extractFeatures

Extract interest point descriptors

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Syntax

[features,validPoints] = extractFeatures(I,points)
[features,validPoints] = extractFeatures(I,points,Name,Value)

Description

example

[features,validPoints] = extractFeatures(I,points) returns extracted feature vectors, also known as descriptors, and their corresponding locations, from a binary or intensity image.

The function derives the descriptors from pixels surrounding an interest point. The pixels represent and match features specified by a single-point location. Each single-point specifies the center location of a neighborhood. The method you use for descriptor extraction depends on the class of the input points.

example

[features,validPoints] = extractFeatures(I,points,Name,Value) uses additional options specified by one or more Name,Value pair arguments.

Examples

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Open Live Script

Read the image.

  I = imread('cameraman.tif');

Find and extract corner features.

  corners = detectHarrisFeatures(I);
  [features, valid_corners] = extractFeatures(I, corners);

Display image.

  figure; imshow(I); hold on

Plot valid corner points.

  plot(valid_corners);

Open Live Script

Read image.

    I = imread('cameraman.tif');

Find and extract features.

    points = detectSURFFeatures(I);
    [features, valid_points] = extractFeatures(I, points);

Display and plot ten strongest SURF features.

    figure; imshow(I); hold on;
    plot(valid_points.selectStrongest(10),'showOrientation',true);

Open Live Script

Read image.

    I = imread('cameraman.tif');

Find features using MSER with SURF feature descriptor.

    regions = detectMSERFeatures(I);
    [features, valid_points] = extractFeatures(I,regions,'Upright',true);

Display SURF features corresponding to the MSER ellipse centers.

    figure; imshow(I); hold on;
    plot(valid_points,'showOrientation',true);

Input Arguments

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Input image, specified as either a binary or 2-D grayscale image.

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

Center location point of a square neighborhood, specified as either a BRISKPoints, SURFPoints, KAZEPoints, MSERRegions, cornerPoints , or ORBPoints object, or an M-by-2 matrix of M number of [x y] coordinates. The table lists the possible input classes of points that can be used for extraction.

Class of Points 
BRISKPointsBinary Robust Invariant Scalable Keypoints (BRISK)
SURFPoints objectSpeeded-Up Robust Features (SURF)
MSERRegions objectMaximally Stable Extremal Regions (MSER)
cornerPointsFeatures from Accelerated Segment Test (FAST), Minimum eigen-value, or Harris
KAZEPointsNon-linear image pyramid-based rotation and orientation invariant features. Similar to SURF, but contains less noisy points.
ORBPointsOriented FAST and rotated BRIEF (ORB) features.
M-by-2 matrix of [x y] coordinatesSimple square neighborhood around [x y] point locations

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 quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'Method','Block' specifies the Block method for descriptor extraction.

Descriptor extraction method, specified as a comma-separated pair consisting of 'Method' and 'FREAK', 'SURF', 'ORB', Block', or 'Auto'.

The table describes how the function implements the descriptor extraction methods.

MethodFeature Vector (Descriptor)
BRISKBinary Robust Invariant Scalable Keypoints (BRISK).
The function sets the Orientation property of the validPoints output object to the orientation of the extracted features, in radians.
FREAKFast Retina Keypoint (FREAK).
The function sets the Orientation property of the validPoints output object to the orientation of the extracted features, in radians.
SURFSpeeded-Up Robust Features (SURF).
The function sets the Orientation property of the validPoints output object to the orientation of the extracted features, in radians.

When you use an MSERRegions object with the SURF method, the Centroid property of the object extracts SURF descriptors. The Axes property of the object selects the scale of the SURF descriptors such that the circle representing the feature has an area proportional to the MSER ellipse area. The scale is calculated as 1/4*sqrt((majorAxes/2).*(minorAxes/2)) and saturated to 1.6, as required by the SURFPoints object.

ORBOriented FAST and rotated BRIEF (ORB) features.
The Orientation property of the validPoints output object is automatically set to the Orientation property of the input ORBPoints object points.
KAZENon-linear pyramid-based features.

The function sets the Orientation property of the validPoints output object to the orientation of the extracted features, in radians.

When you use an MSERRegions object with the KAZE method, the Location property of the object is used to extract KAZE descriptors.

The Axes property of the object selects the scale of the KAZE descriptors such that the circle representing the feature has an area proportional to the MSER ellipse area.

BlockSimple square neighbhorhood.

The Block method extracts only the neighborhoods fully contained within the image boundary. Therefore, the output, validPoints, can contain fewer points than the input POINTS.

AutoThe function selects the Method, based on the class of the input points and implements:
The FREAK method for a cornerPoints input object.
The SURF method for a SURFPoints or MSERRegions input object.
The BRISK method for a BRISKPoints input object.
The ORB method for a ORBPoints input object.

For an M-by-2 input matrix of [x y] coordinates, the function implements the Block method.

Note

The descriptor extraction method must be ORB, if the input points is an ORBPoints object. Also, ORB descriptor extraction method is not supported for any other class of points, except ORBPoints.

Block size, specified as an odd integer scalar. This value defines the local square neighborhood BlockSize-by-BlockSize, centered at each interest point. This option applies only when the function implements the Block method.

Rotation invariance flag, specified a logical scalar. When you set this property to true, the orientation of the feature vectors are not estimated and the feature orientation is set to pi/2. Set this to true when you do not need the image descriptors to capture rotation information. When you set this property to false, the orientation of the features is estimated and the features are then invariant to rotation.

Note

The rotation invariance flag 'Upright' is not supported if the input points is an ORBPoints object.

Length of the SURF or KAZE feature vector (descriptor), specified as 64 or 128. This option applies only when the function implements the SURF or KAZE method. The larger feature size of 128 provides greater accuracy, but decreases the feature matching speed.

Output Arguments

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Feature vectors, returned as a binaryFeatures object or an M-by-N matrix of M feature vectors, also known as descriptors. Each descriptor is of length N.

Valid points associated with each output feature vector (descriptor) in features, returned in the same format as the input. Valid points can be a BRISKPoints, cornerPoints, SURFPoints, KAZEPoints, MSERRegions, ORBPoints object, or an M-by-2 matrix of [x,y] coordinates.

The function extracts descriptors from a region around each interest point. If the region lies outside of the image, the function cannot compute a feature descriptor for that point. When the point of interest lies too close to the edge of the image, the function cannot compute the feature descriptor. In this case, the function ignores the point. The point is not included in the valid points output.

References

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

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

[3] Bay, Herbert, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008.

[4] Alahi, Alexandre, Ortiz, Raphael, and Pierre Vandergheynst, "FREAK: Fast Retina Keypoint", IEEE Conference on Computer Vision and Pattern Recognition, 2012.

[5] Alcantarilla, P.F., A. Bartoli, and A.J. Davison. "KAZE Features", ECCV 2012, Part VI, LNCS 7577 pp. 214, 2012

Extended Capabilities

See Also

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Topics

Introduced in R2011a

Computer Vision Toolbox Documentation

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