| CdrwnPersistentStorageBuffer< T >::_drwnRecordEntry | |
| CdrwnDisjointSets::_node_t | |
| ►Cbinary_function | |
| CdrwnNNGraphSortByImage | |
| CdrwnNNGraphSortByScore | |
| CdrwnBitArray | Implements an efficient packed array of bits |
| ►CdrwnClassificationResults | Encapsulates summary of classifier output from which various curves can be generated (e.g., precision-recall curves) |
| CdrwnPRCurve | Precision-recall curve |
| ►CdrwnCloneable | Interface for cloning object (i.e., virtual copy constructor) |
| ►CdrwnFeatureMap | Defines the interface for a feature mapping  |
| CdrwnBiasFeatureMap | Augments input feature vector with 1 (i.e., to allow for a bias weight) |
| CdrwnIdentityFeatureMap | Copies input feature space to output feature space |
| CdrwnQuadraticFeatureMap | Augments input feature vector with square of each feature (normalized so that if input is zero mean and unit variance so will output) as well as cross-terms and constant one (i.e., to allow for a bias weight) |
| CdrwnSquareFeatureMap | Augments input feature vector with square of each feature (normalized so that if input is zero mean and unit variance so will output) and 1 (i.e., to allow for a bias weight) |
| ►CdrwnJointFeatureMap | Defines the interface for a joint feature mapping  |
| CdrwnBiasJointFeatureMap | Same as drwnIdentityJointFeatureMap but adds a bias term for each class i.e.,  |
| CdrwnIdentityJointFeatureMap | Includes a copy of each feature from the input space for each class other than the last, i.e.,  . This is the standard feature mapping for multi-class logistic models |
| CdrwnQuadraticJointFeatureMap | Same as drwnSquareJointFeatureMap but adds cross-terms |
| CdrwnSquareJointFeatureMap | Same as drwnIdentityJointFeatureMap but adds a square term for each feature i.e.,  |
| ►CdrwnSegImagePixelFeatures | Interface for generating per-pixel features for a drwnSegImageInstance object |
| CdrwnSegImageCompositePixelFeatures | Class for generating composite per-pixel feature vectors |
| CdrwnSegImageFilePixelFeatures | Pre-processed per-pixel features stored in files |
| CdrwnSegImageStdPixelFeatures | Standard per-pixel filterbank features with option to read auxiliary features from a file |
| ►CdrwnSegImageRegionFeatures | Interface for generating per-region (or per-superpixel) features for a drwnSegImageInstance object. The superpixel data member of the drwnSegImageInstance object must be populated |
| CdrwnSegImageStdRegionFeatures | Standard per-region filterbank features computes mean and standard deviation of drwnTextonFilterBank responses over each region |
| ►CdrwnStdObjIface | Standard Darwin object interface (cloneable and writeable) |
| ►CdrwnClassifier | Implements the interface for a generic machine learning classifier |
| CdrwnBoostedClassifier | Implements a mult-class boosted decision-tree classifier. See Zhu et al., Multi-class AdaBoost, 2006 |
| CdrwnCompositeClassifier | Implements a multi-class classifier by combining binary classifiers |
| CdrwnDecisionTree | Implements a (binary-split) decision tree classifier of arbitrary depth |
| ►CdrwnMultiClassLogisticBase | Common functionality for drwnMultiClassLogistic |
| CdrwnTMultiClassLogistic< FeatureMap > | Implements a multi-class logistic classifier templated on a drwnJointFeatureMap |
| CdrwnTMultiClassLogistic< drwnBiasJointFeatureMap > | |
| CdrwnRandomForest | Implements a Random forest ensemble of decision trees classifier. See L. Breiman, "Random Forests", Machine Learning, 2001 |
| CdrwnColourHistogram | Specialized histogram for quantized 3-channel colour values (e.g., RGB) |
| CdrwnConditionalGaussian | Utility class for generating conditonal gaussian distribution |
| CdrwnCondSuffStats | Implements a class for accumulating conditional first- and second-order sufficient statistics |
| CdrwnDeformationCost | Structure for holding dx, dy, dx^2 and dy^2 deformation costs |
| ►CdrwnFactor | Generic interface for a factor. Currently only inherited by drwnTableFactor |
| CdrwnSparseFactor | Encapsulates variables that the represented function is a function of. Allows user to add variables, look up variable cardinality and retrieve all variables. Implementation uses coordinate format (Bader and Kolda, SIAM Journal on Scientific Computing, 2007) |
| CdrwnTableFactor | Factor which stores the value of each assignment explicitly in table form |
| CdrwnFactorGraph | Container and utility functions for factor graphs |
| ►CdrwnFeatureTransform | Implements the interface for a generic feature transforms possibly with learned parameters, e.g., PCA (unsupervised) or LDA (supervised) |
| CdrwnLinearTransform | Implements a linear feature transform with externally settable parameters |
| ►CdrwnSupervisedTransform | Implements interface for supervised feature transforms (i.e., with class labels) |
| CdrwnFisherLDA | Fisher's linear discriminant analysis (LDA) |
| CdrwnTFeatureMapTransform< FeatureMap > | Helper feature transformation based on a drwnFeatureMap |
| ►CdrwnUnsupervisedTransform | Implements interface for unsupervised feature transforms (i.e, without class labels) |
| CdrwnFeatureWhitener | Whitens (zero mean, unit variance) feature vector (see also drwnPCA) |
| CdrwnKMeans | Implements k-means clustering. Outputs the squared-distance to each of the cluster centroids. The nearest cluster can be found by passing the output to the drwn::argmin function. Supports weighted training examples |
| CdrwnPCA | Principal component analysis feature transformation |
| CdrwnGaussian | Implements a multi-variate gaussian distribution |
| CdrwnGaussianMixture | Implements a multi-variant Gaussian mixture model |
| CdrwnPart | A part is defined as a template over a number of channels (possibly one), an offset from the object centroid, and a deformation cost. The part is scored as w^T F(x) where w are the template weights, and F(x) is the feature vector at location x in feature space. Note for edge templates this can be in pixels, but generally will be in some multiple of pixels. It is up to the inference model to handle conversion from feature space to pixel space |
| ►CdrwnPartsModel | Interface for implementing a part-based constellation model (i.e., pictorial structures model) for object detection |
| CdrwnHOGPartsModel | |
| CdrwnTemplatePartsModel | |
| CdrwnPixelNeighbourContrasts | Convenience class for holding pixel contrast weights |
| ►CdrwnRegression | Implements the interface for a generic machine learning regression, e.g. see drwnLinearRegressor |
| ►CdrwnLinearRegressorBase | Common functionality for drwnLinearRegressor |
| CdrwnTLinearRegressor< FeatureMap > | Implements linear regression optimization templated on a drwnFeatureMap |
| CdrwnSuffStats | Implements a class for accumulating first- and second-order sufficient statistics (moments) |
| CdrwnVarUniverse | Data structure for definining the random variables (name and cardinality) for a given problem instance |
| CdrwnCodeProfiler | Static class for providing profile information on functions |
| CdrwnCompressionBuffer | Utility class for compressing data using the zlib library |
| ►CdrwnConfigurableModule | Interface for a configurable module |
| CdrwnADLPInferenceConfig | |
| CdrwnBoostedClassifierConfig | |
| CdrwnCodeProfilerConfig | |
| CdrwnCompositeClassifierConfig | |
| CdrwnConfusionMatrixConfig | |
| CdrwnDecisionTreeConfig | |
| CdrwnGaussianConfig | |
| CdrwnGaussianMixtureConfig | |
| CdrwnGrabCutConfig | |
| CdrwnHOGFeaturesConfig | |
| CdrwnImageCacheConfig | |
| CdrwnImageInPainterConfig | |
| CdrwnImagePyramidCacheConfig | |
| CdrwnKMeansConfig | |
| CdrwnLinearRegressorConfig | |
| CdrwnLoggerConfig | |
| CdrwnMAPInferenceConfig | |
| CdrwnMaskedPatchMatchConfig | |
| CdrwnMultiClassLogisticConfig | |
| CdrwnMultiSegConfig | Manages configuration settings for multiple image segmentation |
| CdrwnNNGraphConfig | |
| CdrwnNNGraphLearnerConfig | |
| CdrwnOpenCVUtilsConfig | |
| CdrwnPartsModelConfig | |
| CdrwnPatchMatchConfig | |
| CdrwnRandomForestConfig | |
| CdrwnSegImagePixelFeaturesConfig | |
| CdrwnSegImageRegionFeaturesConfig | |
| CdrwnThreadPoolConfig | |
| CdrwnTRWSInferenceConfig | |
| CdrwnXMLUtilsConfig | |
| CdrwnConfigurationManager | Configuration manager |
| CdrwnConfusionMatrix | Utility class for computing and printing confusion matrices |
| CdrwnDataset< XType, YType, WType > | Implements a cacheable dataset containing feature vectors, labels and optional weights |
| CdrwnDisjointSets | Implements a forest of disjoint sets abstract data type |
| ►CdrwnFactorOperation | Base class for implementing various operations on table factors. The derived classes store mappings between factor entries making them very fast for iterative algorithms |
| CdrwnFactorAtomicOp | Executes an atomic operation by executing a sequence of factor operations |
| ►CdrwnFactorBinaryOp | Base class for implementing binary factor operations |
| CdrwnFactorDivideOp | Divide one factor by another |
| CdrwnFactorSubtractOp | Subtract one factor from another |
| CdrwnFactorWeightedSumOp | Add a weighted combination of factors |
| CdrwnFactorCopyOp | Copy one factor onto another |
| CdrwnFactorExpAndNormalizeOp | Exponentiate and normalize all the entries in a factor to sum to one |
| CdrwnFactorLogNormalizeOp | Shift all the entries in a factor so that the maximum is zero |
| CdrwnFactorMarginalizeOp | Marginalize out one or more variables in a factor |
| CdrwnFactorMaximizeOp | Maximize over one or more variables in a factor |
| CdrwnFactorMinimizeOp | Minimize over one or more variables in a factor |
| ►CdrwnFactorNAryOp | Base class for implementing n-ary factor operations |
| CdrwnFactorAdditionOp | Add two or more factors together |
| CdrwnFactorProductOp | Multiply two or more factors together |
| CdrwnFactorNormalizeOp | Normalize all the entries in a factor to sum to one. Assumes non-negative entries |
| CdrwnFactorReduceOp | Reduce factor by oberving the value of one or more variables |
| ►CdrwnFactorUnaryOp | Base class for implementing unary factor operations |
| CdrwnFactorMinusEqualsOp | Subtract one (weighted) factor from another inline |
| CdrwnFactorPlusEqualsOp | Add one (weighted) factor to another inline |
| CdrwnFactorTimesEqualsOp | Multiply one factor by another inline |
| CdrwnFactory< U > | Templated factory for creating or cloning objects for a particular base class |
| CdrwnFactoryAutoRegister< U, T > | Helper class for registering classes with a drwnFactory |
| CdrwnFactoryTraits< T > | Some classes may provide default factory registration (e.g., built-in classes such as drwnClassifier and drwnFeatureTransform) |
| CdrwnFactoryTraits< drwnClassifier > | Implements factory for classes derived from drwnClassifier with automatic registration of built-in classes |
| CdrwnFactoryTraits< drwnFeatureTransform > | Implements factory for classes derived from drwnFeatureTransform with automatic registration of built-in classes |
| CdrwnFilterBankResponse | Holds the results of running an image through a bank of filters and allows for computation of features over rectangular regions |
| ►CdrwnGrabCutInstance | Implements the grabCut algorithm of Rother et al., SIGGRAPH 2004 for figure/ground segmentation |
| CdrwnGrabCutInstanceGMM | |
| CdrwnGrabCutInstanceHistogram | |
| CdrwnHistogram< TYPE > | Implements a simple templated histogram class |
| CdrwnHOGFeatures | Encapsulates histogram-of-gradient (HOG) feature computation |
| CdrwnIconFactory | |
| CdrwnImageCache | Caches images in memory up to a maximum number of images or memory limit |
| CdrwnImageInPainter | Performs exemplar-based image inpainting |
| CdrwnImagePyramidCache | Caches image pyramids in main memory up to a maximum number of images or memory limit |
| CdrwnIndexQueue | Provides a queue datastructure on a fixed number of indexes. At most one copy of each index can appear in the queue (a second enqueue is ignored). Membership of the queue can be queried |
| ►CdrwnInference | Interface for various (marginal) inference algorithms |
| ►CdrwnMessagePassingInference | Implements generic message-passing algorithms on factor graphs. See derived classes for specific algorithms |
| ►CdrwnSumProdInference | Implements sum-product inference |
| CdrwnAsyncSumProdInference | Implements asynchronous sum-product inference |
| CdrwnLBPFilterBank | Implements filter bank for encoding local binary patterns |
| CdrwnLogger | Message and error logging. This class is not thread-safe in the interest of not having to flush the log on every message |
| CdrwnLPSolver | Solves equality constrained linear programs with positivity constraints via the log-barrier method |
| ►CdrwnMAPInference | Interface for various MAP inference (energy minimization) algorithms |
| CdrwnADLPInference | Implements the alternating direction method algorithm described in "An Alternating Direction Method for Dual MAP LP Relaxation," Ofer Meshi and Amir Globerson, ECML, 2011 |
| CdrwnAlphaBetaSwapInference | Implements alpha-beta swap inference using graph-cuts (see Boykov et al, 2001). Factor graphs must be pairwise |
| CdrwnAlphaExpansionInference | Implements alpha-expansion inference using graph-cuts (see Boykov et al, 2001). Factor graphs must be pairwise |
| CdrwnDualDecompositionInference | Implements dual decomposition MAP inference (see Komodakis and Paragios, CVPR 2009 and works cited therein). Each factor is treated as a separate slave |
| CdrwnICMInference | Implements iterated conditional modes (ICM) MAP inference. This method was first proposed in Besag, Royal Stats Society, 1986 |
| CdrwnJunctionTreeInference | Implements the junction tree algorithm for exact inference on a factor graph using drwnAsyncMaxProdInference for the actual message passing |
| ►CdrwnMessagePassingMAPInference | Implements generic message-passing algorithms on factor graphs. See derived classes for specific algorithms |
| CdrwnAsyncMaxProdInference | Implements asynchronous max-product (min-sum) inference |
| ►CdrwnGEMPLPInference | Implements the generalized LP-based message passing algorithm of Globerson and Jaakkola, NIPS 2007 |
| CdrwnSontag08Inference | Implements the incremental tightening of the LP MAP inference algorithm from Sontag et al., UAI 2008 |
| CdrwnMaxProdInference | Implements max-product inference |
| CdrwnTRWSInference | Implements the sequential tree-reweighted message passing (TRW-S) algorithm described in "Convergent Tree-Reweighted Message Passing for Energy
Minimization," Kolmogorov, IEEE PAMI, 2006 |
| CdrwnMAPInferenceFactory | Factory for creating drwnMAPInference objects |
| CdrwnMaskedPatchMatch | Implements the basic PatchMatch algorithm of Barnes et al., SIGGRAPH 2009 on masked images |
| ►CdrwnMaxFlow | Interface for maxflow/min-cut algorithms (for minimizing submodular quadratic pseudo-Boolean functions) |
| CdrwnBKMaxFlow | Implementation of Boykov and Kolmogorov's maxflow algorithm |
| CdrwnEdmondsKarpMaxFlow | Implementation of Edmonds-Karp maxflow/min-cut algorithm |
| CdrwnMouseState | Mouse state and mouse callback for populating the mouse state. Used by the drwnWaitMouse function |
| CdrwnMultiSegRegionDefinitions | Provides a mechanism for mapping region IDs to colours and class names. Can be initialized from an XML configuration file or programmatically for a number of standard datasets |
| CdrwnNNGraph | Class for maintaining a nearest neighbour graph over superpixel images. Search moves are implemented by templated functions in the drwnNNGraphMoves namespace |
| CdrwnNNGraphDefaultMetric | Implements the scoring functions needed by the search moves. Required member functions are isMatchable(), isFinite() and score() |
| CdrwnNNGraphEdge | Encapsulates an outgoing edge in a drwnNNGraph |
| CdrwnNNGraphImageData | Holds image, segments and other housekeeping information for an image |
| CdrwnNNGraphLabelsEqualMetric | |
| CdrwnNNGraphLabelsEqualMetricNoUnknown | |
| CdrwnNNGraphLabelsNotEqualMetric | |
| CdrwnNNGraphLabelsNotEqualMetricNoUnknown | |
| ►CdrwnNNGraphLearner | Learn the distance metric base class with full set of constraints (i.e., loss function over all targets and imposters) |
| CdrwnNNGraphLLearner | Learn the distance metric M = LL^T as L^T |
| CdrwnNNGraphMLearner | |
| ►CdrwnNNGraphSparseLearner | Learn the distance metric base class with sparse set of constraints (i.e., loss function over further target and nearest imposter only) |
| CdrwnNNGraphLSparseLearner | |
| CdrwnNNGraphNodeAnnotation< T > | Templated utility class for holding annotations for every node in a graph. See learning code for example use |
| CdrwnNNGraphNodeAnnotation< drwnNNGraphLearnViolatedConstraints > | |
| CdrwnNNGraphNodeAnnotation< drwnTriplet > | |
| CdrwnNNGraphNodeIndex | |
| CdrwnObject | Encapsulates a 2D object in an image for object detection |
| ►CdrwnOptimizer | Interface for solving large-scale unconstrained optimization problems using L-BFGS |
| CdrwnLinearRegressorBase | Common functionality for drwnLinearRegressor |
| CdrwnMultiClassLogisticBase | Common functionality for drwnMultiClassLogistic |
| CdrwnOrderedMap< KeyType, ValueType > | Provides a datastructure for that can be indexed by a KeyType (usually a string) or unsigned integer, i.e., the index |
| CdrwnPartsAssignment | Class for holding as assignment to part locations and occlusion variables |
| CdrwnPartsInference | Helper class for running inference in a (constellation) parts-based model. Supports linear and quadratic distance transforms for deformation costs. Computes argmax_{x,c,z} m_i(x_i, z_i) + d_i(x_i, c) + p(c) where m_i(x, 0) = matchingCost_i(x) + priorCost_i(x) m_i(x, 1) = occlusionCost + priorCost_i(x) d_i(x, c) = [dx, dy]^T fabs(x - o - c) + [dx2, dy2]^T (x - o - c).^2 |
| CdrwnPartsModelMixture< T > | Mixture of parts models (T must have a parts model interface). Inference returns the best scoring model and its parts locations |
| CdrwnPatchMatchEdge | Represents an edge in the drwnPatchMatchGraph |
| CdrwnPatchMatchGraph | Each image maintains a W-by-H-by-K array of match records referencing the (approximate) best K matches to other images. Image filename rather than the images themselves are stored. Duplicate filenames (images) are not allowed |
| CdrwnPatchMatchGraphLearner | Learns a PatchMatchGraph by iteratively performing search moves over the space of matches |
| CdrwnPatchMatchGraphRepaint | Class for repainting an image from matches within the PatchMatchGraph |
| CdrwnPatchMatchImageRecord | Records matches for one level in an image pyramid |
| CdrwnPatchMatchNode | Represents a node in the drwnPatchMatchGraph |
| CdrwnPersistentBlock | Persistent storage block used internally by drwnPersistentStorage |
| ►CdrwnPersistentRecord | Interface class for drwnPersistentStorage |
| CdrwnNNGraphImage | Holds nodes (superpixels) for a single image |
| CdrwnNNGraphNode | Encapsulates a superpixel node in a drwnNNGraph |
| CdrwnPatchMatchImagePyramid | Record of patch matches for mutliple levels each image |
| CdrwnPersistentVectorRecord< T > | Templated class for storing vector records |
| CdrwnPersistentVectorVectorRecord< T > | Templated class for storing vector-of-vector records |
| CdrwnSuperpixelContainer | Holds multiple oversegmentations for a given image |
| CdrwnPersistentStorage | Provides indexed storage for multiple records using two files (a binary data file and a text index file) |
| CdrwnPersistentStorageBuffer< T > | Provides buffered storage (delayed write-back) of objects with a drwnPersistentRecord interface |
| ►CdrwnPixelSegCRFInference | Alpha-expansion inference for a pixel-level CRF model with unary, contrast-dependent pairwise, and custom higher-order terms |
| CdrwnRobustPottsCRFInference | And where 0.0 < < 0.5 |
| CdrwnWeightedRobustPottsCRFInference | And where 0.0 < < 0.5, W_c = w_i |
| ►CdrwnProperties | Provides an abstract interface for dynamic properties |
| CdrwnClassifier | Implements the interface for a generic machine learning classifier |
| CdrwnFeatureTransform | Implements the interface for a generic feature transforms possibly with learned parameters, e.g., PCA (unsupervised) or LDA (supervised) |
| CdrwnMultiSegConfig | Manages configuration settings for multiple image segmentation |
| CdrwnPropertiesCopy | |
| CdrwnRegression | Implements the interface for a generic machine learning regression, e.g. see drwnLinearRegressor |
| ►CdrwnPropertyInterface | |
| CdrwnStoragePropertyInterface< T > | |
| ►CdrwnStoragePropertyInterface< bool > | |
| CdrwnBooleanProperty | |
| ►CdrwnStoragePropertyInterface< double > | |
| ►CdrwnDoubleProperty | |
| CdrwnDoubleRangeProperty | |
| ►CdrwnStoragePropertyInterface< Eigen::MatrixXd > | |
| CdrwnMatrixProperty | |
| ►CdrwnStoragePropertyInterface< Eigen::VectorXd > | |
| CdrwnVectorProperty | |
| ►CdrwnStoragePropertyInterface< int > | |
| ►CdrwnIntegerProperty | |
| CdrwnRangeProperty | |
| CdrwnSelectionProperty | |
| ►CdrwnStoragePropertyInterface< list< string > > | |
| CdrwnListProperty | |
| ►CdrwnStoragePropertyInterface< string > | |
| ►CdrwnStringProperty | |
| CdrwnDirectoryProperty | |
| CdrwnFilenameProperty | |
| ►CdrwnQPSolver | Quadratic program solver |
| CdrwnLogBarrierQPSolver | |
| CdrwnSegImageInstance | Encapsulates a single instance of an image for multi-class pixel labeling problems (i.e., image segmentation) |
| CdrwnSLICCentroid | |
| CdrwnSmartPointer< T > | Implements a shared pointer interface to avoid the need to deep copy constant (shared) objects |
| CdrwnSmartPointer< drwnVarUniverse > | |
| CdrwnSmartPointerCmpLessThan< T > | Comparison operator for objects held in a drwnSmartPointer |
| CdrwnSparseLPSolver | Solves linear programs with sparse equality constraints |
| CdrwnSparseVec< T > | Quick-and-dirty sparse vector class as a plugin replacement for std::vector |
| CdrwnTableFactorMapping | Creates a mapping between entries in two tables |
| CdrwnTableFactorStorage | Shared memory for table factors |
| CdrwnTemplateMatcher | Utility class for computing multiple template matches |
| CdrwnTextonFilterBank | Implements a 17-dimensional filter bank |
| ►CdrwnThreadJob | Interface for a thread job functor |
| CdrwnDecisionTreeThread | |
| CdrwnGaussianMixtureThread | |
| CdrwnNNGraphAppendImageJob | |
| CdrwnNNGraphDataMatrixThread | |
| CdrwnNNGraphMoveUpdateThread< DistanceMetric > | |
| CdrwnNNGraphProjectFeaturesThread | |
| CdrwnNNGraphSparseSubGradientThread | |
| CdrwnNNGraphSubGradientThread | |
| CdrwnNNGraphThreadedMoves::drwnNNGraphThreadedExhaustiveJob< DistanceMetric > | Threading exhaustive functor |
| CdrwnNNGraphThreadedMoves::drwnNNGraphThreadedInitializeJob< DistanceMetric > | Threading initialize functor |
| CdrwnNNGraphThreadedMoves::drwnNNGraphThreadedRescoreJob< DistanceMetric > | Threading rescore functor |
| CdrwnNNGraphThreadedMoves::drwnNNGraphThreadedUpdateJob< DistanceMetric > | Threading update functor |
| CdrwnPatchMatchThreadedInitialize | |
| CdrwnPatchMatchThreadedUpdate | |
| CGridSearchJob | |
| CInferenceThread | |
| ClabelTransferJob | |
| CloadDataJob | |
| CloadDataJob | |
| CdrwnThreadPool | Implements a pool of threads for running concurrent jobs |
| CdrwnTriplet< T, U, V > | Basic datatype for holding three objects of arbitrary type. Similar to the STL pair<> class |
| ►CdrwnTypeable | Interface for an object that returns its own type as a string |
| CdrwnFeatureMap | Defines the interface for a feature mapping |
| CdrwnJointFeatureMap | Defines the interface for a joint feature mapping |
| ►CdrwnWriteable | Interface for objects that can serialize and de-serialize themselves |
| CdrwnCrossValidator | Utility class for cross-validating classifier meta-parameters by brute-force testing of all combinations of some given settings |
| CdrwnObjectList | List of objects for the same image (see drwnObject) |
| CdrwnObjectSequence | Sequence of images, each with a list of objects (see drwnObjectList) |
| CdrwnPixelSegModel | Implements a pixel-level CRF model for multi-class image segmentation (pixel labeling) |
| CdrwnStdObjIface | Standard Darwin object interface (cloneable and writeable) |
| CdrwnWeightedEdge | |
| CdrwnWeightedPixelEdge | Weighted undirected arc between pixels in an image |
| CdrwnSparseVec< T >::iterator | |
| CdrwnTableFactorMapping::iterator | Iterator for indexing entries in the tables |
| CdrwnXMLUtils::load_node< T > | Helper class for loading xml nodes into containers of objects |
| CdrwnXMLUtils::load_node< T * > | Helper class for (creating and) loading xml nodes into containers of pointers to objects |
| ►Cmap | |
| CdrwnObjectSequence | Sequence of images, each with a list of objects (see drwnObjectList) |
| CdrwnPartialAssignment | Defines an assignment to a subset of the variables |
| CdrwnSparseVec< T >::reference | |
| CdrwnXMLUtils::save_node< T > | Helper class for saving objects to xml nodes |
| CdrwnXMLUtils::save_node< T * > | Helper class for saving pointers to objects to xml nodes |
| ►Cvector | |
| CdrwnObjectList | List of objects for the same image (see drwnObject) |
| ►CwxApp | |
| CDarwinApp | |
| ►CwxDialog | |
| CdrwnMatrixEditor | |
| CdrwnOptionsEditor | |
| CdrwnTextEditor | |
| ►CwxFrame | |
| CMainWindow | |
| ►CwxScrolledWindow | |
| CMainCanvas | |
| ►CwxStatusBar | |
| CdrwnStatusBar | |
| ►CwxWindow | |
| CdrwnProgressBar | |
