pub trait BinaryDescriptorMatcherTraitConst: AlgorithmTraitConst {
fn as_raw_BinaryDescriptorMatcher(&self) -> *const c_void;
fn match_(
&self,
query_descriptors: &Mat,
train_descriptors: &Mat,
matches: &mut Vector<DMatch>,
mask: &Mat
) -> Result<()> { ... }
fn knn_match(
&self,
query_descriptors: &Mat,
train_descriptors: &Mat,
matches: &mut Vector<Vector<DMatch>>,
k: i32,
mask: &Mat,
compact_result: bool
) -> Result<()> { ... }
fn radius_match(
&self,
query_descriptors: &Mat,
train_descriptors: &Mat,
matches: &mut Vector<Vector<DMatch>>,
max_distance: f32,
mask: &Mat,
compact_result: bool
) -> Result<()> { ... }
}Expand description
furnishes all functionalities for querying a dataset provided by user or internal to class (that user must, anyway, populate) on the model of @ref features2d_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 , each with length
or
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
bits. In particular, when
(where
is the Hamming norm), there must exist a substring k (with
) such that
That means that if Hamming distance between each of the m substring is strictly greater than
, then
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
, i-th hash table is
searched for entries distant at the most
from
and a set of
candidates
is obtained. The union of sets
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
, deleting the codes that are distant more that r from q.
Required Methods
fn as_raw_BinaryDescriptorMatcher(&self) -> *const c_void
Provided Methods
For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class
Parameters
- queryDescriptors: query descriptors
- trainDescriptors: dataset of descriptors furnished by user
- matches: vector to host retrieved matches
- mask: mask to select which input descriptors must be matched to one in dataset
C++ default parameters
- mask: Mat()
For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class
Parameters
- queryDescriptors: query descriptors
- trainDescriptors: dataset of descriptors furnished by user
- matches: vector to host retrieved matches
- k: number of the closest descriptors to be returned for every input query
- mask: mask to select which input descriptors must be matched to ones in dataset
- compactResult: flag 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)
C++ default parameters
- mask: Mat()
- compact_result: false
For every input query descriptor, retrieve, from a dataset provided from user or from the one internal to class, all the descriptors that are not further than maxDist from input query
Parameters
- queryDescriptors: query descriptors
- trainDescriptors: dataset of descriptors furnished by user
- matches: vector to host retrieved matches
- maxDistance: search radius
- mask: mask to select which input descriptors must be matched to ones in dataset
- compactResult: flag 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)
C++ default parameters
- mask: Mat()
- compact_result: false