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# detectMinEigenFeatures

Detect corners using minimum eigenvalue algorithm and return cornerPoints object

## Syntax

• points = detectMinEigenFeatures(I) example
• points = detectMinEigenFeatures(I,Name,Value)

## Description

example

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.

Code Generation Support:
Compile-time constant input: FilterSize
Supports MATLAB Function block: No
Generated code for this function uses a precompiled platform-specific shared library.
Code Generation Support, Usage Notes, and Limitations

## Examples

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### Find Corner Points Using the Eigenvalue Algorithm

```I = checkerboard;
```

Find the corners.

```corners = detectMinEigenFeatures(I);
```

Display the results.

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

## Input Arguments

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### I — Input imageM-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 Pair 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 single 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 corners0.01 (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 dimension5 (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

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### points — Corner pointscornerPoints 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.