Back to Adaptive Vision Studio website

You are here: Start » Filter Reference » Computer Vision » Image Analysis » DetectCorners_Foerstner

DetectCorners_Foerstner


Module: FoundationBasic

Detects corners using the Foerstner algorithm.

Applications

Detection of characteristic points on an image.
Name Type Range Description
inMonoImage Image Input image
inRoi Region* Range of pixels to be processed
inCornerQuality Real 0.0 - 1.0 Threshold on regularity of the corner
inStrengthThreshold Real 0.0 - 255.0 Threshold on contrast of gradients forming the corner
inLocalness Integer 1 - 11 How big-scaled the corners should be
outPoints Point2DArray Found corner points
diagRoundnessImage Image Calculated roundness for each input pixel
diagStrengthImage Image Calculated strength for each pixel

Requirements

For input inMonoImage only pixel formats are supported: 1⨯uint8, 1⨯int8, 1⨯uint16, 1⨯int16, 1⨯int32, 1⨯real.

Read more about pixel formats in Image documentation.

Description

The operation detects corners using Foerstner algorithm. Its goal is to find corners defined as crossings of image edges.
For every square window of size 2*inLocalness+1 a convolution matrix is computed: \[M = \left(\begin{array}{ccc} \sum g_r^2 & \sum g_c g_r \\ \sum g_c g_r & \sum g_c^2 \end{array} \right) \] where the summation is performed over the whole window and \(g_r, g_c\) denote horizontal and vertical gradient respectively at the point.
Then the strength and so called roundness of the window is computed, where: \[strength = \mathrm{tr}(M)\] \[roundness = \mathrm{tr}(M)^2 / (4*\mathrm{det}(M))\] Roundness measures how similar are the gradients which form the corner.
Only windows with strength greater than inStrengthThreshold and roundness greater than inCornerQuality are considered. Then the non-maximum suppression on the window roundness is performed.
Finally the window's candidate for the corner is determined. It is done by minimizing square distance to all tangent lines (i.e. perpendicular to gradients) within the window, with distances weighted with gradient lengths.

Examples

DetectCorners_Foerstner with different inStrengthThreshold values.

Remarks

Strength of the window corresponds to average gradient strength within the window.
Roundness of the window is always between 0 and 1. For most applications inCornerQuality values below 0.5 are not recommended.
Higher inLocalness values (e.g. greater than 4) may help to get rid of noise on the image, but decrease precision.

Errors

This filter can throw an exception to report error. Read how to deal with errors in Error Handling.

List of possible exceptions:

Error type Description
DomainError Not supported inMonoImage pixel format in DetectCorners_Foerstner.

Complexity Level

This filter is available on Advanced Complexity Level.

Filter Group

This filter is member of DetectCorners filter group.

See Also