A unique id identifying the settings and algorithms used to generate the output result. The id shall stay consistent throughout restarts of the jvm.

Apply the kernel to the 2d array. If the desired output is a int[][] array refer to {@link #applyInt(int[][])}. @param input the input array to apply

Merge a hash into this object and lazily update the values as required. A merge operation looks at every individual bit and increments or decrements t

Create a dummy hash @param bits @param algoId @return

@return the hash resolution in bits

Calculate hashes for the given images. Invoking the hash function on the same image has to return the same hash value. A comparison of the hashes rela

Merge a hash into this object and lazily update the values as required. A merge operation looks at every individual bit and increments or decrements t

Calculate the hamming distance of 2 hash values. The distance of two hashes is the difference of the individual bits found in the hash. <p> The normal

Calculate the normalized weighted distance between the supplied hash and this hash. Opposed to the hamming distance the weighted distance takes parti

Adds the hashes produced by the added hashing algorithm of this image to the filter. Future calls to {@link #isPresent(File)} will return true for the

Calculate the hamming distance of 2 hash values. The distance of two hashes is the difference of the individual bits found in the hash. <p> The hammin

Return the algorithm identifier specifying by which algorithm and setting this hash was created. The id shall remain constant. @return The algorithm

Get the current category of the image described by this unique id. A category usually maps an image to a cluster. @param uniqueId the id of a previou

Execute all supplied hashing algorithms in the order they were supplied and check if the images are similar @param image First input image @param im

@return the threshold

Gets the weighted distance of this bit in the range [0-1] @param bitIndex the position in the hash @param bit if true the distance to a 1 bit wi

Return an immutable map copy of the algorithms currently used in the matcher. This map is a hard copy of the hashmap and does not updated if the under

Return all elements of the tree whose hamming distance is smaller or equal than the supplied max distance. If the tree is configured to ensureHashCon

@return the base BigInteger holding the hash value

Get the actual bit key resolution of all hashes computed by this algorithm. <p> Be aware that this value may differ from: <ul> <li>the supplied bit

Some hashing algorithms need a greater or lower key resolution. Allow individual test to override this. @return

Add a zero bit to the hash

Added hashing algorithms will be queried to seed the internal rng. If not enough hashing algorithms are provided by the user exists algorithms will be

Return the array index to compute the kernel value @param pIndex The pixel index of the array @param widthOrHeight the width or height of the

@return an unmofifiable collection of the hashing algorithms added to this matcher.

Add a one bit to the hash

Add the image to the matcher allowing the image to be found in future searches. @param image The image whose hash will be added to the matcher

Ensure that the hashValue is up to date. Call this method when ever the value is needed.

Calculate the hamming distance of 2 hash values. The distance of two hashes is the difference of the individual bits found in the hash. <p> The normal

Compute the closest category of an image and afterwards add it to the internal categorization queue. Some matchers may choose to immediately update th

Drop all data in the tables and delete the database files. This method currently only supports h2 databases. After this method terminates successfully

Map a hashing algorithm to a table name @param hashAlgo The hashing algorithm @return the table name to identify the table used to save hashes produc

Convert the internal state of the hashbuilder to a big integer object @return a big integer object

Add test images to this image matcher which will be used to construct the random forest. <p> Be aware that labeled images are kept in memory as long

Apply the kernel to the 2d array with each value casted to a int value. @param input the input array to apply the kernel on @return a new array creat

Check if an entry with the given uniqueId already exists @param uniqueId the unique id to check against @param hashAlgo the hashing algorithm @return

Ensure that the distance array is up to date. Call this method when ever the array is needed.

Apply the filter to the input image and return an altered copy @param bi the input image to apply the filter on @return the altered image

Return a mask indicating if the bits are above a certain uncertainty. <p> The certainty of a hash is calculated by comparing the number of 0 bits at p

@return if the normalized hamming distance shall be used.

@return the name

@return the bImage

Calculate the hamming distance of 2 hash values. The distance of two hashes is the difference of the individual bits found in the hash. <p> The hammin