detectMinEigenFeatures

Detect corners using minimum eigenvalue algorithm and return cornerPoints object

Syntax

points = detectMinEigenFeatures(I)

points = detectMinEigenFeatures(I,Name,Value)

Description

points = detectMinEigenFeatures(I) returns a cornerPoints object, points. The object contains information about the feature points detected in a 2-D grayscale input image, I. The detectMinEigenFeatures function uses the minimum eigenvalue algorithm developed by Shi and Tomasi to find feature points.

points = detectMinEigenFeatures(I,Name,Value) uses additional options specified by one or more Name,Value pair arguments.

Examples

Find Corner Points Using the Eigenvalue Algorithm

Read the image.

I = checkerboard;

Find the corners.

corners = detectMinEigenFeatures(I);

Display the results.

imshow(I); hold on;
plot(corners.selectStrongest(50));

Input Arguments

I — Input image

M-by-N 2-D grayscale image

Input image, specified in 2-D grayscale. The input image must be real and nonsparse.

Data Types: single | double | int16 | uint8 | uint16 | logical

Name-Value Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'MinQuality','0.01','ROI', [50,150,100,200] specifies that the detector must use a 1% minimum accepted quality of corners within the designated region of interest. This region of interest is located at x=50, y=150. The ROI has a width of 100 pixels, and a height of 200 pixels.

MinQuality — Minimum accepted quality of corners

0.01 (default)

Minimum accepted quality of corners, specified as the comma-separated pair consisting of 'MinQuality' and a scalar value in the range [0,1].

The minimum accepted quality of corners represents a fraction of the maximum corner metric value in the image. Larger values can be used to remove erroneous corners.

Example: 'MinQuality', 0.01

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

FilterSize — Gaussian filter dimension

5 (default)

Gaussian filter dimension, specified as the comma-separated pair consisting of 'FilterSize' and an odd integer value in the range [3, inf).

The Gaussian filter smooths the gradient of the input image.

The function uses the FilterSize value to calculate the filter’s dimensions, FilterSize-by-FilterSize. It also defines the standard deviation as FilterSize/3.

Example: 'FilterSize', 5

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

ROI — Rectangular region

[1 1 size(I,2) size(I,1)] (default) | vector

Rectangular region for corner detection, specified as a comma-separated pair consisting of 'ROI' and a vector of the format [x y width height]. The first two integer values [x y] represent the location of the upper-left corner of the region of interest. The last two integer values represent the width and height.

Example: 'ROI', [50,150,100,200]

Output Arguments

points — Corner points

cornerPoints object

Corner points, returned as a cornerPoints object. The object contains information about the feature points detected in the 2-D grayscale input image.

References

[1] Shi, J., and C. Tomasi, "Good Features to Track," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 1994, pp. 593–600.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• Use in a MATLAB Function block is not supported.

• 'FilterSize' must be a compile-time constant.

• Generated code for this function uses a precompiled platform-specific shared library.