Classes | |
| class | Bitmap |
| class | Clusteror |
| Base class for objects that cluster feature vectors. More... | |
| class | ClusterorFactory |
| class | Color64 |
| class | Computor |
| Base class for feature computors. More... | |
| class | FeatureDefinition |
| class | FeatureParameter |
| class | FeatureTable |
| A table with features. More... | |
| class | FeatureTableResult |
| class | FeatureTableSet |
| class | Grid |
| class | HarrisLaplaceDetector |
| class | InterestPointFeature |
| class | IntWeibullNgbPnLoop |
| Neighbourhood functor for weibull fitting. More... | |
| class | FISTDescriptor |
| class | LabelSet |
| class | PointDescriptorTable |
| A table with points and their descriptors. More... | |
| class | RadiusClusteror |
| Radius based clustering of feature vectors using similarity. More... | |
| class | RandomTree |
| Representation of a random tree (a decision tree that maps a feature vector to a code word). More... | |
| class | TestFunctions |
| This class tests some feature functionality. More... | |
| class | TestMakeRandomTree |
| class | TestRandomTree |
| class | VirtualFeatureTable |
| class | VirtualFeatureTableFactory |
| class | VirtualFeatureTableIOBufferReader |
| class | VirtualFeatureTableIOBufferWriter |
| class | VirtualFeatureTableMemory |
| class | VisSem |
| class | WeibullNgbPnLoop |
| Neighbourhood functor for weibull fitting. More... | |
| class | WeightedFeatureList |
Typedefs | |
| typedef std::vector< String > | FeatureList |
| typedef Table::TableTem< Column::ColumnTem< Quid >, Vector::VectorSet< Array::Array2dScalarReal64 > > | FeatureTableBaseType |
| typedef Table::TableTem< Vector::ColumnVectorSet, Column::ColumnInt32 > | AnnotatedFeatureTable |
| typedef Table::TableTem< Column::ColumnTem< Real64 >, Column::ColumnTem< Real64 >, Column::ColumnTem< Real64 >, Column::ColumnTem< Real64 >, Column::ColumnTem< Real64 >, Vector::VectorSet< Array::Array2dScalarReal64 > > | PointDescriptorTableBaseType |
| typedef std::vector< RandomTree * > | RandomForest |
| typedef Table::TableTem< Column::ColumnInt32, Column::ColumnInt32, Column::ColumnReal64 > | RandomTreeTable |
Functions | |
| ILOG_VAR_INIT (ClusterorFactory, Impala.Core.Feature) | |
| ILOG_VAR_INIT (Computor, Impala.Core.Feature) | |
| void | AddDSurfOptions (CmdOptions &options) |
| void | GetDSurfOptions (CmdOptions &options, int &haarSize, int &subregionSize, int &spatiality) |
| void | ExtractFeatureIntoVector (Real64 *vector, std::vector< Array::Array2dScalarReal64 * > &features, int x, int y, int spatiality) |
| subroutine of DSurf - extract from the collection of images 'features'; at the point (x,y) a feature that we will store in vector 'v' x and y are assumed to be in range of the image considering spatiality | |
| Matrix::Mat * | DSurf (Array::Array2dScalarReal64 *image, PointDescriptorTable *pointData, int sampleRate, int numberHaarSum, int spatiality) |
| image in, descriptors are added to pointList | |
| void | DumpFeatureTableHistogram (FeatureTable *table, FeatureDefinition fdef, int id) |
| ILOG_VAR_INIT (FeatureTable, Impala.Core.Feature) | |
| ILOG_VAR_INIT (FeatureTableResult, Impala.Core.Feature) | |
| ILOG_VAR_INIT (FeatureTableSet, Impala.Core.Feature) | |
| void | Normalize (Array::Array2dScalarReal64 *im) |
| std::vector< Array::Array2dScalarReal64 * > | GetColorChannels (Array::Array2dVec3Real64 *input, String colorModel) |
| std::vector< Array::Array2dScalarReal64 * > | GetColorChannels (Array::Array2dVec3UInt8 *inputInt, String colorModel) |
| void | HaarFirstLine (std::vector< Array::Array2dScalarReal64 * > &response, Array::Array2dScalarReal64 *image, int y) |
| void | HaarSecondLine (std::vector< Array::Array2dScalarReal64 * > &response, Array::Array2dScalarReal64 *image, int y) |
| std::vector< Array::Array2dScalarReal64 * > | HaarResponse (Array::Array2dScalarReal64 *image) |
returns a vector of four images:
| |
| ILOG_VAR_INIT (HarrisLaplaceDetector, Core.Feature) | |
| ILOG_VAR_INIT (InterestPointFeature, Core.Feature) | |
| bool | StringIsFullyNumeric (String s) |
| void | CalculateFISTDescriptors (Array::Array2dVec3UInt8 *inputNoBorder, PointDescriptorTable *pointData, String descriptor) |
| template<class SrcArrayT> | |
| PointDescriptorTable * | LaplacianDetection (SrcArrayT *imIntensity, bool useRecGauss, Real64 precision) |
| template<class SrcArrayT> | |
| PointDescriptorTable * | LaplacianDetector (SrcArrayT *input, bool useRecGauss=true, Real64 precision=3.0) |
| void | Dump (AnnotatedFeatureTable *t, std::ostream &os) |
| Histogram::Histogram1dTem< int > * | MakeHistogram (const AnnotatedFeatureTable *data, int nrClasses, bool *filter) |
| makes a historgram of the values found in the second column of data (the column with the 'class id') considering the filter. | |
| double | Gain (const AnnotatedFeatureTable *data, int nrClasses, bool *left, bool *right) |
| according to Jasper: The Gain is defined as in [Shotton08], taken again from Lepetit, CVPR 2005 | |
| void | SplitSet (bool *&left, bool *&right, int dimension, double value, const AnnotatedFeatureTable *data, bool *filter) |
| returns filters for left and right through ref to pointer (pointer is set to a new address, so if either of these point to data when this function is called it will introduce a memory leak). | |
| void | TryRandomSplit (int &dimension, double &value, double &gain, const AnnotatedFeatureTable *data, bool *filter, int nrClasses, Util::Random &rng) |
| Take a random split point and return it and it's gain in the first three reference parameters. | |
| void | FindSplit (int &dimension, double &value, const AnnotatedFeatureTable *data, bool *filter, int nrClasses, int nrTrials, Util::Random &rng) |
| Find a split by trying random splits and returning the one with the maximum gain. | |
| int | GetCodeWord () |
| returns a new id every time the function is called. | |
| Feature::RandomTree * | MakeRandomTree (const AnnotatedFeatureTable *data, bool *filter, int nrClasses, int maxDepth, int nrTrials, Util::Random &rng) |
| recursive funtion finds a split and calls itself for the 'left' and 'right' splits | |
| RandomTree * | MakeRandomTree (const AnnotatedFeatureTable *data, int nrClasses, int maxDepth, int nrTrials, Util::Random &rng) |
| void | Dump (Matrix::Mat *matrix) |
| Vector::VectorTem< Real64 > | MarkovStationaryFeature (Matrix::Mat *cooccuranceMatrix) |
| Vector::VectorTem< Real64 > | MarkovStationaryFeatureTimed (Matrix::Mat *m) |
| void | testMSF () |
| void | WriteBINDESC1ToBuffer (Util::IOBuffer *buffer, Matrix::Mat *points, Matrix::Mat *descriptors, String theDataType) |
| void | WriteKOEN1ToBuffer (Util::IOBuffer *buffer, Matrix::Mat *points, Matrix::Mat *descriptors, String theDataType) |
| String | ReadBINDESC1FromBuffer (Matrix::Mat *&points, Matrix::Mat *&descriptors, Util::IOBuffer *buffer, bool readPoints, bool readDescriptors) |
| ILOG_VAR_INIT (PointDescriptorTable, Impala.Core.Feature) | |
| RandomForest | ReadRandomForest (RandomTreeTable *table) |
| RandomForest | ReadRandomForest (FeatureTable *table) |
| int | GetCodebookLength (FeatureTable *table) |
| void | DeleteForest (RandomForest forest) |
| Or should we make this the method of a class RandomForest? | |
| ILOG_VAR_INIT (RandomTree, Impala.Core.Feature) | |
| void | Write (RandomTree *tree, RandomTreeTable *table) |
| Writes the tree to the end of the table. | |
| RandomTree * | Read (RandomTreeTable *table, int &index) |
| Call with valid table and the index of the position in the tree where the tree starts (0 for example). | |
| RandomTreeTable * | MakeRandomTreeTable (FeatureTable *ft) |
| FeatureTable * | MakeFeatureTable (RandomTreeTable *rtt) |
| FeatureTable * | ReadCodebookFromBuffer (Util::IOBuffer *buf) |
| Array::Array2dScalarReal64 * | CreateCircleMask (int patchSize) |
| void | ComputeRegionDescriptor (std::vector< Real64 > &vec, Array::Array2dVec3UInt8 *src, String descriptor, bool useCircularMask, int srcCenterX, int srcCenterY) |
| void | MakePatch (Array::Array2dVec3UInt8 *&patch, Array::Array2dVec3UInt8 *img, int x, int y, int pixelsAround) |
| void | CalculateRegionDescriptors (Array::Array2dVec3UInt8 *inputNoBorder, PointDescriptorTable *pointData, String descriptor) |
| void | CalculateSurfDescriptors (Array::Array2dVec3UInt8 *inputNoBorder, PointDescriptorTable *pointData, String descriptor, int haarSize, int components, int spatiality) |
| this code is analogous to CalculateFIST2Descriptors | |
| ILOG_VAR_INIT (TestFunctions, Impala.Core.Feature) | |
| CPPUNIT_TEST_SUITE_REGISTRATION (TestFunctions) | |
| ILOG_CLASS_INIT (TestMakeRandomTree, Impala.Core.Feature) | |
| CPPUNIT_TEST_SUITE_REGISTRATION (TestMakeRandomTree) | |
| ILOG_CLASS_INIT (TestRandomTree, Impala.Core.Feature) | |
| CPPUNIT_TEST_SUITE_REGISTRATION (TestRandomTree) | |
| ILOG_VAR_INIT (VirtualFeatureTable, Impala.Core.Feature) | |
| ILOG_VAR_INIT (VirtualFeatureTableFactory, Impala.Core.Feature) | |
| ILOG_VAR_INIT (VirtualFeatureTableIOBufferReader, Impala.Core.Feature) | |
| ILOG_VAR_INIT (VirtualFeatureTableIOBufferWriter, Impala.Core.Feature) | |
| ILOG_VAR_INIT (VirtualFeatureTableMemory, Impala.Core.Feature) | |
| ILOG_VAR_INIT (VisSem, Impala.Core.Feature) | |
| Vector::VectorSet< Core::Array::Array2dScalarReal64 > * | Weibull (Core::Array::Array2dVec3UInt8 *im, Real64 sigma) |
| Vector::VectorSet< Core::Array::Array2dScalarReal64 > * | WeibullIRGB (Core::Array::Array2dVec3UInt8 *im, Real64 sigma, bool doMu=false, bool doAnderson=false, bool doArjan=false) |
| ILOG_VAR_INIT (WeightedFeatureList, Impala.Core.Feature) | |
| void | Read (WeightedFeatureList *featList, Util::IOBuffer *buf) |
| bool | Read (WeightedFeatureList *featList, Persistency::File file) |
| bool | Write (WeightedFeatureList *featList, Util::IOBuffer *buf) |
| bool | Write (WeightedFeatureList *featList, Persistency::File file) |
Variables | |
| const Quid | cInvalidCodeWord = 0x8000000000000000LL |
1.5.1