feb 02 21:46:15 2022 in commit: 2e3dcb5eafe36d640aa68585be0192f07ca1eabb
Main index |
Imagery index |
Topics index |
Keywords index |
Graphical index |
GRASS Development Team,
GRASS GIS 8.2.0 Reference Manual