A B C D E F G H I J K L M N O P Q R S T U V W

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sampleIndex - Variable in class dimensionality_reduction.PCA

counts the number of currently loaded samples

SampleLocalFeatures - Class in codebook_generation

This class is used for creating samples of local features (in csv format) which can then be used for learning visual vocabularies.

SampleLocalFeatures() - Constructor for class codebook_generation.SampleLocalFeatures

sampleSize - Variable in class dimensionality_reduction.PCA

number of elements in each sample

samplesX - Variable in class utilities.boofcv_extensions.DescribePointSurfModNormalization

samplesY - Variable in class utilities.boofcv_extensions.DescribePointSurfModNormalization

sampleToEigenSpace(double[]) - Method in class dimensionality_reduction.PCA

Converts a vector from sample space into eigen space.

sampleWidth - Variable in class utilities.boofcv_extensions.DescribePointSurfModNormalization

scanForPlugins() - Static method in class utilities.ImageIOGreyScale

Scans for plug-ins on the application class path, loads their service provider classes, and registers a service provider instance for each one found with the IIORegistry.

serialVersionUID - Static variable in class codebook_generation.SimpleKMeansWithOutput

for serialization.

setBasisMatrix(double[][]) - Method in class dimensionality_reduction.PCA

Sets the PCA basis matrix.

setCacheDirectory(File) - Method in class utilities.ImageIOGreyScale.CacheInfo

setCacheDirectory(File) - Static method in class utilities.ImageIOGreyScale

Sets the directory where cache files are to be created.

setCoarseQuantizer(double[][]) - Method in class data_structures.IVFADC

With this method we initialize the table holding the coarse quantizers.

setCompact(boolean) - Method in class dimensionality_reduction.PCA

setDescriptorDatasetFormat(String) - Method in class feature_filtering.EntropyBasedFiltering

setDescriptorLength(int) - Method in class vector_aggregation.AbstractFeatureAggregator

setDiscretizationFilter(String) - Method in class feature_filtering.EntropyBasedFiltering

setDisplayStdDevs(boolean) - Method in class codebook_generation.SimpleKMeansWithOutput

Sets whether standard deviations and nominal count.

setDistanceFunction(DistanceFunction) - Method in class codebook_generation.SimpleKMeansWithOutput

sets the distance function to use for instance comparison.

setDontReplaceMissingValues(boolean) - Method in class codebook_generation.SimpleKMeansWithOutput

Sets whether missing values are to be replaced.

setDoWhitening(boolean) - Method in class dimensionality_reduction.PCA

setEigenvalues(double[]) - Method in class dimensionality_reduction.PCA

Initializes the diagonal eigenvalue matrix PCA.W by using the supplied vector and then whitens the projection matrix PCA.V_t

setExternalId(String) - Method in class utilities.Result

setFastDistanceCalc(boolean) - Method in class codebook_generation.SimpleKMeansWithOutput

Sets whether to use faster distance calculation.

setHasPermission(Boolean) - Method in class utilities.ImageIOGreyScale.CacheInfo

setImage(II) - Method in class utilities.boofcv_extensions.DescribePointSurfNormalization

setIndexSearchTime(long) - Method in class utilities.Answer

setInitializeUsingKMeansPlusPlusMethod(boolean) - Method in class codebook_generation.SimpleKMeansWithOutput

Set whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm (rather than the standard random selection of initial cluster centers).

setInternalId(int) - Method in class utilities.Result

setL2Normalization(boolean) - Method in class feature_extraction.DescriptorExtractor

setMaxIterations(int) - Method in class codebook_generation.SimpleKMeansWithOutput

set the maximum number of iterations to be executed.

setMean(double[]) - Method in class dimensionality_reduction.PCA

setNameLookupTime(long) - Method in class utilities.Answer

setNumCentroids(int) - Method in class vector_aggregation.AbstractFeatureAggregator

setNumClusters(int) - Method in class codebook_generation.SimpleKMeansWithOutput

set the number of clusters to generate.

setNumExecutionSlots(int) - Method in class codebook_generation.SimpleKMeansWithOutput

Set the degree of parallelism to use.

setOptions(String[]) - Method in class codebook_generation.SimpleKMeansWithOutput

Parses a given list of options.

setPCAFromFile(String) - Method in class dimensionality_reduction.PCA

Initializes the PCA matrix, means vector and optionally eigenvalues matrix from the given file.

setPercentiles(String) - Method in class vector_aggregation.VladAggregatorWithFiltering

This method sets the percentile values used by the dist methdod.

setPowerNormalization(boolean) - Method in class feature_extraction.DescriptorExtractor

setPreserveInstancesOrder(boolean) - Method in class codebook_generation.SimpleKMeansWithOutput

Sets whether order of instances must be preserved.

setProductQuantizer(double[][][]) - Method in class data_structures.IVFADC

With this method we initialize the table holding the product quantizer.

setResults(Result[]) - Method in class utilities.Answer

setScaleSpace(SiftImageScaleSpace) - Method in class utilities.boofcv_extensions.DescribePointSiftNormalization

setUseCache(boolean) - Method in class utilities.ImageIOGreyScale.CacheInfo

setUseCache(boolean) - Static method in class utilities.ImageIOGreyScale

Sets a flag indicating whether a disk-based cache file should be used when creating ImageInputStreams and ImageOutputStreams.

setW(int) - Method in class data_structures.IVFADC

sift(int, int, int) - Static method in class utilities.boofcv_extensions.FactoryDescribePointAlgsNormalization

sift(int, float, boolean, int) - Static method in class utilities.boofcv_extensions.FactoryDetectDescribeNormalization

Creates a new SIFT feature and describer.

sift(double, int, int, boolean, int, float, int, double, int) - Static method in class utilities.boofcv_extensions.FactoryDetectDescribeNormalization

Creates a new SIFT feature detector and describer.

SIFTExtractor - Class in feature_extraction

This class used the BOOFCV library for extracting SIFT features.

SIFTExtractor() - Constructor for class feature_extraction.SIFTExtractor

SIFTExtractor(boolean) - Constructor for class feature_extraction.SIFTExtractor

SIFTLength - Static variable in class feature_extraction.DescriptorExtractor

sigmaToRadius - Variable in class utilities.boofcv_extensions.DescribePointSiftNormalization

SimpleKMeansWithOutput - Class in codebook_generation

Cluster data using the k means algorithm.

SimpleKMeansWithOutput() - Constructor for class codebook_generation.SimpleKMeansWithOutput

the default constructor.

SimpleKMeansWithOutput.KMeansClusterTask - Class in codebook_generation

SimpleKMeansWithOutput.KMeansClusterTask(Instances, int, int, int[]) - Constructor for class codebook_generation.SimpleKMeansWithOutput.KMeansClusterTask

SimpleKMeansWithOutput.KMeansComputeCentroidTask - Class in codebook_generation

SimpleKMeansWithOutput.KMeansComputeCentroidTask(int, Instances) - Constructor for class codebook_generation.SimpleKMeansWithOutput.KMeansComputeCentroidTask

squareScaling(BufferedImage) - Method in class feature_extraction.ImageScaling

This method returns a scaled instance of the provided BufferedImage.

squareScaling(String) - Method in class feature_extraction.ImageScaling

Same as ImageScaling.squareScaling(BufferedImage) but takes the imageFileName instead of a BufferedImage.

ss - Variable in class utilities.boofcv_extensions.DescribePointSiftNormalization

ss - Variable in class utilities.boofcv_extensions.DetectDescribeSiftNormalization

startExecutorPool() - Method in class codebook_generation.SimpleKMeansWithOutput

Start the pool of execution threads

subVectorLength - Variable in class data_structures.ADC

The length of each subvector.

subVectorLength - Variable in class data_structures.IVFADC

The length of each subvector of the residual vectors.

SumVsMeanAggregationData - Class in experimental_data_creation

This class can be used to generate the datasets of sum vs mean aggregation experiment.

SumVsMeanAggregationData() - Constructor for class experimental_data_creation.SumVsMeanAggregationData

surf(Class<T>) - Static method in class utilities.boofcv_extensions.FactoryDescribePointAlgsNormalization

surf(float, int, int, int, int, int, int, boolean, Class<T>) - Static method in class utilities.boofcv_extensions.FactoryDetectDescribeNormalization

Creates a SURF descriptor.

SURFExtractor - Class in feature_extraction

This class implements SURF feature extraction using the BoofCV library.

SURFExtractor() - Constructor for class feature_extraction.SURFExtractor

SURFExtractor(int, int, boolean) - Constructor for class feature_extraction.SURFExtractor

SURFLength - Static variable in class feature_extraction.DescriptorExtractor

SURForSIFTExtractionExample - Class in feature_extraction

Extracts SURF or SIFT features from images contained in a folder and writes a .surf(b) or .sift(b) file for each image.

SURForSIFTExtractionExample() - Constructor for class feature_extraction.SURForSIFTExtractionExample

syncRate - Variable in class data_structures.AbstractSearchStructure

The rate at which the disk-based indexed should be updated.