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detectSURFFeatures

Detect SURF features and return SURFPoints object

Syntax

  • points = detectSURFFeatures(I)
  • points = detectSURFFeatures(I,Name,Value)

Description

points = detectSURFFeatures(I) returns a SURFPoints object, POINTS, containing information about SURF features detected in the 2-D grayscale input image I. The detectSURFFeatures function implements the Speeded-Up Robust Features (SURF) algorithm to find blob features.

points = detectSURFFeatures(I,Name,Value) Additional control for the algorithm requires specification of parameters and corresponding values. An additional option is specified by one or more Name,Value pair arguments.

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

Input Arguments

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I — Input imageM-by-N 2-D grayscale image

Input image, specified as an M-by-N 2-D grayscale. The input image must be a real nonsparse value.

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.

'MetricThreshold' — Strongest feature threshold1000.0 (default) | non-negative scalar

Strongest feature threshold, specified as the comma-separated pair consisting of 'MetricThreshold' and a non-negative scalar. To return more blobs, decrease the value of this threshold.

'NumOctaves' — Number of octaves3 (default) | scalar, greater than or equal to 1

Number of octaves to implement, specified as the comma-separated pair consisting of 'NumOctaves' and an integer scalar, greater than or equal to 1. Increase this value to detect larger blobs. Recommended values are between 1 and 4.

Each octave spans a number of scales that are analyzed using varying size filters:

OctaveFilter sizes
1 9-by-9, 15-by-15, 21-by-21, 27-by-27, ...
2 15-by-15, 27-by-27, 39-by-39, 51-by-51, ...
3 27-by-27, 51-by-51, 75-by-75, 99-by-99, ...
4....

Higher octaves use larger filters and subsample the image data. Larger number of octaves will result in finding larger size blobs. Set the NumOctaves parameter appropriately for the image size. For example, a 50-by-50 image should not require you to set the NumOctaves parameter, greater than 2. The NumScaleLevels parameter controls the number of filters used per octave. At least three levels are required to analyze the data in a single octave.

'NumScaleLevels' — Number of scale levels per octave4 (default) | integer scalar, greater than or equal to 3

Number of scale levels per octave to compute, specified as the comma-separated pair consisting of 'NumScaleLevels' and an integer scalar, greater than or equal to 3. Increase this number to detect more blobs at finer scale increments. Recommended values are between 3 and 6.

'ROI' — Rectangular region of interest[1 1 size(I,2) size(I,1)] (default) | vector

Rectangular region of interest, specified as a vector. The vector must be in the format [x y width height]. When you specify an ROI, the function detects corners within the area located at [x y] of size specified by [width height] . The [x y] elements specify the upper left corner of the region.

Output Arguments

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points — SURF featuresSURFPoints object

SURF features, returned as a SURFPoints object. This object contains information about SURF features detected in a grayscale image.

Examples

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Detect SURF Interest Points in a Grayscale Image

Read image and detect interest points.

    I = imread('cameraman.tif');
    points = detectSURFFeatures(I);

Display locations of interest in image.

    imshow(I); hold on;
    plot(points.selectStrongest(10));

References

[1] Bradski, G. and A. Kaehler, Learning OpenCV : Computer Vision with the OpenCV Library. O'Reilly: Sebastopol, CA, 2008.

[2] Herbert, B., A. Ess, T. Tuytelaars, and L. Van Gool, SURF: "Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008.

See Also

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