Module opencv::line_descriptor

Binary descriptors for lines extracted from an image

Introduction

One of the most challenging activities in computer vision is the extraction of useful information from a given image. Such information, usually comes in the form of points that preserve some kind of property (for instance, they are scale-invariant) and are actually representative of input image.

The goal of this module is seeking a new kind of representative information inside an image and providing the functionalities for its extraction and representation. In particular, differently from previous methods for detection of relevant elements inside an image, lines are extracted in place of points; a new class is defined ad hoc to summarize a line's properties, for reuse and plotting purposes.

Computation of binary descriptors

To obtatin a binary descriptor representing a certain line detected from a certain octave of an image, we first compute a non-binary descriptor as described in LBD . Such algorithm works on lines extracted using EDLine detector, as explained in EDL . Given a line, we consider a rectangular region centered at it and called line support region (LSR). Such region is divided into a set of bands , whose length equals the one of line.

If we indicate with the direction of line, the orthogonal and clockwise direction to line can be determined; these two directions, are used to construct a reference frame centered in the middle point of line. The gradients of pixels inside LSR can be projected to the newly determined frame, obtaining their local equivalent .

Later on, a Gaussian function is applied to all LSR's pixels along direction; first, we assign a global weighting coefficient to i-th row in LSR, where is the distance of i-th row from the center row in LSR, and is the width of bands (the same for every band). Secondly, considering a band and its neighbor bands , we assign a local weighting , where is the distance of k-th row from the center row in and . Using the global and local weights, we obtain, at the same time, the reduction of role played by gradients far from line and of boundary effect, respectively.

Each band in LSR has an associated band descriptor(BD) which is computed considering previous and next band (top and bottom bands are ignored when computing descriptor for first and last band). Once each band has been assignen its BD, the LBD descriptor of line is simply given by

To compute a band descriptor , each k-th row in it is considered and the gradients in such row are accumulated:

with .

By stacking previous results, we obtain the band description matrix (BDM)

with the number of rows in band :

Each can be obtained using the standard deviation vector and mean vector of . Thus, finally:

Once the LBD has been obtained, it must be converted into a binary form. For such purpose, we consider 32 possible pairs of BD inside it; each couple of BD is compared bit by bit and comparison generates an 8 bit string. Concatenating 32 comparison strings, we get the 256-bit final binary representation of a single LBD.

Structs

BinaryDescriptor	Class implements both functionalities for detection of lines and computation of their binary descriptor.
BinaryDescriptorMatcher	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
BinaryDescriptor_Params	List of BinaryDescriptor parameters:
DrawLinesMatchesFlags	struct for drawing options
KeyLine	A class to represent a line
LSDDetector
LSDParam	Lines extraction methodology

Constants

MLN10
RELATIVE_ERROR_FACTOR
UINT32_1

Functions

draw_keylines	Draws keylines.
draw_line_matches	Draws the found matches of keylines from two images.