i3dlib

Documentation

i3dlib Class List

Here are the classes, structs, unions and interfaces with brief descriptions:
i3d::AOSCLMCFilter< VOXELIN, VOXELOUT >This class implements the nonlinear diffusion filter, which was published by Catte, Lions, Morel and Coll. As the other nonlinear filters, this filter smooths the image in the relatively continuos region and do not smooth the significante edges. In the typical output image is suppresed the noise and the edges remain sharp and well localised. The diffused image is the solution of the equation $ u_t = \mathrm{div} (g(|\nabla u_{\sigma}|^2) \nabla u)$
i3d::AOSPMFilter< VOXELIN, VOXELOUT >This class implements the Perona-Malik anisotropic diffusion filter, which was published by Perona and Malik. This filter was historical first published nonlinear diffusion filter and therefore is fundamental. As the other anisotropic filters, this filter smooths the image in the relatively continuos region and do not smooth the significante edges. In the typical output image is suppresed the noise and the edges remain sharp and well localised. The diffused image is the solution of the equation $ u_t = \mathrm{div} (g(|\nabla u|) \nabla u)$
i3d::AOSTVFilter< VOXELIN, VOXELOUT >This class implements the Total Variations nonlinear diffusion filter and the Ballanced Forward Backward filter, which were published by Andreu et al. and Keeling et al. As the other nonlinear filters, these filter smooths the image in the relatively continuos region and do not smooth the significante edges. In the typical output image is suppresed the noise and the edges remain sharp and well localised. The diffused image is the solution of the equation $ u_t = \mathrm{div}(\frac{\nabla u}{|\nabla u|}) $
i3d::BINARY
i3d::Buffer
i3d::components_distance_matrix
i3d::ComponentToRemove< VOXEL >
i3d::ExplicitScheme< VOXELIN, VOXELOUT >This class is together with class ExplicitSchemeFunction the abstract class for segmentation methods and image preproccessing filters, which compute the solution of underlying PDE on the whole domain (i.e. whole image). The explicit scheme in time is implemented in these two sister classes. The finite difference solver implemented in these two "sister" classes computes the update to the solution and the "ideal" (possible maximal) time step in every iteration. The computed update is then added to the solution. These two "sister" classes implements only the solver, the user should instantiate some offspring to get the full functional filter or segmentation method
i3d::ExplicitSchemeFunction< VOXEL >This class is together with class ExplicitSchemeFunction the abstract class for segmentation methods and image preproccessing filters, which compute the solution of underlying PDE on the whole domain (i.e. whole image). The explicit scheme in time is implemented in these two sister classes. The finite difference solver implemented in these two "sister" classes computes the update to the solution and the "ideal" (possible maximal) time step in every iteration. The computed update is then added to the solution. These two "sister" classes implements only the solver, the user should instantiate some offspring to get the full functional filter or segmentation method
i3d::Filter< NUMBER >
i3d::GradientMagnitudeEstimator< VOXEL >This class implements simple Estimator of the gradient magnitude. The Gradient is estimated by simple central differences in the direction of each axe. The Magnitude is computed as the square root of the sum of individual derivatives
Heap< VOXEL, COMP >
i3d::HistFinder< T >
i3d::HistInfoHistogram shape description
i3d::HRCA_Histogram< T >Histogram class
i3d::HRCA_LUT< ITYPE, OTYPE >Class implements operations with LUT for unsigned numerical types (unsigned char and unsigned short)
i3d::I3DReader
i3d::I3DWriter
i3d::ICSReader
i3d::ICSWriter
i3d::Image3d< VOXEL >The main template class which stores and manipulates the image data
i3d::ImageFileHeader
i3d::ImageReader
i3d::ImageWriter
i3d::ImgFormatsTable
i3d::ImplicitAOSScheme< VOXELIN, VOXELOUT >This class is root abstract class for wide family of nonlinear diffusion filters. This class implements the skeleton of numerical semi-implicit AOS scheme. Instances of this class will not produce any usable output. To obtain fully functional nonlinear diffusion filter try to instantiate some offspring of this class
i3d::ImplicitLODScheme< VOXELIN, VOXELOUT >This class is root abstract class for filters, which can be computed by simple LOD numerical scheme. This class implements the skeleton of numerical semi-implicit LOD (locally one dimensional) scheme. Instances of this class will not produce any usable output. To obtain fully functional filter try to instantiate some offspring of this class. As an example can serve the LODGaussianBlur class
i3d::InternalException
i3d::IOException
i3d::JPEGReader
i3d::JPEGWriter
i3d::KMeansLS< VOXELIN, VOXELOUT >This class implements simple the Kmeans segmentation algorithm for two means. Documentation TBA
i3d::LabeledImage3d< LABEL, VOXEL >
i3d::LibException
i3d::LODGaussianBlur< VOXELIN, VOXELOUT >This class implements the classical Gaussian Blur filter by solving the following PDE $ u_t = \Delta u = (u_{xx} + u_{yy}) $. The main advantage is the constant speed of computation for any size of standard deviation $ \sigma $. The speed is comparable to the speed of Fourier transform based filters. The second advantage is that this filter preserve the average grey value of the blurred image
i3d::MCDEFilter< VOXELIN, VOXELOUT >This class implements together with class MCDEFilterFunction the Modified Curvature Diffusion Flow anisotropic diffusion filter, which was published by Whitaker and Xue. As the other anisotropic filters, this filter smooths the image in the relatively continuos region and do not smooth the significante edges. Moreover it generates the so called morphological scale space. In the typical output image is suppresed the noise and the edges remain sharp and well localised. The diffused image is the solution of the equation $ u_t = |\nabla u| \cdot \mathrm{div} \left( g(| \nabla u|)\frac{ \nabla u }{ |\nabla u|} \right) $. The particular implementation is based on explicit finite difference solver and therefore there exist some limitations to the time step
i3d::MCDEFilterFunction< VOXEL >This class implements together with class MCDEFilter the Modified Curvature Diffusion Flow anisotropic diffusion filter, which was published by Whitaker and Xue. As the other anisotropic filters, this filter smooths the image in the relatively continuos region and do not smooth the significante edges. Moreover it generates the so called morphological scale space. In the typical output image is suppresed the noise and the edges remain sharp and well localised. The diffused image is the solution of the equation $ u_t = |\nabla u| \cdot \mathrm{div} \left( g(| \nabla u|)\frac{ \nabla u }{ |\nabla u|} \right) $. The particular implementation is based on explicit finite difference solver and therefore there exist some limitations to the time step. There is no need to instantiate this class in the user program. This class is instantiated automatically in the MCDEFilter class
i3d::MCFFilter< VOXELIN, VOXELOUT >This class implements together with class MCFFilterFunction the Curvature Flow anisotropic diffusion filter, which was published by Alvarez, Lions and Morel. As the other anisotropic filters, this filter smooths the image in the relatively continuos region and do not smooth the significante edges. Moreover it generates the so called morphological scaale space. In the typical output image is suppresed the noise and the edges remain sharp and well localised. The diffused image is the solution of the equation $ u_t = \mathrm{div} ( \nabla u / |\nabla u|) |\nabla u|$. The particular implemantion is based on explicit finite difference solver and therefore there exist some limitations to the time step
i3d::MCFFilterFunction< VOXEL >This class implements together with class MCFFilter the Curvature Flow anisotropic diffusion filter, which was published by Alvarez, Lions and Morel. As the other anisotropic filters, this filter smooths the image in the relatively continuos region and do not smooth the significante edges. Moreover it generates the so called morphological scale space. In the typical output image is suppresed the noise and the edges remain sharp and well localised. The diffused image is the solution of the equation $ u_t = \mathrm{div} ( \nabla u / |\nabla u|) |\nabla u|$. The particular implemantion is based on explicit finite difference solver and therefore there exist some limitations to the time step. There is no need to instantiate this class in the user program. This class is instantiated automatically in the MCFFilter class
i3d::METAIOHeader
i3d::METAIOReader
i3d::METAIOWriter
i3d::Neighbourhood
i3d::ObjectInfo< LABEL, VOXEL >Object in an image
i3d::PDESolver< VOXELIN, VOXELOUT >This class is together with class PDESolverFunction the root abstract class for wide family of classes which implements segmentation methods and image preprocessing filters. All these offsprings of this base class have one common feature. The key idea of those methods or filters is described by partial differential equation. The execution of the methods or application of the filters has in the background the solution of the underlying PDE
i3d::PDESolverFunction< VOXEL >This class is together with class PDESolver the root abstract class for wide family of classes which implements segmentation methods and image preprocessing filters. All these offsprings of this base class have one common feature. The key idea of those methods or filters is described by partial differential equation, which is evolving in time. This class serves to compute the difference between two timesteps
i3d::PMFilter< VOXELIN, VOXELOUT >This class implements together with class PMFilterFunction the Perona-Malik anisotropic diffusion filter, which was published by Perona and Malik. This filter was historical first published anisotropic diffusion filter and therefore is fundamental. As the other anisotropic filters, this filter smooths the image in the relatively continuos region and do not smooth the significante edges. In the typical output image is suppresed the noise and the edges remain sharp and well localised. The diffused image is the solution of the equation $ u_t = \mathrm{div} (g(|\nabla u|) \nabla u)$. The particular implemantion is based on explicit finite difference solver and therefore there exist some limitations to the time step. If you want to use the schema with no timestep limitation take a look to the documentation of AOSPMFilter
i3d::PMFilterFunction< VOXEL >This class implements together with class PMFilter the Perona-Malik anisotropic diffusion filter, which was published by Perona and Malik. This filter was historical first published anisotropic diffusion filter and therefore is fundamental. As the other anisotropic filters, this filter smooths the image in the relatively continuos region and do not smooth the significante edges. In the typical output image is suppresed the noise and the edges remain sharp and well localised. The diffused image is the solution of the equation $ u_t = \mathrm{div} (g(|\nabla u|) \nabla u)$ There is no need to instantiate this class in the user program. This class is instantiated automatically in the PMFilter class
i3d::PrincipalAxes
i3d::Resolution
i3d::RGB
i3d::RGB16
i3d::SequenceReader
i3d::SequenceWriter
sgngreater
i3d::Shape< LABEL >
i3d::SpatialDerivatives< VOXEL >This class implements spatial derivatives operator neede for optical flow comp. Documentation TBA
i3d::TGAReader
i3d::TGAWriter
i3d::ThresholdActiveContours< VOXELIN, VOXELOUT >This class implements the threshold dynamics approximation of the Chan-Vese functional. Documentation TBA
i3d::TIFFReader
i3d::TIFFWriter
i3d::VectField3d< TYPE >
i3d::Vector3d< T >
i3d::VOI< T >