Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 12]

►NdrwnNNGraphThreadedMoves	Templated drwnNNGraph search moves (multi-threaded)
CdrwnNNGraphThreadedExhaustiveJob	Threading exhaustive functor
CdrwnNNGraphThreadedInitializeJob	Threading initialize functor
CdrwnNNGraphThreadedRescoreJob	Threading rescore functor
CdrwnNNGraphThreadedUpdateJob	Threading update functor
►NdrwnXMLUtils
Cload_node	Helper class for loading xml nodes into containers of objects
Cload_node< T * >	Helper class for (creating and) loading xml nodes into containers of pointers to objects
Csave_node	Helper class for saving objects to xml nodes
Csave_node< T * >	Helper class for saving pointers to objects to xml nodes
CDarwinApp
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
CdrwnADLPInferenceConfig
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
CdrwnAsyncMaxProdInference	Implements asynchronous max-product (min-sum) inference
CdrwnAsyncSumProdInference	Implements asynchronous sum-product inference
CdrwnBiasFeatureMap	Augments input feature vector with 1 (i.e., to allow for a bias weight)
CdrwnBiasJointFeatureMap	Same as drwnIdentityJointFeatureMap but adds a bias term for each class i.e., $\phi(x, y) = \left(\delta\!\left\{y = 0\right\} (x^T, 1), \ldots, \delta\!\left\{y = K - 2\right\} (x^T, 1)\right) \in \mathbb{R}^{(K - 1)(n + 1)}$
CdrwnBitArray	Implements an efficient packed array of bits
CdrwnBKMaxFlow	Implementation of Boykov and Kolmogorov's maxflow algorithm
CdrwnBooleanProperty
CdrwnBoostedClassifier	Implements a mult-class boosted decision-tree classifier. See Zhu et al., Multi-class AdaBoost, 2006
CdrwnBoostedClassifierConfig
CdrwnClassificationResults	Encapsulates summary of classifier output from which various curves can be generated (e.g., precision-recall curves)
CdrwnClassifier	Implements the interface for a generic machine learning classifier
CdrwnCloneable	Interface for cloning object (i.e., virtual copy constructor)
CdrwnCodeProfiler	Static class for providing profile information on functions
CdrwnCodeProfilerConfig
CdrwnColourHistogram	Specialized histogram for quantized 3-channel colour values (e.g., RGB)
CdrwnCompositeClassifier	Implements a multi-class classifier by combining binary classifiers
CdrwnCompositeClassifierConfig
CdrwnCompressionBuffer	Utility class for compressing data using the zlib library
CdrwnConditionalGaussian	Utility class for generating conditonal gaussian distribution
CdrwnCondSuffStats	Implements a class for accumulating conditional first- and second-order sufficient statistics
CdrwnConfigurableModule	Interface for a configurable module
CdrwnConfigurationManager	Configuration manager
CdrwnConfusionMatrix	Utility class for computing and printing confusion matrices
CdrwnConfusionMatrixConfig
CdrwnCrossValidator	Utility class for cross-validating classifier meta-parameters by brute-force testing of all combinations of some given settings
CdrwnDataset	Implements a cacheable dataset containing feature vectors, labels and optional weights
CdrwnDecisionTree	Implements a (binary-split) decision tree classifier of arbitrary depth
CdrwnDecisionTreeConfig
CdrwnDecisionTreeThread
CdrwnDeformationCost	Structure for holding dx, dy, dx^2 and dy^2 deformation costs
CdrwnDirectoryProperty
►CdrwnDisjointSets	Implements a forest of disjoint sets abstract data type
C_node_t
CdrwnDoubleProperty
CdrwnDoubleRangeProperty
CdrwnDualDecompositionInference	Implements dual decomposition MAP inference (see Komodakis and Paragios, CVPR 2009 and works cited therein). Each factor is treated as a separate slave
CdrwnEdmondsKarpMaxFlow	Implementation of Edmonds-Karp maxflow/min-cut algorithm
CdrwnFactor	Generic interface for a factor. Currently only inherited by drwnTableFactor
CdrwnFactorAdditionOp	Add two or more factors together
CdrwnFactorAtomicOp	Executes an atomic operation by executing a sequence of factor operations
CdrwnFactorBinaryOp	Base class for implementing binary factor operations
CdrwnFactorCopyOp	Copy one factor onto another
CdrwnFactorDivideOp	Divide one factor by another
CdrwnFactorExpAndNormalizeOp	Exponentiate and normalize all the entries in a factor to sum to one
CdrwnFactorGraph	Container and utility functions for factor graphs
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
CdrwnFactorMinusEqualsOp	Subtract one (weighted) factor from another inline
CdrwnFactorNAryOp	Base class for implementing n-ary factor operations
CdrwnFactorNormalizeOp	Normalize all the entries in a factor to sum to one. Assumes non-negative entries
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
CdrwnFactorPlusEqualsOp	Add one (weighted) factor to another inline
CdrwnFactorProductOp	Multiply two or more factors together
CdrwnFactorReduceOp	Reduce factor by oberving the value of one or more variables
CdrwnFactorSubtractOp	Subtract one factor from another
CdrwnFactorTimesEqualsOp	Multiply one factor by another inline
CdrwnFactorUnaryOp	Base class for implementing unary factor operations
CdrwnFactorWeightedSumOp	Add a weighted combination of factors
CdrwnFactory	Templated factory for creating or cloning objects for a particular base class
CdrwnFactoryAutoRegister	Helper class for registering classes with a drwnFactory
CdrwnFactoryTraits	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
CdrwnFeatureMap	Defines the interface for a feature mapping $\phi : \mathbb{R}^n \rightarrow \mathbb{R}^m$
CdrwnFeatureTransform	Implements the interface for a generic feature transforms possibly with learned parameters, e.g., PCA (unsupervised) or LDA (supervised)
CdrwnFeatureWhitener	Whitens (zero mean, unit variance) feature vector (see also drwnPCA)
CdrwnFilenameProperty
CdrwnFilterBankResponse	Holds the results of running an image through a bank of filters and allows for computation of features over rectangular regions
CdrwnFisherLDA	Fisher's linear discriminant analysis (LDA)
CdrwnGaussian	Implements a multi-variate gaussian distribution
CdrwnGaussianConfig
CdrwnGaussianMixture	Implements a multi-variant Gaussian mixture model
CdrwnGaussianMixtureConfig
CdrwnGaussianMixtureThread
CdrwnGEMPLPInference	Implements the generalized LP-based message passing algorithm of Globerson and Jaakkola, NIPS 2007
CdrwnGrabCutConfig
CdrwnGrabCutInstance	Implements the grabCut algorithm of Rother et al., SIGGRAPH 2004 for figure/ground segmentation
CdrwnGrabCutInstanceGMM
CdrwnGrabCutInstanceHistogram
CdrwnHistogram	Implements a simple templated histogram class
CdrwnHOGFeatures	Encapsulates histogram-of-gradient (HOG) feature computation
CdrwnHOGFeaturesConfig
CdrwnHOGPartsModel
CdrwnICMInference	Implements iterated conditional modes (ICM) MAP inference. This method was first proposed in Besag, Royal Stats Society, 1986
CdrwnIconFactory
CdrwnIdentityFeatureMap	Copies input feature space to output feature space
CdrwnIdentityJointFeatureMap	Includes a copy of each feature from the input space for each class other than the last, i.e., $\phi(x, y) = \left(\delta\!\left\{y = 0\right\} x^T, \ldots, \delta\!\left\{y = K - 2\right\}x^T\right) \in \mathbb{R}^{(K - 1) n}$ . This is the standard feature mapping for multi-class logistic models
CdrwnImageCache	Caches images in memory up to a maximum number of images or memory limit
CdrwnImageCacheConfig
CdrwnImageInPainter	Performs exemplar-based image inpainting
CdrwnImageInPainterConfig
CdrwnImagePyramidCache	Caches image pyramids in main memory up to a maximum number of images or memory limit
CdrwnImagePyramidCacheConfig
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
CdrwnIntegerProperty
CdrwnJointFeatureMap	Defines the interface for a joint feature mapping $\phi : \mathbb{R} \times \mathbb{Z} \rightarrow \mathbb{R}^m$
CdrwnJunctionTreeInference	Implements the junction tree algorithm for exact inference on a factor graph using drwnAsyncMaxProdInference for the actual message passing
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
CdrwnKMeansConfig
CdrwnLBPFilterBank	Implements filter bank for encoding local binary patterns
CdrwnLinearRegressorBase	Common functionality for drwnLinearRegressor
CdrwnLinearRegressorConfig
CdrwnLinearTransform	Implements a linear feature transform with externally settable parameters
CdrwnListProperty
CdrwnLogBarrierQPSolver
CdrwnLogger	Message and error logging. This class is not thread-safe in the interest of not having to flush the log on every message
CdrwnLoggerConfig
CdrwnLPSolver	Solves equality constrained linear programs with positivity constraints via the log-barrier method
CdrwnMAPInference	Interface for various MAP inference (energy minimization) algorithms
CdrwnMAPInferenceConfig
CdrwnMAPInferenceFactory	Factory for creating drwnMAPInference objects
CdrwnMaskedPatchMatch	Implements the basic PatchMatch algorithm of Barnes et al., SIGGRAPH 2009 on masked images
CdrwnMaskedPatchMatchConfig
CdrwnMatrixEditor
CdrwnMatrixProperty
CdrwnMaxFlow	Interface for maxflow/min-cut algorithms (for minimizing submodular quadratic pseudo-Boolean functions)
CdrwnMaxProdInference	Implements max-product inference
CdrwnMessagePassingInference	Implements generic message-passing algorithms on factor graphs. See derived classes for specific algorithms
CdrwnMessagePassingMAPInference	Implements generic message-passing algorithms on factor graphs. See derived classes for specific algorithms
CdrwnMouseState	Mouse state and mouse callback for populating the mouse state. Used by the drwnWaitMouse function
CdrwnMultiClassLogisticBase	Common functionality for drwnMultiClassLogistic
CdrwnMultiClassLogisticConfig
CdrwnMultiSegConfig	Manages configuration settings for multiple image segmentation
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
CdrwnNNGraphAppendImageJob
CdrwnNNGraphConfig
CdrwnNNGraphDataMatrixThread
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
CdrwnNNGraphImage	Holds nodes (superpixels) for a single image
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)
CdrwnNNGraphLearnerConfig
CdrwnNNGraphLLearner	Learn the distance metric M = LL^T as L^T
CdrwnNNGraphLSparseLearner
CdrwnNNGraphMLearner
CdrwnNNGraphMoveUpdateThread
CdrwnNNGraphNode	Encapsulates a superpixel node in a drwnNNGraph
CdrwnNNGraphNodeAnnotation	Templated utility class for holding annotations for every node in a graph. See learning code for example use
CdrwnNNGraphNodeIndex
CdrwnNNGraphProjectFeaturesThread
CdrwnNNGraphSortByImage
CdrwnNNGraphSortByScore
CdrwnNNGraphSparseLearner	Learn the distance metric base class with sparse set of constraints (i.e., loss function over further target and nearest imposter only)
CdrwnNNGraphSparseSubGradientThread
CdrwnNNGraphSubGradientThread
CdrwnObject	Encapsulates a 2D object in an image for object detection
CdrwnObjectList	List of objects for the same image (see drwnObject)
CdrwnObjectSequence	Sequence of images, each with a list of objects (see drwnObjectList)
CdrwnOpenCVUtilsConfig
CdrwnOptimizer	Interface for solving large-scale unconstrained optimization problems using L-BFGS
CdrwnOptionsEditor
CdrwnOrderedMap	Provides a datastructure for that can be indexed by a `KeyType` (usually a string) or unsigned integer, i.e., the index
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
CdrwnPartialAssignment	Defines an assignment to a subset of the variables
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
CdrwnPartsModel	Interface for implementing a part-based constellation model (i.e., pictorial structures model) for object detection
CdrwnPartsModelConfig
CdrwnPartsModelMixture	Mixture of parts models (T must have a parts model interface). Inference returns the best scoring model and its parts locations
CdrwnPatchMatchConfig
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
CdrwnPatchMatchImagePyramid	Record of patch matches for mutliple levels each image
CdrwnPatchMatchImageRecord	Records matches for one level in an image pyramid
CdrwnPatchMatchNode	Represents a node in the drwnPatchMatchGraph
CdrwnPatchMatchThreadedInitialize
CdrwnPatchMatchThreadedUpdate
CdrwnPCA	Principal component analysis feature transformation
CdrwnPersistentBlock	Persistent storage block used internally by drwnPersistentStorage
CdrwnPersistentRecord	Interface class for drwnPersistentStorage
CdrwnPersistentStorage	Provides indexed storage for multiple records using two files (a binary data file and a text index file)
►CdrwnPersistentStorageBuffer	Provides buffered storage (delayed write-back) of objects with a drwnPersistentRecord interface
C_drwnRecordEntry
CdrwnPersistentVectorRecord	Templated class for storing vector records
CdrwnPersistentVectorVectorRecord	Templated class for storing vector-of-vector records
CdrwnPixelNeighbourContrasts	Convenience class for holding pixel contrast weights
CdrwnPixelSegCRFInference	Alpha-expansion inference for a pixel-level CRF model with unary, contrast-dependent pairwise, and custom higher-order terms
CdrwnPixelSegModel	Implements a pixel-level CRF model for multi-class image segmentation (pixel labeling)
CdrwnPRCurve	Precision-recall curve
CdrwnProgressBar
CdrwnProperties	Provides an abstract interface for dynamic properties
CdrwnPropertiesCopy
CdrwnPropertyInterface
CdrwnQPSolver	Quadratic program solver
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)
CdrwnQuadraticJointFeatureMap	Same as drwnSquareJointFeatureMap but adds cross-terms
CdrwnRandomForest	Implements a Random forest ensemble of decision trees classifier. See L. Breiman, "Random Forests", Machine Learning, 2001
CdrwnRandomForestConfig
CdrwnRangeProperty
CdrwnRegression	Implements the interface for a generic machine learning regression, e.g. see drwnLinearRegressor
CdrwnRobustPottsCRFInference	And where 0.0 < < 0.5
CdrwnSegImageCompositePixelFeatures	Class for generating composite per-pixel feature vectors
CdrwnSegImageFilePixelFeatures	Pre-processed per-pixel features stored in files
CdrwnSegImageInstance	Encapsulates a single instance of an image for multi-class pixel labeling problems (i.e., image segmentation)
CdrwnSegImagePixelFeatures	Interface for generating per-pixel features for a drwnSegImageInstance object
CdrwnSegImagePixelFeaturesConfig
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
CdrwnSegImageRegionFeaturesConfig
CdrwnSegImageStdPixelFeatures	Standard per-pixel filterbank features with option to read auxiliary features from a file
CdrwnSegImageStdRegionFeatures	Standard per-region filterbank features computes mean and standard deviation of drwnTextonFilterBank responses over each region
CdrwnSelectionProperty
CdrwnSLICCentroid
CdrwnSmartPointer	Implements a shared pointer interface to avoid the need to deep copy constant (shared) objects
CdrwnSmartPointerCmpLessThan	Comparison operator for objects held in a drwnSmartPointer
CdrwnSontag08Inference	Implements the incremental tightening of the LP MAP inference algorithm from Sontag et al., UAI 2008
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)
CdrwnSparseLPSolver	Solves linear programs with sparse equality constraints
►CdrwnSparseVec	Quick-and-dirty sparse vector class as a plugin replacement for std::vector
Citerator
Creference
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)
CdrwnSquareJointFeatureMap	Same as drwnIdentityJointFeatureMap but adds a square term for each feature i.e., $\phi(x, y) = \left(\delta\!\left\{y = 0\right\} (x^T, \frac{1}{\sqrt{2}} diag(xx^T - I), 1), \ldots, \delta\!\left\{y = K - 2\right\} (x^T, \frac{1}{\sqrt{2}} diag(xx^T - I), 1)\right) \in \mathbb{R}^{(K - 1)(2n + 1)}$
CdrwnStatusBar
CdrwnStdObjIface	Standard Darwin object interface (cloneable and writeable)
CdrwnStoragePropertyInterface
CdrwnStringProperty
CdrwnSuffStats	Implements a class for accumulating first- and second-order sufficient statistics (moments)
CdrwnSumProdInference	Implements sum-product inference
CdrwnSuperpixelContainer	Holds multiple oversegmentations for a given image
CdrwnSupervisedTransform	Implements interface for supervised feature transforms (i.e., with class labels)
CdrwnTableFactor	Factor which stores the value of each assignment explicitly in table form
►CdrwnTableFactorMapping	Creates a mapping between entries in two tables
Citerator	Iterator for indexing entries in the tables
CdrwnTableFactorStorage	Shared memory for table factors
CdrwnTemplateMatcher	Utility class for computing multiple template matches
CdrwnTemplatePartsModel
CdrwnTextEditor
CdrwnTextonFilterBank	Implements a 17-dimensional filter bank
CdrwnTFeatureMapTransform	Helper feature transformation based on a drwnFeatureMap
CdrwnThreadJob	Interface for a thread job functor
CdrwnThreadPool	Implements a pool of threads for running concurrent jobs
CdrwnThreadPoolConfig
CdrwnTLinearRegressor	Implements linear regression optimization templated on a drwnFeatureMap
CdrwnTMultiClassLogistic	Implements a multi-class logistic classifier templated on a drwnJointFeatureMap
CdrwnTriplet	Basic datatype for holding three objects of arbitrary type. Similar to the STL pair<> class
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
CdrwnTRWSInferenceConfig
CdrwnTypeable	Interface for an object that returns its own type as a string
CdrwnUnsupervisedTransform	Implements interface for unsupervised feature transforms (i.e, without class labels)
CdrwnVarUniverse	Data structure for definining the random variables (name and cardinality) for a given problem instance
CdrwnVectorProperty
CdrwnWeightedEdge
CdrwnWeightedPixelEdge	Weighted undirected arc between pixels in an image
CdrwnWeightedRobustPottsCRFInference	And where 0.0 < < 0.5, W_c = w_i
CdrwnWriteable	Interface for objects that can serialize and de-serialize themselves
CdrwnXMLUtilsConfig
CGridSearchJob
CInferenceThread
ClabelTransferJob
CloadDataJob
CMainCanvas
CMainWindow