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

[detail level 12]

drwnFactorGraphUtils	Utility functions for factor graphs
drwnMatlabUtils	Utility functions for converting between Matlab and Darwin datatypes
drwnNNGraphMoves	Templated drwnNNGraph search moves
drwnNNGraphThreadedMoves	Templated drwnNNGraph search moves (multi-threaded)
drwnNNGraphThreadedInitializeJob	Threading initialize functor
drwnNNGraphThreadedRescoreJob	Threading rescore functor
drwnNNGraphThreadedUpdateJob	Threading update functor
drwnNNGraphThreadedExhaustiveJob	Threading exhaustive functor
drwnNNGraphVis	Visualization routines for a drwnNNGraph
drwnPatchMatchVis	Visualization routines
drwnXMLUtils
save_node	Helper class for saving objects to xml nodes
save_node< T * >	Helper class for saving pointers to objects to xml nodes
load_node	Helper class for loading xml nodes into containers of objects
load_node< T * >	Helper class for (creating and) loading xml nodes into containers of pointers to objects
DarwinApp
drwnADLPInference	Implements the alternating direction method algorithm described in "An Alternating Direction Method for Dual MAP LP Relaxation," Ofer Meshi and Amir Globerson, ECML, 2011
drwnADLPInferenceConfig
drwnAlphaBetaSwapInference	Implements alpha-beta swap inference using graph-cuts (see Boykov et al, 2001). Factor graphs must be pairwise
drwnAlphaExpansionInference	Implements alpha-expansion inference using graph-cuts (see Boykov et al, 2001). Factor graphs must be pairwise
drwnAsyncMaxProdInference	Implements asynchronous max-product (min-sum) inference
drwnAsyncSumProdInference	Implements asynchronous sum-product inference
drwnBiasFeatureMap	Augments input feature vector with 1 (i.e., to allow for a bias weight)
drwnBiasJointFeatureMap	Same as drwnIdentityJointFeatureMap but adds a bias term for each class i.e., $\phi(x, y) = \left(\delta\!\left\{y = 0\right\} (x^T, 1), \ldots, \delta\!\left\{y = K - 2\right\} (x^T, 1)\right) \in \mathbb{R}^{(K - 1)(n + 1)}$
drwnBitArray	Implements an efficient packed array of bits
drwnBKMaxFlow	Implementation of Boykov and Kolmogorov's maxflow algorithm
drwnBooleanProperty
drwnBoostedClassifier	Implements a mult-class boosted decision-tree classifier. See Zhu et al., Multi-class AdaBoost, 2006
drwnBoostedClassifierConfig
drwnClassificationResults	Encapsulates summary of classifier output from which various curves can be generated (e.g., precision-recall curves)
drwnClassifier	Implements the interface for a generic machine learning classifier
drwnCloneable	Interface for cloning object (i.e., virtual copy constructor)
drwnCodeProfiler	Static class for providing profile information on functions
drwnCodeProfilerConfig
drwnColourHistogram	Specialized histogram for quantized 3-channel colour values (e.g., RGB)
drwnCompositeClassifier	Implements a multi-class classifier by combining binary classifiers
drwnCompositeClassifierConfig
drwnCompressionBuffer	Utility class for compressing data using the zlib library
drwnConditionalGaussian	Utility class for generating conditonal gaussian distribution
drwnCondSuffStats	Implements a class for accumulating conditional first- and second-order sufficient statistics
drwnConfigurableModule	Interface for a configurable module
drwnConfigurationManager	Configuration manager
drwnConfusionMatrix	Utility class for computing and printing confusion matrices
drwnConfusionMatrixConfig
drwnCrossValidator	Utility class for cross-validating classifier meta-parameters by brute-force testing of all combinations of some given settings
drwnDataset	Implements a cacheable dataset containing feature vectors, labels and optional weights
drwnDecisionTree	Implements a (binary-split) decision tree classifier of arbitrary depth
drwnDecisionTreeConfig
drwnDecisionTreeThread
drwnDeformationCost	Structure for holding dx, dy, dx^2 and dy^2 deformation costs
drwnDirectoryProperty
drwnDisjointSets	Implements a forest of disjoint sets abstract data type
_node_t
drwnDoubleProperty
drwnDoubleRangeProperty
drwnDualDecompositionInference	Implements dual decomposition MAP inference (see Komodakis and Paragios, CVPR 2009 and works cited therein). Each factor is treated as a separate slave
drwnEdmondsKarpMaxFlow	Implementation of Edmonds-Karp maxflow/min-cut algorithm
drwnFactor	Generic interface for a factor. Currently only inherited by drwnTableFactor
drwnFactorAdditionOp	Add two or more factors together
drwnFactorAtomicOp	Executes an atomic operation by executing a sequence of factor operations
drwnFactorBinaryOp	Base class for implementing binary factor operations
drwnFactorCopyOp	Copy one factor onto another
drwnFactorDivideOp	Divide one factor by another
drwnFactorExpAndNormalizeOp	Exponentiate and normalize all the entries in a factor to sum to one
drwnFactorGraph	Container and utility functions for factor graphs
drwnFactorLogNormalizeOp	Shift all the entries in a factor so that the maximum is zero
drwnFactorMarginalizeOp	Marginalize out one or more variables in a factor
drwnFactorMaximizeOp	Maximize over one or more variables in a factor
drwnFactorMinimizeOp	Minimize over one or more variables in a factor
drwnFactorMinusEqualsOp	Subtract one (weighted) factor from another inline
drwnFactorNAryOp	Base class for implementing n-ary factor operations
drwnFactorNormalizeOp	Normalize all the entries in a factor to sum to one. Assumes non-negative entries
drwnFactorOperation	Base class for implementing various operations on table factors. The derived classes store mappings between factor entries making them very fast for iterative algorithms
drwnFactorPlusEqualsOp	Add one (weighted) factor to another inline
drwnFactorProductOp	Multiply two or more factors together
drwnFactorReduceOp	Reduce factor by oberving the value of one or more variables
drwnFactorSubtractOp	Subtract one factor from another
drwnFactorTimesEqualsOp	Multiply one factor by another inline
drwnFactorUnaryOp	Base class for implementing unary factor operations
drwnFactorWeightedSumOp	Add a weighted combination of factors
drwnFactory	Templated factory for creating or cloning objects for a particular base class
drwnFactoryAutoRegister	Helper class for registering classes with a drwnFactory
drwnFactoryTraits	Some classes may provide default factory registration (e.g., built-in classes such as drwnClassifier and drwnFeatureTransform)
drwnFactoryTraits< drwnClassifier >	Implements factory for classes derived from drwnClassifier with automatic registration of built-in classes
drwnFactoryTraits< drwnFeatureTransform >	Implements factory for classes derived from drwnFeatureTransform with automatic registration of built-in classes
drwnFeatureMap	Defines the interface for a feature mapping $\phi : \mathbb{R}^n \rightarrow \mathbb{R}^m$
drwnFeatureTransform	Implements the interface for a generic feature transforms possibly with learned parameters, e.g., PCA (unsupervised) or LDA (supervised)
drwnFeatureWhitener	Whitens (zero mean, unit variance) feature vector (see also drwnPCA)
drwnFilenameProperty
drwnFilterBankResponse	Holds the results of running an image through a bank of filters and allows for computation of features over rectangular regions
drwnFisherLDA	Fisher's linear discriminant analysis (LDA)
drwnGaussian	Implements a multi-variate gaussian distribution
drwnGaussianConfig
drwnGaussianMixture	Implements a multi-variant Gaussian mixture model
drwnGaussianMixtureConfig
drwnGaussianMixtureThread
drwnGEMPLPInference	Implements the generalized LP-based message passing algorithm of Globerson and Jaakkola, NIPS 2007
drwnGrabCutConfig
drwnGrabCutInstance	Implements the grabCut algorithm of Rother et al., SIGGRAPH 2004 for figure/ground segmentation
drwnGrabCutInstanceGMM
drwnGrabCutInstanceHistogram
drwnHistogram	Implements a simple templated histogram class
drwnHOGFeatures	Encapsulates histogram-of-gradient (HOG) feature computation
drwnHOGFeaturesConfig
drwnHOGPartsModel
drwnICMInference	Implements iterated conditional modes (ICM) MAP inference. This method was first proposed in Besag, Royal Stats Society, 1986
drwnIconFactory
drwnIdentityFeatureMap	Copies input feature space to output feature space
drwnIdentityJointFeatureMap	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
drwnImageCache	Caches images in memory up to a maximum number of images or memory limit
drwnImageCacheConfig
drwnImageInPainter	Performs exemplar-based image inpainting
drwnImageInPainterConfig
drwnImagePyramidCache	Caches image pyramids in main memory up to a maximum number of images or memory limit
drwnImagePyramidCacheConfig
drwnIndexQueue	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
drwnInference	Interface for various (marginal) inference algorithms
drwnIntegerProperty
drwnJointFeatureMap	Defines the interface for a joint feature mapping $\phi : \mathbb{R} \times \mathbb{Z} \rightarrow \mathbb{R}^m$
drwnJunctionTreeInference	Implements the junction tree algorithm for exact inference on a factor graph using drwnAsyncMaxProdInference for the actual message passing
drwnKMeans	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
drwnKMeansConfig
drwnLBPFilterBank	Implements filter bank for encoding local binary patterns
drwnLinearRegressorBase	Common functionality for drwnLinearRegressor
drwnLinearRegressorConfig
drwnLinearTransform	Implements a linear feature transform with externally settable parameters
drwnListProperty
drwnLogBarrierQPSolver
drwnLogger	Message and error logging. This class is not thread-safe in the interest of not having to flush the log on every message
drwnLoggerConfig
drwnLPSolver	Solves equality constrained linear programs with positivity constraints via the log-barrier method
drwnMAPInference	Interface for various MAP inference (energy minimization) algorithms
drwnMAPInferenceConfig
drwnMAPInferenceFactory	Factory for creating drwnMAPInference objects
drwnMaskedPatchMatch	Implements the basic PatchMatch algorithm of Barnes et al., SIGGRAPH 2009 on masked images
drwnMaskedPatchMatchConfig
drwnMatrixEditor
drwnMatrixProperty
drwnMaxFlow	Interface for maxflow/min-cut algorithms (for minimizing submodular quadratic pseudo-Boolean functions)
drwnMaxProdInference	Implements max-product inference
drwnMessagePassingInference	Implements generic message-passing algorithms on factor graphs. See derived classes for specific algorithms
drwnMessagePassingMAPInference	Implements generic message-passing algorithms on factor graphs. See derived classes for specific algorithms
drwnMouseState	Mouse state and mouse callback for populating the mouse state. Used by the drwnWaitMouse function
drwnMultiClassLogisticBase	Common functionality for drwnMultiClassLogistic
drwnMultiClassLogisticConfig
drwnMultiSegConfig	Manages configuration settings for multiple image segmentation
drwnMultiSegRegionDefinitions	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
drwnNNGraph	Class for maintaining a nearest neighbour graph over superpixel images. Search moves are implemented by templated functions in the drwnNNGraphMoves namespace
drwnNNGraphAppendImageJob
drwnNNGraphConfig
drwnNNGraphDataMatrixThread
drwnNNGraphDefaultMetric	Implements the scoring functions needed by the search moves. Required member functions are isMatchable(), isFinite() and score()
drwnNNGraphEdge	Encapsulates an outgoing edge in a drwnNNGraph
drwnNNGraphImage	Holds nodes (superpixels) for a single image
drwnNNGraphImageData	Holds image, segments and other housekeeping information for an image
drwnNNGraphLabelsEqualMetric
drwnNNGraphLabelsEqualMetricNoUnknown
drwnNNGraphLabelsNotEqualMetric
drwnNNGraphLabelsNotEqualMetricNoUnknown
drwnNNGraphLearner	Learn the distance metric base class with full set of constraints (i.e., loss function over all targets and imposters)
drwnNNGraphLearnerConfig
drwnNNGraphLLearner	Learn the distance metric M = LL^T as L^T
drwnNNGraphLSparseLearner
drwnNNGraphMLearner
drwnNNGraphMoveUpdateThread
drwnNNGraphNode	Encapsulates a superpixel node in a drwnNNGraph
drwnNNGraphNodeAnnotation	Templated utility class for holding annotations for every node in a graph. See learning code for example use
drwnNNGraphNodeIndex
drwnNNGraphProjectFeaturesThread
drwnNNGraphSortByImage
drwnNNGraphSortByScore
drwnNNGraphSparseLearner	Learn the distance metric base class with sparse set of constraints (i.e., loss function over further target and nearest imposter only)
drwnNNGraphSparseSubGradientThread
drwnNNGraphSubGradientThread
drwnObject	Encapsulates a 2D object in an image for object detection
drwnObjectList	List of objects for the same image (see drwnObject)
drwnObjectSequence	Sequence of images, each with a list of objects (see drwnObjectList)
drwnOpenCVUtilsConfig
drwnOptimizer	Interface for solving large-scale unconstrained optimization problems using L-BFGS
drwnOptionsEditor
drwnOrderedMap	Provides a datastructure for that can be indexed by a `KeyType` (usually a string) or unsigned integer, i.e., the index
drwnPart	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
drwnPartialAssignment	Defines an assignment to a subset of the variables
drwnPartsAssignment	Class for holding as assignment to part locations and occlusion variables
drwnPartsInference	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
drwnPartsModel	Interface for implementing a part-based constellation model (i.e., pictorial structures model) for object detection
drwnPartsModelConfig
drwnPartsModelMixture	Mixture of parts models (T must have a parts model interface). Inference returns the best scoring model and its parts locations
drwnPatchMatchConfig
drwnPatchMatchEdge	Represents an edge in the drwnPatchMatchGraph
drwnPatchMatchGraph	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
drwnPatchMatchGraphLearner	Learns a PatchMatchGraph by iteratively performing search moves over the space of matches
drwnPatchMatchGraphRepaint	Class for repainting an image from matches within the PatchMatchGraph
drwnPatchMatchImagePyramid	Record of patch matches for mutliple levels each image
drwnPatchMatchImageRecord	Records matches for one level in an image pyramid
drwnPatchMatchNode	Represents a node in the drwnPatchMatchGraph
drwnPatchMatchThreadedInitialize
drwnPatchMatchThreadedUpdate
drwnPCA	Principal component analysis feature transformation
drwnPersistentBlock	Persistent storage block used internally by drwnPersistentStorage
drwnPersistentRecord	Interface class for drwnPersistentStorage
drwnPersistentStorage	Provides indexed storage for multiple records using two files (a binary data file and a text index file)
drwnPersistentStorageBuffer	Provides buffered storage (delayed write-back) of objects with a drwnPersistentRecord interface
_drwnRecordEntry
drwnPersistentVectorRecord	Templated class for storing vector records
drwnPersistentVectorVectorRecord	Templated class for storing vector-of-vector records
drwnPixelNeighbourContrasts	Convenience class for holding pixel contrast weights
drwnPixelSegCRFInference	Alpha-expansion inference for a pixel-level CRF model with unary, contrast-dependent pairwise, and custom higher-order terms
drwnPixelSegModel	Implements a pixel-level CRF model for multi-class image segmentation (pixel labeling)
drwnPRCurve	Precision-recall curve
drwnProgressBar
drwnProperties	Provides an abstract interface for dynamic properties
drwnPropertiesCopy
drwnPropertyInterface
drwnQPSolver	Quadratic program solver
drwnQuadraticFeatureMap	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)
drwnQuadraticJointFeatureMap	Same as drwnSquareJointFeatureMap but adds cross-terms
drwnRandomForest	Implements a Random forest ensemble of decision trees classifier. See L. Breiman, "Random Forests", Machine Learning, 2001
drwnRandomForestConfig
drwnRangeProperty
drwnRegression	Implements the interface for a generic machine learning regression, e.g. see drwnLinearRegressor
drwnRobustPottsCRFInference	And where 0.0 < < 0.5
drwnSegImageCompositePixelFeatures	Class for generating composite per-pixel feature vectors
drwnSegImageFilePixelFeatures	Pre-processed per-pixel features stored in files
drwnSegImageInstance	Encapsulates a single instance of an image for multi-class pixel labeling problems (i.e., image segmentation)
drwnSegImagePixelFeatures	Interface for generating per-pixel features for a drwnSegImageInstance object
drwnSegImagePixelFeaturesConfig
drwnSegImageRegionFeatures	Interface for generating per-region (or per-superpixel) features for a drwnSegImageInstance object. The `superpixel` data member of the drwnSegImageInstance object must be populated
drwnSegImageRegionFeaturesConfig
drwnSegImageStdPixelFeatures	Standard per-pixel filterbank features with option to read auxiliary features from a file
drwnSegImageStdRegionFeatures	Standard per-region filterbank features computes mean and standard deviation of drwnTextonFilterBank responses over each region
drwnSelectionProperty
drwnSLICCentroid
drwnSmartPointer	Implements a shared pointer interface to avoid the need to deep copy constant (shared) objects
drwnSmartPointerCmpLessThan	Comparison operator for objects held in a drwnSmartPointer
drwnSontag08Inference	Implements the incremental tightening of the LP MAP inference algorithm from Sontag et al., UAI 2008
drwnSparseLPSolver	Solves linear programs with sparse equality constraints
drwnSparseVec	Quick-and-dirty sparse vector class as a plugin replacement for std::vector
iterator
reference
drwnSquareFeatureMap	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)
drwnSquareJointFeatureMap	Same as drwnIdentityJointFeatureMap but adds a square term for each feature i.e., $\phi(x, y) = \left(\delta\!\left\{y = 0\right\} (x^T, \frac{1}{\sqrt{2}} diag(xx^T - I), 1), \ldots, \delta\!\left\{y = K - 2\right\} (x^T, \frac{1}{\sqrt{2}} diag(xx^T - I), 1)\right) \in \mathbb{R}^{(K - 1)(2n + 1)}$
drwnStatusBar
drwnStdObjIface	Standard Darwin object interface (cloneable and writeable)
drwnStoragePropertyInterface
drwnStringProperty
drwnSuffStats	Implements a class for accumulating first- and second-order sufficient statistics (moments)
drwnSumProdInference	Implements sum-product inference
drwnSuperpixelContainer	Holds multiple oversegmentations for a given image
drwnSupervisedTransform	Implements interface for supervised feature transforms (i.e., with class labels)
drwnTableFactor	Factor which stores the value of each assignment explicitly in table form
drwnTableFactorMapping	Creates a mapping between entries in two tables
iterator	Iterator for indexing entries in the tables
drwnTableFactorStorage	Shared memory for table factors
drwnTemplateMatcher	Utility class for computing multiple template matches
drwnTemplatePartsModel
drwnTextEditor
drwnTextonFilterBank	Implements a 17-dimensional filter bank
drwnTFeatureMapTransform	Helper feature transformation based on a drwnFeatureMap
drwnThreadJob	Interface for a thread job functor
drwnThreadPool	Implements a pool of threads for running concurrent jobs
drwnThreadPoolConfig
drwnTLinearRegressor	Implements linear regression optimization templated on a drwnFeatureMap
drwnTMultiClassLogistic	Implements a multi-class logistic classifier templated on a drwnJointFeatureMap
drwnTriplet	Basic datatype for holding three objects of arbitrary type. Similar to the STL pair<> class
drwnTRWSInference	Implements the sequential tree-reweighted message passing (TRW-S) algorithm described in "Convergent Tree-Reweighted Message Passing for Energy Minimization," Kolmogorov, IEEE PAMI, 2006
drwnTRWSInferenceConfig
drwnTypeable	Interface for an object that returns its own type as a string
drwnUnsupervisedTransform	Implements interface for unsupervised feature transforms (i.e, without class labels)
drwnVarUniverse	Data structure for definining the random variables (name and cardinality) for a given problem instance
drwnVectorProperty
drwnWeightedEdge
drwnWeightedPixelEdge	Weighted undirected arc between pixels in an image
drwnWeightedRobustPottsCRFInference	And where 0.0 < < 0.5, W_c = w_i
drwnWriteable	Interface for objects that can serialize and de-serialize themselves
drwnXMLUtilsConfig
GridSearchJob
InferenceThread
labelTransferJob
loadDataJob
MainCanvas
MainWindow