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Todo List
Member drwnAsyncMaxProdInference::decodeBeliefs (drwnFullAssignment &mapAssignment)
improve this? consisted decoding?
Member drwnColourHistogram::accumulate (unsigned char red, unsigned char green, unsigned char blue)
interpolate between 8 neighbouring bins
Member drwnCreateHeatMap (const cv::Mat &m, drwnColorMap cm=DRWN_COLORMAP_RAINBOW)
refactor to use drwnCreateHeatMap(m, vector<CvScalar>) variant
Member drwnDataset< XType, YType, WType >::subSample (int sampleRate, bool bBalanced=false)

consider sample weighting

enforce balanced for discrete targets only

Member drwnDualDecompositionInference::inference (drwnFullAssignment &mapAssignment)
change for min-marginal subgradient
Class drwnFactor

move into own file.

allow for more efficient storage of higher-order factors

move to a templated design

Class drwnFactorGraph
template on a particular factor type (could be all table factors, for example, or generic drwnFactors)
Member drwnFactorGraphUtils::removeUniformFactors (drwnFactorGraph &graph)
reduce factors where variable cardinality is one
Member drwnFactorReduceOp::execute ()
speed up
Member drwnGEMPLPInference::inference (drwnFullAssignment &mapAssignment)
add this to computation graph
Member drwnHistogram< TYPE >::mergeWeightedHistogram (const drwnHistogram &H, double w)
needs to be extended
Class drwnInference
derive from drwnStdObjectIface to allow factory creation
Member drwnLPSolver::solve ()
solve with blockwise elimination
Class drwnMAPInference
derive from drwnStdObjectIface to allow factory creation
Class drwnMAPInferenceFactory
use drwnFactory templated class (but then drwnMAPInference needs to be default constructable)
Member drwnMaskedPatchMatch::visualize () const
Add modifySourceImage and modifyTargetImage functions based on a mask
Member drwnMaxProdInference::decodeBeliefs (drwnFullAssignment &mapAssignment)
improve this? consisted decoding?
Member drwnMergeSuperpixels (const cv::Mat &img, cv::Mat &seg, unsigned maxSegs)
look for "best matching" neighbour to merge with
Member drwnNNGraphMoves::rescore (drwnNNGraph &graph, unsigned imgIndx, const DistanceMetric &M)
delete the node
Class drwnNNGraphMoveUpdateThread< DistanceMetric >
features are replicated for positive and negative nn graphs which is a memory waste. Try refactor with features external.
Member drwnPatchMatchGraphLearner::appendCIELabFeatures (const cv::Mat &img, cv::Mat &features, int nChannel=0) const
move these elsewhere
Member drwnPatchMatchGraphLearner::cacheImageFeatures ()
allow a pixel feature generator to be provided caches features for all image pyramids
Member drwnPatchMatchGraphLearner::initialize ()
cache image features here instead of in constructor?
Class drwnPatchMatchImagePyramid
make cacheable so drwnPatchMatchGraph can handle thousands of images
Class drwnPersistentStorageBuffer< T >
rename to drwnPersistentRecordBuffer
Class drwnPixelSegCRFInference
add custom addAuxiliaryTerms function
Member drwnPixelSegModel::cacheBoostedPixelResponses (drwnSegImageInstance &instance) const
refactor using persistent storage class
Class drwnProperties
Could also store within property interface object (which would make for slower access within class members, but easier declaration of lots of properties).
Member drwnQPSolver::lineSearchGeneral (const VectorXd &x, const VectorXd &dx, const VectorXd &nu, const VectorXd &dnu) const
Member drwnQPSolver::solveSimplex ()
implement specialized simplex solver
Member drwnSLICSuperpixels (const cv::Mat &img, unsigned nClusters, double spatialWeight=200.0, double threshold=1.0e-3)
replace with separate x and y grid sizes
Class drwnSparseLPSolver
refactor drwnLPSolver code to use templated sparse/dense matrix
Member drwnSparseLPSolver::solve ()
solve with blockwise elimination
Class drwnTableFactor
drwnSparseTableFactor class and drwnPottsFactor class
Class drwnTableFactorMapping

create a multi-table mapping (will reduce amount of repeated computation in iterators)

specialize for unary and pairwise cases

Member drwnTableFactorMapping::drwnTableFactorMapping (const vector< int > &dstVars, const vector< int > &srcVars, const drwnVarUniversePtr &pUniverse)
group variables as above if appear contiguously in src and dst
Member drwnTLinearRegressor< FeatureMap >::getRegression (const vector< double > &features) const
define feature mapping functions in terms of Eigen::VectorXd
Member drwnTMultiClassLogistic< FeatureMap >::getClassScores (const vector< double > &features, vector< double > &outputScores) const
define feature mapping functions in terms of Eigen::VectorXd