points_sojkaT_points_sojkapoints_sojkaPointsSojkaPointsSojka (Operator)

Name

points_sojkaT_points_sojkapoints_sojkaPointsSojkaPointsSojka — Find corners using the Sojka operator.

Signature

Description

points_sojkapoints_sojkapoints_sojkaPointsSojkaPointsSojka defines a corner as the point of intersection of two straight, non-collinear gray value edges. To decide whether a point of the input image ImageImageImageImageimage is a corner or not, a neighborhood of MaskSizeMaskSizeMaskSizeMaskSizemaskSize x MaskSizeMaskSizeMaskSizeMaskSizemaskSize points is inspected. Only those image regions that are relevant for the decision are considered. Pixels with a magnitude of the gradient of less than MinGradMinGradMinGradMinGradminGrad are ignored from the outset.

Furthermore, only those of the remaining points are used that belong to one of the two gray value edges that form the corner. For this, the so called Apparentness is calculated, which is an indicator of the probability that the examined point actually is a corner point. Essentially, it is determined by the number of relevant points and their gradients. A point can only be accepted as a corner when its Apparentness is at least MinApparentnessMinApparentnessMinApparentnessMinApparentnessminApparentness. Typical values of MinApparentnessMinApparentnessMinApparentnessMinApparentnessminApparentness should range in the region of a few multiples of MinGradMinGradMinGradMinGradminGrad.

To calculate the Apparentness, each mask point is weighted according to two criteria: First, the influence of a mask point is weighted with a Gaussian of size SigmaWSigmaWSigmaWSigmaWsigmaW according to its distance from the possible corner point. SigmaWSigmaWSigmaWSigmaWsigmaW should be roughly a quarter to the half of MaskSizeMaskSizeMaskSizeMaskSizemaskSize to obtain a reasonable proportion of the size of the weighting function to the mask size. Secondly, the distance of the point from the (assumed) ideal gray value edge is estimated and the point is weighted with a Gaussian of size SigmaDSigmaDSigmaDSigmaDsigmaD according to that distance. I.e., pixels that (due to the discretization of the input image) lie farther from the ideal gray value edge have less influence on the result than pixels with a smaller distance. Typically, it is not necessary to modify the default value 0.75 of SigmaDSigmaDSigmaDSigmaDsigmaD .

As a further criterion, the angle is calculated, by which the gray value edges change their direction in the corner point. A point can only be accepted as a corner when this angle is greater than MinAngleMinAngleMinAngleMinAngleminAngle.

The position of the detected corner points is returned in (RowRowRowRowrow, ColumnColumnColumnColumncolumn). RowRowRowRowrow and ColumnColumnColumnColumncolumn are calculated with subpixel accuracy if SubpixSubpixSubpixSubpixsubpix is 'true'"true""true""true""true". They are calculated only with pixel accuracy if SubpixSubpixSubpixSubpixsubpix is 'false'"false""false""false""false".

Parallelization

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

Result

points_sojkapoints_sojkapoints_sojkaPointsSojkaPointsSojka returns 2 (H_MSG_TRUE) if all parameters are correct and no error occurs during the execution. If the input is empty the behavior can be set via set_system('no_object_result',<Result>)set_system("no_object_result",<Result>)set_system("no_object_result",<Result>)SetSystem("no_object_result",<Result>)SetSystem("no_object_result",<Result>). If necessary, an exception is raised.

References

Eduard Sojka: “A New and Efficient Algorithm for Detecting the Corners in Digital Images”. Pattern Recognition, Luc Van Gool (Editor), LNCS 2449, pp. 125-132, Springer Verlag, 2002.

Module

Foundation