Package boofcv.factory.feature.associate

Class FactoryAssociation

java.lang.Object

boofcv.factory.feature.associate.FactoryAssociation

public class FactoryAssociation
extends Object

Creates algorithms for associating TupleDesc_F64 features.

Constructor Summary

Constructors

Constructor	Description
FactoryAssociation()

Method Summary

Modifier and Type	Method	Description
static AssociateNearestNeighbor<TupleDesc_F64>	associateNearestNeighbor(@Nullable ConfigAssociateNearestNeighbor config, NearestNeighbor nn)
static <D> ScoreAssociation<D>	defaultScore(Class<D> tupleType)	Given a feature descriptor type it returns a "reasonable" default ScoreAssociation.
static <D> AssociateDescription<D>	ensureUnique(AssociateDescription<D> associate)	Checks and if necessary wraps the association to ensure that it returns only unique associations
static <D> AssociateDescription2D<D>	ensureUnique(AssociateDescription2D<D> associate)	Checks and if necessary wraps the association to ensure that it returns only unique associations
static <D extends TupleDesc<D>> AssociateDescription<D>	generic(ConfigAssociate config, DescriptorInfo<D> info)
static <D> AssociateDescription2D<D>	generic2(ConfigAssociate config, DescriptorInfo info)	Creates a generic association algorithm that uses descriptors as well as the 2D location of image features.
static <D> AssociateDescription<D>	greedy(@Nullable ConfigAssociateGreedy config, ScoreAssociation<D> score)	Returns an algorithm for associating features together which uses a brute force greedy algorithm.
static <D> AssociateDescription2D<D>	greedy2D(@Nullable ConfigAssociateGreedy config, ConfigLength maxDistance, ScoreAssociation<D> score)	Returns an algorithm for associating features together which uses a brute force greedy algorithm.
static AssociateDescription<TupleDesc_F64>	kdRandomForest(@Nullable ConfigAssociateNearestNeighbor configNN, int dimension, int numTrees, int numConsiderSplit, long randomSeed)	Approximate association using multiple random K-D trees (random forest) for descriptors with a high degree of freedom, e.g.
static AssociateDescription<TupleDesc_F64>	kdtree(@Nullable ConfigAssociateNearestNeighbor configNN, int dimension)	Approximate association using a K-D tree degree of moderate size (10-15) that uses a best-bin-first search order.
static <TD extends TupleDesc<TD>> KdTreeDistance<TD>	kdtreeDistance(int dof, Class<TD> type)
static <D> ScoreAssociation<D>	scoreEuclidean(Class<D> tupleType, boolean squared)	Scores features based on the Euclidean distance between them.
static <D> ScoreAssociation<D>	scoreHamming(Class<D> tupleType)	Hamming distance between two binary descriptors.
static ScoreAssociation<NccFeature>	scoreNcc()	Scores features based on their Normalized Cross-Correlation (NCC).
static <D> ScoreAssociation<D>	scoreSad(Class<D> tupleType)	Scores features based on Sum of Absolute Difference (SAD).

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

FactoryAssociation

public FactoryAssociation()

Method Details

generic

public static <D extends TupleDesc<D>>
AssociateDescription<D> generic(ConfigAssociate config,
 DescriptorInfo<D> info)

generic2

public static <D> AssociateDescription2D<D> generic2(ConfigAssociate config,
 DescriptorInfo info)

Creates a generic association algorithm that uses descriptors as well as the 2D location of image features.

ensureUnique

public static <D> AssociateDescription<D> ensureUnique(AssociateDescription<D> associate)

Checks and if necessary wraps the association to ensure that it returns only unique associations

ensureUnique

public static <D> AssociateDescription2D<D> ensureUnique(AssociateDescription2D<D> associate)

Checks and if necessary wraps the association to ensure that it returns only unique associations

greedy

public static <D> AssociateDescription<D> greedy(@Nullable
 @Nullable ConfigAssociateGreedy config,
 ScoreAssociation<D> score)

Returns an algorithm for associating features together which uses a brute force greedy algorithm. See AssociateGreedyDesc for details.

Type Parameters:

D - Data structure being associated

Parameters:

score - Computes the fit score between two features.

config - Configuration

Returns:

AssociateDescription

greedy2D

public static <D> AssociateDescription2D<D> greedy2D(@Nullable
 @Nullable ConfigAssociateGreedy config,
 ConfigLength maxDistance,
 ScoreAssociation<D> score)

Returns an algorithm for associating features together which uses a brute force greedy algorithm. See AssociateGreedyDesc for details.

Type Parameters:

D - Data structure being associated

Parameters:

score - Computes the fit score between two features.

config - Configuration

maxDistance - Specifies maximum distance allowed.

Returns:

AssociateDescription

kdtree

public static AssociateDescription<TupleDesc_F64> kdtree(@Nullable
 @Nullable ConfigAssociateNearestNeighbor configNN,
 int dimension)

Approximate association using a K-D tree degree of moderate size (10-15) that uses a best-bin-first search order.

Parameters:

dimension - Number of elements in the feature vector

Returns:

Association using approximate nearest neighbor

See Also:

• AssociateNearestNeighbor_ST
• KdTreeSearch1Bbf

kdRandomForest

public static AssociateDescription<TupleDesc_F64> kdRandomForest(@Nullable
 @Nullable ConfigAssociateNearestNeighbor configNN,
 int dimension,
 int numTrees,
 int numConsiderSplit,
 long randomSeed)

Approximate association using multiple random K-D trees (random forest) for descriptors with a high degree of freedom, e.g. > 20

Parameters:

dimension - Number of elements in the feature vector

numTrees - Number of trees that are considered. Try 10 and tune.

numConsiderSplit - Number of nodes that are considered when generating a tree. Must be less than the point's dimension. Try 5

randomSeed - Seed used by random number generator

Returns:

Association using approximate nearest neighbor

See Also:

• AssociateNearestNeighbor_ST
• KdForestBbfNearestNeighbor

kdtreeDistance

public static <TD extends TupleDesc<TD>>
KdTreeDistance<TD> kdtreeDistance(int dof,
 Class<TD> type)

associateNearestNeighbor

public static AssociateNearestNeighbor<TupleDesc_F64> associateNearestNeighbor(@Nullable
 @Nullable ConfigAssociateNearestNeighbor config,
 NearestNeighbor nn)

defaultScore

public static <D> ScoreAssociation<D> defaultScore(Class<D> tupleType)

Given a feature descriptor type it returns a "reasonable" default ScoreAssociation.

Parameters:

tupleType - Class type which extends TupleDesc

Returns:

A class which can score two potential associations

scoreSad

public static <D> ScoreAssociation<D> scoreSad(Class<D> tupleType)

Scores features based on Sum of Absolute Difference (SAD).

Parameters:

tupleType - Type of descriptor being scored

Returns:

SAD scorer

scoreNcc

public static ScoreAssociation<NccFeature> scoreNcc()

Scores features based on their Normalized Cross-Correlation (NCC).

Returns:

NCC score

scoreEuclidean

public static <D> ScoreAssociation<D> scoreEuclidean(Class<D> tupleType,
 boolean squared)

Scores features based on the Euclidean distance between them. The square is often used instead of the Euclidean distance since it is much faster to compute.

Parameters:

tupleType - Type of descriptor being scored

squared - IF true the distance squared is returned. Usually true

Returns:

Euclidean distance measure

scoreHamming

public static <D> ScoreAssociation<D> scoreHamming(Class<D> tupleType)

Hamming distance between two binary descriptors.

Parameters:

tupleType - Type of descriptor being scored

Returns:

Hamming distance measure