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void | setScaleFactor (float scale_factor) |
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float | getScaleFactor () |
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override string | getDefaultName () |
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void | detect (Mat image, MatOfKeyPoint keypoints, Mat mask) |
| Detects keypoints in an image (first variant) or image set (second variant).
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void | detect (Mat image, MatOfKeyPoint keypoints) |
| Detects keypoints in an image (first variant) or image set (second variant).
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void | detect (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > masks) |
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void | detect (List< Mat > images, List< MatOfKeyPoint > keypoints) |
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void | compute (Mat image, MatOfKeyPoint keypoints, Mat descriptors) |
| Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).
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void | compute (List< Mat > images, List< MatOfKeyPoint > keypoints, List< Mat > descriptors) |
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void | detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors, bool useProvidedKeypoints) |
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void | detectAndCompute (Mat image, Mat mask, MatOfKeyPoint keypoints, Mat descriptors) |
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int | descriptorSize () |
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int | descriptorType () |
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int | defaultNorm () |
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void | write (string fileName) |
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void | read (string fileName) |
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override bool | empty () |
| Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
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override string | getDefaultName () |
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IntPtr | getNativeObjAddr () |
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virtual void | clear () |
| Clears the algorithm state.
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void | save (string filename) |
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void | Dispose () |
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void | ThrowIfDisposed () |
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Class implementing BEBLID (Boosted Efficient Binary Local Image Descriptor), described in [Suarez2020BEBLID] .
BEBLID [Suarez2020BEBLID] is a efficient binary descriptor learned with boosting. It is able to describe keypoints from any detector just by changing the scale_factor parameter. In several benchmarks it has proved to largely improve other binary descriptors like ORB or BRISK with the same efficiency. BEBLID describes using the difference of mean gray values in different regions of the image around the KeyPoint, the descriptor is specifically optimized for image matching and patch retrieval addressing the asymmetries of these problems.
If you find this code useful, please add a reference to the following paper: <BLOCKQUOTE> Iago Suárez, Ghesn Sfeir, José M. Buenaposada, and Luis Baumela. BEBLID: Boosted efficient binary local image descriptor. Pattern Recognition Letters, 133:366–372, 2020. </BLOCKQUOTE>
The descriptor was trained using 1 million of randomly sampled pairs of patches (20% positives and 80% negatives) from the Liberty split of the UBC datasets [winder2007learning] as described in the paper [Suarez2020BEBLID]. You can check in the AKAZE example how well BEBLID works. Detecting 10000 keypoints with ORB and describing with BEBLID obtains 561 inliers (75%) whereas describing with ORB obtains only 493 inliers (63%).