Point Feature Types
Image feature detection is a building block of many computer vision tasks, such as image registration, tracking, and object detection. The Computer Vision System Toolbox™ includes a variety of functions for image feature detection. These functions return points objects that store information specific to particular types of features, including (x,y) coordinates (in theLocation
property). You can pass a points object from a detection function to a variety of other functions that require feature points as inputs. The algorithm that a detection function uses determines the type of points object it returns.
Functions That Return Points Objects
Points Object | Returned By | Type of Feature |
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cornerPoints |
detectFASTFeatures Features from accelerated segment test (FAST) algorithm Uses an approximate metric to determine corners.[1] |
Corners |
detectMinEigenFeatures Minimum eigenvalue algorithm Uses minimum eigenvalue metric to determine corner locations.[4] |
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detectHarrisFeatures Harris-Stephens algorithm More efficient than the minimum eigenvalue algorithm.[3] |
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BRISKPoints |
detectBRISKFeatures Binary Robust Invariant Scalable Keypoints (BRISK) algorithm[6] |
Corners |
SURFPoints |
detectSURFFeatures Speeded-up robust features (SURF) algorithm[11] |
Blobs |
KAZEPoints |
detectKAZEFeatures KAZE is not an acronym, but a name derived from the Japanese wordkaze, which means wind. The reference is to the flow of air ruled by nonlinear processes on a large scale.[12] |
![]() Multi-scale blob features Reduced blurring of object boundaries |
MSERRegions |
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Regions of uniform intensity |
Functions That Accept Points Objects
Function | Description | |||
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relativeCameraPose |
Compute relative rotation and translation between camera poses |
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estimateFundamentalMatrix |
Estimate fundamental matrix from corresponding points in stereo images | |||
estimateGeometricTransform |
Estimate geometric transform from matching point pairs | |||
estimateUncalibratedRectification |
Uncalibrated stereo rectification | |||
extractFeatures |
Extract interest point descriptors | |||
Method | Feature Vector | |||
BRISK |
The function sets theOrientation property of thevalidPoints output object to the orientation of the extracted features, in radians. |
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FREAK |
The function sets theOrientation property of thevalidPoints output object to the orientation of the extracted features, in radians. |
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SURF |
The function sets theOrientation property of thevalidPoints output object to the orientation of the extracted features, in radians.When you use an |
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KAZE |
Non-linear pyramid-based features. The function sets the When you use an The |
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Block |
Simple square neighbhorhood. The |
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Auto |
The function selects theMethod based on the class of the input points and implements:
For anM-by-2 input matrix of [xy] coordinates, the function implements the |
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extractHOGFeatures |
Extract histogram of oriented gradients (HOG) features | |||
insertMarker |
Insert markers in image or video | |||
showMatchedFeatures |
Display corresponding feature points | |||
triangulate |
3-D locations of undistorted matching points in stereo images | |||
undistortPoints |
Correct point coordinates for lens distortion |
References
[1] Rosten, E., and T. Drummond, “Machine Learning for High-Speed Corner Detection.” 9th European Conference on Computer Vision. Vol. 1, 2006, pp. 430–443.
[2] Mikolajczyk, K., and C. Schmid. “A performance evaluation of local descriptors.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 27, Issue 10, 2005, pp. 1615–1630.
[3] Harris, C., and M. J. Stephens. “A Combined Corner and Edge Detector.” Proceedings of the 4th Alvey Vision Conference. August 1988, pp. 147–152.
[4] 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.
[5] Tuytelaars, T。k . Mikolajczyk。“当地发票ariant Feature Detectors: A Survey.” Foundations and Trends in Computer Graphics and Vision. Vol. 3, Issue 3, 2007, pp. 177–280.
[6] Leutenegger, S., M. Chli, and R. Siegwart. “BRISK: Binary Robust Invariant Scalable Keypoints.” Proceedings of the IEEE International Conference. ICCV, 2011.
[7] Nister, D., and H. Stewenius. "Linear Time Maximally Stable Extremal Regions." Lecture Notes in Computer Science. 10th European Conference on Computer Vision. Marseille, France: 2008, no. 5303, pp. 183–196.
[8] Matas, J., O. Chum, M. Urba, and T. Pajdla. "Robust wide-baseline stereo from maximally stable extremal regions." Proceedings of British Machine Vision Conference. 2002, pp. 384–396.
[9] Obdrzalek D., S. Basovnik, L. Mach, and A. Mikulik. "Detecting Scene Elements Using Maximally Stable Colour Regions." Communications in Computer and Information Science. La Ferte-Bernard, France: 2009, Vol. 82 CCIS (2010 12 01), pp 107–115.
[10] Mikolajczyk, K., T. Tuytelaars, C. Schmid, A. Zisserman, T. Kadir, and L. Van Gool. "A Comparison of Affine Region Detectors." International Journal of Computer Vision. Vol. 65, No. 1–2, November, 2005, pp. 43–72 .
[11] Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool. “SURF:Speeded Up Robust Features.” Computer Vision and Image Understanding (CVIU).Vol. 110, No. 3, 2008, pp. 346–359.
[12] Alcantarilla, P.F., A. Bartoli, and A.J. Davison. "KAZE Features", ECCV 2012, Part VI, LNCS 7577 pp. 214, 2012
Related Topics
- Detect BRISK Points in an Image and Mark Their Locations
- Find Corner Points in an Image Using the FAST Algorithm
- Find Corner Points Using the Harris-Stephens Algorithm
- Find Corner Points Using the Eigenvalue Algorithm
- Find MSER Regions in an Image
- Detect SURF Interest Points in a Grayscale Image
- Automatically Detect and Recognize Text in Natural Images
- Object Detection in a Cluttered Scene Using Point Feature Matching