クラス
struct	cv::line_descriptor::KeyLine
	線を表現するクラス[【詳解】（英語］

class	cv::line_descriptor::BinaryDescriptor
	クラスは，線の検出とそのバイナリ記述子の計算の両方の機能を実装しています．[【詳解】（英語］

struct	cv::line_descriptor::LSDParam

class	cv::line_descriptor::LSDDetector

class	cv::line_descriptor::BinaryDescriptorMatcher
	は，ユーザやクラス内部から提供された（いずれにせよユーザが入力しなければならない）データセットに対して，以下のモデルで問い合わせを行うためのすべての機能を提供します．Descriptor Matchers [【詳解】（英語］

struct	cv::line_descriptor::DrawLinesMatchesFlags

関数
CV_EXPORTS_W void	cv::line_descriptor::drawLineMatches (const Mat &img1, const std::vector< KeyLine > &keylines1, const Mat &img2, const std::vector< KeyLine > &keylines2, const std::vector< DMatch > &matches1to2, CV_OUT Mat &outImg, const Scalar &matchColor=Scalar::all(-1), const Scalar &singleLineColor=Scalar::all(-1), const std::vector< char > &matchesMask=std::vector< char >(), int flags=DrawLinesMatchesFlags::DEFAULT)
	2つの画像からマッチしたキーラインを描画します。[【詳解】（英語］

CV_EXPORTS_W void	cv::line_descriptor::drawKeylines (const Mat &image, const std::vector< KeyLine > &keylines, CV_OUT Mat &outImage, const Scalar &color=Scalar::all(-1), int flags=DrawLinesMatchesFlags::DEFAULT)
	キーラインを描画します．[【詳解】（英語］

詳解

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 $\{B_1, B_2, ..., B_m\}$ , whose length equals the one of line.

If we indicate with $\bf{d}_L$ the direction of line, the orthogonal and clockwise direction to line $\bf{d}_{\perp}$ can be determined; these two directions, are used to construct a reference frame centered in the middle point of line. The gradients of pixels $\bf{g'}$ inside LSR can be projected to the newly determined frame, obtaining their local equivalent $\bf{g'} = (\bf{g}^T \cdot \bf{d}_{\perp}, \bf{g}^T \cdot \bf{d}_L)^T \triangleq (\bf{g'}_{d_{\perp}}, \bf{g'}_{d_L})^T$ .

Later on, a Gaussian function is applied to all LSR's pixels along $\bf{d}_\perp$ direction; first, we assign a global weighting coefficient $f_g(i) = (1/\sqrt{2\pi}\sigma_g)e^{-d^2_i/2\sigma^2_g}$ to i*-th row in LSR, where $d_i$ is the distance of i-th row from the center row in LSR, $\sigma_g = 0.5(m \cdot w - 1)$ and $w$ is the width of bands (the same for every band). Secondly, considering a band $B_j$ and its neighbor bands $B_{j-1}, B_{j+1}$ , we assign a local weighting $F_l(k) = (1/\sqrt{2\pi}\sigma_l)e^{-d'^2_k/2\sigma_l^2}$ , where $d'_k$ is the distance of k-th row from the center row in $B_j$ and $\sigma_l = w$ . 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 $B_j$ 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

$LBD = (BD_1^T, BD_2^T, ... , BD^T_m)^T.$

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

$\begin{matrix} \bf{V1}^k_j = \lambda \sum\limits_{\bf{g}'_{d_\perp}>0}\bf{g}'_{d_\perp}, & \bf{V2}^k_j = \lambda \sum\limits_{\bf{g}'_{d_\perp}<0} -\bf{g}'_{d_\perp}, \\ \bf{V3}^k_j = \lambda \sum\limits_{\bf{g}'_{d_L}>0}\bf{g}'_{d_L}, & \bf{V4}^k_j = \lambda \sum\limits_{\bf{g}'_{d_L}<0} -\bf{g}'_{d_L}\end{matrix}.$

with $\lambda = f_g(k)f_l(k)$ .

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

$BDM_j = \left(\begin{matrix} \bf{V1}_j^1 & \bf{V1}_j^2 & \ldots & \bf{V1}_j^n \\ \bf{V2}_j^1 & \bf{V2}_j^2 & \ldots & \bf{V2}_j^n \\ \bf{V3}_j^1 & \bf{V3}_j^2 & \ldots & \bf{V3}_j^n \\ \bf{V4}_j^1 & \bf{V4}_j^2 & \ldots & \bf{V4}_j^n \end{matrix} \right) \in \mathbb{R}^{4\times n},$

with $n$ the number of rows in band $B_j$ :

$n = \begin{cases} 2w, & j = 1||m; \\ 3w, & \mbox{else}. \end{cases}$

Each $BD_j$ can be obtained using the standard deviation vector $S_j$ and mean vector $M_j$ of $BDM_J$ . Thus, finally:

$LBD = (M_1^T, S_1^T, M_2^T, S_2^T, \ldots, M_m^T, S_m^T)^T \in \mathbb{R}^{8m}$

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.

関数詳解

◆ drawKeylines()

CV_EXPORTS_W void cv::line_descriptor::drawKeylines	(	const Mat &	image,
		const std::vector< KeyLine > &	keylines,
		CV_OUT Mat &	outImage,
		const Scalar &	color = `Scalar::all(-1)`,
		int	flags = `DrawLinesMatchesFlags::DEFAULT`
	)

キーラインを描画します．

引数

image	入力画像
keylines	描画されるキーライン
outImage	描画する出力画像
color	描画される線の色（デフォルト値に設定されている場合，色はランダムに選択されます）．
flags	描画フラグ

◆ drawLineMatches()

CV_EXPORTS_W void cv::line_descriptor::drawLineMatches	(	const Mat &	img1,
		const std::vector< KeyLine > &	keylines1,
		const Mat &	img2,
		const std::vector< KeyLine > &	keylines2,
		const std::vector< DMatch > &	matches1to2,
		CV_OUT Mat &	outImg,
		const Scalar &	matchColor = `Scalar::all(-1)`,
		const Scalar &	singleLineColor = `Scalar::all(-1)`,
		const std::vector< char > &	matchesMask = `std::vector< char >()`,
		int	flags = `DrawLinesMatchesFlags::DEFAULT`
	)

2つの画像からマッチしたキーラインを描画します。