line_descriptor_BinaryDescriptorMatcher
import { line_descriptor_BinaryDescriptorMatcher } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods. Inherits Algorithm.
furnishes all functionalities for querying a dataset provided by user or internal to
class (that user must, anyway, populate) on the model of features_match
Once descriptors have been extracted from an image (both they represent lines and points), it becomes interesting to be able to match a descriptor with another one extracted from a different image and representing the same line or point, seen from a differente perspective or on a different scale. In reaching such goal, the main headache is designing an efficient search algorithm to associate a query descriptor to one extracted from a dataset. In the following, a matching modality based on Multi-Index Hashing (MiHashing) will be described.
Multi-Index Hashing
The theory described in this section is based on [MIH] . Given a dataset populated with binary
codes, each code is indexed m times into m different hash tables, according to m substrings it
has been divided into. Thus, given a query code, all the entries close to it at least in one
substring are returned by search as neighbor candidates. Returned entries are then checked for
validity by verifying that their full codes are not distant (in Hamming space) more than r bits
from query code. In details, each binary code h composed of b bits is divided into m
disjoint substrings \mathbf{h}^{(1)}, ..., \mathbf{h}^{(m)}, each with length
\lfloor b/m \rfloor or \lceil b/m \rceil bits. Formally, when two codes h and g differ
by at the most r bits, in at the least one of their m substrings they differ by at the most
\lfloor r/m \rfloor bits. In particular, when ||\mathbf{h}-\mathbf{g}||_H \le r (where ||.||_H
is the Hamming norm), there must exist a substring k (with 1 \le k \le m) such that
||\mathbf{h}^{(k)} - \mathbf{g}^{(k)}||_H \le \left\lfloor \frac{r}{m} \right\rfloor .
That means that if Hamming distance between each of the m substring is strictly greater than
\lfloor r/m \rfloor, then ||\mathbf{h}-\mathbf{g}||_H must be larger that r and that is a
contradiction. If the codes in dataset are divided into m substrings, then m tables will be
built. Given a query q with substrings \{\mathbf{q}^{(i)}\}^m_{i=1}, i-th hash table is
searched for entries distant at the most \lfloor r/m \rfloor from \mathbf{q}^{(i)} and a set of
candidates \mathcal{N}_i(\mathbf{q}) is obtained. The union of sets
\mathcal{N}(\mathbf{q}) = \bigcup_i \mathcal{N}_i(\mathbf{q}) is a superset of the r-neighbors
of q. Then, last step of algorithm is computing the Hamming distance between q and each
element in \mathcal{N}(\mathbf{q}), deleting the codes that are distant more that r from q.
Constructors and members
static new
Constructor.
The BinaryDescriptorMatcher constructed is able to store and manage 256-bits long entries.
new(): line_descriptor_BinaryDescriptorMatcher;The line_descriptor_BinaryDescriptorMatcher result.
clone
Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.
clone(): this;The this result.
match
For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class
match(queryDescriptors: Mat, trainDescriptors: Mat, matches: DMatchVector, mask: Mat): void;2 available overloads
match(queryDescriptors: Mat, trainDescriptors: Mat, matches: DMatchVector): void;match(queryDescriptors: Mat, trainDescriptors: Mat, matches: DMatchVector, mask: Mat): void;queryDescriptorsquery descriptors
trainDescriptorsdataset of descriptors furnished by user
matchesOutput destination, filled by the native operation. vector to host retrieved matches
maskmask to select which input descriptors must be matched to one in dataset
matchQuery
For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class
matchQuery(queryDescriptors: Mat, matches: DMatchVector, masks: MatVector): void;2 available overloads
matchQuery(queryDescriptors: Mat, matches: DMatchVector): void;matchQuery(queryDescriptors: Mat, matches: DMatchVector, masks: MatVector): void;queryDescriptorsquery descriptors
matchesOutput destination, filled by the native operation. vector to host retrieved matches
masksvector of masks to select which input descriptors must be matched to one in dataset (the i-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to i-th image)
knnMatch
For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class
knnMatch(queryDescriptors: Mat, trainDescriptors: Mat, matches: DMatchVectorVector, k: number, mask: Mat, compactResult: boolean): void;3 available overloads
knnMatch(queryDescriptors: Mat, trainDescriptors: Mat, matches: DMatchVectorVector, k: number): void;knnMatch(queryDescriptors: Mat, trainDescriptors: Mat, matches: DMatchVectorVector, k: number, mask: Mat): void;knnMatch(queryDescriptors: Mat, trainDescriptors: Mat, matches: DMatchVectorVector, k: number, mask: Mat, compactResult: boolean): void;queryDescriptorsquery descriptors
trainDescriptorsdataset of descriptors furnished by user
matchesOutput destination, filled by the native operation. vector to host retrieved matches
knumber of the closest descriptors to be returned for every input query
maskmask to select which input descriptors must be matched to ones in dataset
compactResultflag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)
knnMatchQuery
For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class
knnMatchQuery(queryDescriptors: Mat, matches: DMatchVectorVector, k: number, masks: MatVector, compactResult: boolean): void;3 available overloads
knnMatchQuery(queryDescriptors: Mat, matches: DMatchVectorVector, k: number): void;knnMatchQuery(queryDescriptors: Mat, matches: DMatchVectorVector, k: number, masks: MatVector): void;knnMatchQuery(queryDescriptors: Mat, matches: DMatchVectorVector, k: number, masks: MatVector, compactResult: boolean): void;queryDescriptorsquery descriptors
matchesvector to host retrieved matches
knumber of the closest descriptors to be returned for every input query
masksvector of masks to select which input descriptors must be matched to ones in dataset (the i-th mask in vector indicates whether each input query can be matched with descriptors in dataset relative to i-th image)
compactResultflag to obtain a compact result (if true, a vector that doesn't contain any matches for a given query is not inserted in final result)
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