OpenCvSharp.XImgProc Namespace

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.

cv::ximgproc functions

Strategy for the selective search segmentation algorithm.

Interface for realizations of Domain Transform filter.

Class implementing EdgeBoxes algorithm from @cite ZitnickECCV14edgeBoxes

Interface for implementations of Fast Bilateral Solver.

Interface for implementations of Fast Global Smoother filter.

Class implementing the FLD (Fast Line Detector) algorithm described in @cite Lee14.

Interface for realizations of Guided Filter.

Helper class for training part of [P. Dollar and C. L. Zitnick. Structured Forests for Fast Edge Detection, 2013].

Class implementing edge detection algorithm from @cite Dollar2013 :

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.

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.

Class implementing the SLIC (Simple Linear Iterative Clustering) superpixels algorithm described in @cite Achanta2012.

Specifies the part of Hough space to calculate

one form three modes DTF_NC, DTF_RF and DTF_IC which corresponds to three modes for filtering 2D signals in the article.

Specifies to do or not to do skewing of Hough transform image

Specifies binary operations.

Specifies the binarization method to use in cv::ximgproc::niBlackThreshold

Specifies the degree of rules validation.

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.

thinning algorithm

Specifies weight types of weighted median filter.