Namespace OpenCvSharp.XImgProc
Classes
- AdaptiveManifoldFilter
Interface for Adaptive Manifold Filter realizations.
Below listed optional parameters which may be set up with Algorithm::set function.
- member double sigma_s = 16.0 Spatial standard deviation.
- member double sigma_r = 0.2 Color space standard deviation.
- member int tree_height = -1 Height of the manifold tree (default = -1 : automatically computed).
- member int num_pca_iterations = 1 Number of iterations to computed the eigenvector.
- member bool adjust_outliers = false Specify adjust outliers using Eq. 9 or not.
- member bool use_RNG = true Specify use random number generator to compute eigenvector or not.
- ContourFitting
Class for ContourFitting algorithms. ContourFitting matches two contours minimizing distance using their Fourier descriptors, a scaling factor, an angle rotation and a starting point factor adjustment.
- DTFilter
Interface for realizations of Domain Transform filter.
- DisparityFilter
Main interface for all disparity map filters.
- DisparityWLSFilter
Disparity map filter based on Weighted Least Squares filter (in form of Fast Global Smoother that is a lot faster than traditional Weighted Least Squares filter implementations) and optional use of left-right-consistency-based confidence to refine the results in half-occlusions and uniform areas.
- EdgeAwareInterpolator
Sparse match interpolation algorithm based on modified locally-weighted affine estimator and Fast Global Smoother as post-processing filter.
- EdgeBoxes
Class implementing EdgeBoxes algorithm from @cite ZitnickECCV14edgeBoxes
- EdgeDrawing
Class implementing the ED (EdgeDrawing), EDLines, EDPF, EDCircles and ColorED algorithms.
- EdgeDrawingParams
Parameters for EdgeDrawing algorithms.
- FastBilateralSolverFilter
Interface for implementations of Fast Bilateral Solver.
- FastGlobalSmootherFilter
Interface for implementations of Fast Global Smoother filter.
- FastLineDetector
Class implementing the FLD (Fast Line Detector) algorithm described in @cite Lee14.
- GuidedFilter
Interface for realizations of Guided Filter.
- RFFeatureGetter
Helper class for training part of [P. Dollar and C. L. Zitnick. Structured Forests for Fast Edge Detection, 2013].
- RICInterpolator
Sparse match interpolation algorithm based on modified piecewise locally-weighted affine estimator called Robust Interpolation method of Correspondences (RIC), and Variational and Fast Global Smoother as post-processing filter. RICInterpolator is an extension of EdgeAwareInterpolator; its main concept is a piece-wise affine model based on over-segmentation via SLIC superpixel estimation, with an efficient propagation mechanism to estimate among the piece-wise models.
- ScanSegment
Class implementing the F-DBSCAN (Accelerated superpixel image segmentation with a parallelized DBSCAN algorithm) superpixels algorithm.
The algorithm uses a parallelised DBSCAN cluster search that is resistant to noise, competitive in segmentation quality, and faster than existing superpixel segmentation methods. The output is deterministic when the number of processing threads is fixed, and requires the source image to be in Lab colour format.
- SparseMatchInterpolator
Main interface for all filters, that take sparse matches as an input and produce a dense per-pixel matching (optical flow) as an output.
- StructuredEdgeDetection
Class implementing edge detection algorithm from @cite Dollar2013 :
- SuperpixelLSC
Class implementing the LSC (Linear Spectral Clustering) superpixels algorithm described in @cite LiCVPR2015LSC.
LSC(Linear Spectral Clustering) produces compact and uniform superpixels with low computational costs.Basically, a normalized cuts formulation of the superpixel segmentation is adopted based on a similarity metric that measures the color similarity and space proximity between image pixels.LSC is of linear computational complexity and high memory efficiency and is able to preserve global properties of images.
- SuperpixelSEEDS
Class implementing the SEEDS (Superpixels Extracted via Energy-Driven Sampling) superpixels algorithm described in @cite VBRV14.
The algorithm uses an efficient hill-climbing algorithm to optimize the superpixels' energy function that is based on color histograms and a boundary term, which is optional.The energy function encourages superpixels to be of the same color, and if the boundary term is activated, the superpixels have smooth boundaries and are of similar shape. In practice it starts from a regular grid of superpixels and moves the pixels or blocks of pixels at the boundaries to refine the solution.The algorithm runs in real-time using a single CPU.
- SuperpixelSLIC
Class implementing the SLIC (Simple Linear Iterative Clustering) superpixels algorithm described in @cite Achanta2012.
Enums
- AngleRangeOption
Specifies the part of Hough space to calculate
- EdgeAwareFiltersList
one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article.
- GradientOperator
Gradient operators for EdgeDrawing.
- HoughDeskewOption
Specifies to do or not to do skewing of Hough transform image
- HoughOP
Specifies binary operations.
- LocalBinarizationMethods
Specifies the binarization method to use in cv::ximgproc::niBlackThreshold
- RulesOption
Specifies the degree of rules validation.
- SLICType
The algorithm variant to use for SuperpixelSLIC: SLIC segments image using a desired region_size, and in addition SLICO will optimize using adaptive compactness factor, while MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels.
- ThinningTypes
thinning algorithm
- WMFWeightType
Specifies weight types of weighted median filter.