i.edge - GRASS GIS manual

NAME

i.edge - Canny edge detector.

KEYWORDS

SYNOPSIS

i.edge

i.edge --help

i.edge [-n] input=name output=name [angles_map=name] [low_threshold=float] [high_threshold=float] [sigma=float] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-n: Create empty output if input map is empty; Default: no output and ERROR
--overwrite: Allow output files to overwrite existing files
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

input=name [required]: Name of input raster map
output=name [required]: Name for output raster map
angles_map=name: Map with angles
low_threshold=float: Low treshold for edges in Canny; Default: 3
high_threshold=float: High treshold for edges in Canny; Default: 10
sigma=float: Kernel radius; Default: 2

DESCRIPTION
NOTES
- Algorithm
- Inputs and parameters
EXAMPLE
KNOWN ISSUES
REFERENCES
SEE ALSO
AUTHORS

DESCRIPTION

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.

NOTES

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.

Algorithm

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].

Inputs and parameters

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 (lT) high_threshold (hT). It is recommended to use lT and hT threshold values in ratio (computed as hT/lT) between 2 and 3 [Sonka1999].

EXAMPLE

# set the region (resolution) to Landsat image
g.region raster=lsat7_2000_20@landsat

# 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

KNOWN ISSUES

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.

REFERENCES

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: https://www.tomgibara.com/computer-vision/canny-edge-detector.
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.

AUTHORS

Anna Kratochvilova, Vaclav Petras

SOURCE CODE

Available at: i.edge source code (history)

Latest change: Monday Nov 11 18:04:48 2024 in commit: 59e289fdb093de6dd98d5827973e41128196887d