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The library of FLANN or Fast Library for Approximate Nearest Neighbors comprises a collection of algorithms that have been optimized for the search of fast nearest neighbor in huge datasets besides high dimensional aspects.

The Proposal of FLANN

Pao, the flat network comprising of an input layer besides an output layer to form arbitrarily complicated decision regions for guiding real-world applications has proposed FLANN. FLANN does generate output by stretching the inputs by non-linear orthogonal functions, such as Chebyshev polynomial. After this, the processing of the final output layer is done. Every input neuron does correspond to an element of the input vector.

Again, the output layer comprises an output neuron that computes the effort of software development in the form of a linear weighted outputs’ sum. The non-normal features of the datasets do lead FLANN to low-prediction accuracy as well as high-computational complexity. For alleviating these shortcomings, some proposed processes have been formulated and they exploit the finest characteristics of FLANN and ISO.

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Features of FLANN

FLANN works faster compared to BFMatcher for huge datasets. For a FLANN-based matcher, a person needs to pass a couple of dictionaries that specify the algorithm that needs to be used besides its connection parameters. The first one in this context is IndexParams. For different algorithms, the info needs to be passed is explained in the docs of FLANN. So, in the form of a summary for some algorithms, such as SURF, SIFT, etc. you must pass the following:

Index_params = dict (algorithm = FLANN_INDEX_KDTREE, trees = 5

When you are utilizing ORB, then you could pass the following and the commented values happen to be recommended according to the docs though it did not propose needed outcomes in a few cases.

The subsequent dictionary is named SearchParams that specifies how many times the trees present in the index must be traversed recursively. When there are higher values then they provide improved precision though takes more time too. So, when you wish to alter the value, you need to pass search_params = dict (checks=100).

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The Method of Using FLANN from C++

When you wish to utilize the full flexibility and power of a templated code, you need to utilize the bindings of C++. For using these bindings, you need to comprise the library header named fileflann.hpp or the libflanncpps.a.

The Process of Using FLANN from MATLAB

You can use the library of FLANN from MATLAB via some wrapper functions, like flann search, flann build index, flann load index, flann save index, flann free index, and flann set distance type.

The Method of Using FLANN from Python

A person can use FLANN from the programs of python utilizing the bindings of python with the library.

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