Class implementing the F-DBSCAN (Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm) superpixels algorithm by Loke SC, et al. [loke2021accelerated] for original paper.
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virtual | ~ScanSegment () |
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virtual void | getLabelContourMask (OutputArray image, bool thick_line=false)=0 |
| Returns the mask of the superpixel segmentation stored in the ScanSegment object.
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virtual void | getLabels (OutputArray labels_out)=0 |
| Returns the segmentation labeling of the image.
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virtual int | getNumberOfSuperpixels ()=0 |
| Returns the actual superpixel segmentation from the last image processed using iterate.
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virtual void | iterate (InputArray img)=0 |
| Calculates the superpixel segmentation on a given image with the initialized parameters in the ScanSegment object.
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| Algorithm () |
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virtual | ~Algorithm () |
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virtual void | clear () |
| Clears the algorithm state.
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virtual bool | empty () const |
| Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
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virtual String | getDefaultName () const |
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virtual void | read (const FileNode &fn) |
| Reads algorithm parameters from a file storage.
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virtual void | save (const String &filename) const |
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void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
| simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
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virtual void | write (FileStorage &fs) const |
| Stores algorithm parameters in a file storage.
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Class implementing the F-DBSCAN (Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm) superpixels algorithm by Loke SC, et al. [loke2021accelerated] for original paper.
The algorithm uses a parallelised DBSCAN cluster search that is resistant to noise, competitive in segmentation quality, and faster than existing superpixel segmentation methods. When tested on the Berkeley Segmentation Dataset, the average processing speed is 175 frames/s with a Boundary Recall of 0.797 and an Achievable Segmentation Accuracy of 0.944. The computational complexity is quadratic O(n2) and more suited to smaller images, but can still process a 2MP colour image faster than the SEEDS algorithm in OpenCV. The output is deterministic when the number of processing threads is fixed, and requires the source image to be in Lab colour format.