形状の距離とマッチング

詳細説明

クラス
class	cv::AffineTransformer
	OpenCV のアフィン変換アルゴリズムのラッパークラス。: さらに...
class	cv::ChiHistogramCostExtractor
	カイ二乗 (Chi) に基づくコスト抽出。: さらに...
class	cv::EMDHistogramCostExtractor
	EMD に基づくコスト抽出。: さらに...
class	cv::EMDL1HistogramCostExtractor
	EMD-L1 に基づくコスト抽出。: さらに...
class	cv::HausdorffDistanceExtractor
	輪郭で定義される形状間の単純なハウスドルフ距離尺度。さらに...
class	cv::HistogramCostExtractor
	ヒストグラムコストアルゴリズムの抽象基底クラス。さらに...
class	cv::NormHistogramCostExtractor
	ノルムに基づくコスト抽出。: さらに...
class	cv::ShapeContextDistanceExtractor
	Shape Context 記述子およびマッチングアルゴリズムの実装。さらに...
class	cv::ShapeDistanceExtractor
	形状距離アルゴリズムの抽象基底クラス。さらに...
class	cv::ShapeTransformer
	形状変換アルゴリズムの抽象基底クラス。さらに...
class	cv::ThinPlateSplineShapeTransformer
	変換の定義。さらに...

関数
Ptr< AffineTransformer >	cv::createAffineTransformer (bool fullAffine)
Ptr< HistogramCostExtractor >	cv::createChiHistogramCostExtractor (int nDummies=25, float defaultCost=0.2f)
Ptr< HistogramCostExtractor >	cv::createEMDHistogramCostExtractor (int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f)
Ptr< HistogramCostExtractor >	cv::createEMDL1HistogramCostExtractor (int nDummies=25, float defaultCost=0.2f)
Ptr< HausdorffDistanceExtractor >	cv::createHausdorffDistanceExtractor (int distanceFlag=cv::NORM_L2, float rankProp=0.6f)
Ptr< HistogramCostExtractor >	cv::createNormHistogramCostExtractor (int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f)
Ptr< ShapeContextDistanceExtractor >	cv::createShapeContextDistanceExtractor (int nAngularBins=12, int nRadialBins=4, float innerRadius=0.2f, float outerRadius=2, int iterations=3, const Ptr< HistogramCostExtractor > &comparer=createChiHistogramCostExtractor(), const Ptr< ShapeTransformer > &transformer=createThinPlateSplineShapeTransformer())
Ptr< ThinPlateSplineShapeTransformer >	cv::createThinPlateSplineShapeTransformer (double regularizationParameter=0)
float	cv::EMDL1 (InputArray signature1, InputArray signature2)
	Haibin Ling と Kazunori Okuda による論文「EMD-L1: An efficient and Robust Algorithm for comparing histogram-based descriptors」、および Elizaveta Levina と Peter Bickel による論文「The Earth Mover's Distance is the Mallows Distance: Some Insights from Statistics」に基づき、重み付けされた2つの点配置間の「最小仕事量」距離を計算する。

関数詳解

createAffineTransformer()

Ptr< AffineTransformer > cv::createAffineTransformer

(

bool

fullAffine

)

Python:
	cv.createAffineTransformer(	fullAffine	) ->	retval

#include <opencv2/shape/shape_transformer.hpp>

完全なコンストラクタ

createChiHistogramCostExtractor()

Ptr< HistogramCostExtractor > cv::createChiHistogramCostExtractor	(	int	nDummies = 25,
		float	defaultCost = 0.2f )

Python:
	cv.createChiHistogramCostExtractor(	[, nDummies[, defaultCost]]	) ->	retval

#include <opencv2/shape/hist_cost.hpp>

createEMDHistogramCostExtractor()

Ptr< HistogramCostExtractor > cv::createEMDHistogramCostExtractor	(	int	flag = DIST_L2,
		int	nDummies = 25,
		float	defaultCost = 0.2f )

Python:
	cv.createEMDHistogramCostExtractor(	[, flag[, nDummies[, defaultCost]]]	) ->	retval

#include <opencv2/shape/hist_cost.hpp>

createEMDL1HistogramCostExtractor()

Ptr< HistogramCostExtractor > cv::createEMDL1HistogramCostExtractor	(	int	nDummies = 25,
		float	defaultCost = 0.2f )

Python:
	cv.createEMDL1HistogramCostExtractor(	[, nDummies[, defaultCost]]	) ->	retval

#include <opencv2/shape/hist_cost.hpp>

createHausdorffDistanceExtractor()

Ptr< HausdorffDistanceExtractor > cv::createHausdorffDistanceExtractor	(	int	distanceFlag = cv::NORM_L2,
		float	rankProp = 0.6f )

Python:
	cv.createHausdorffDistanceExtractor(	[, distanceFlag[, rankProp]]	) ->	retval

#include <opencv2/shape/shape_distance.hpp>

createNormHistogramCostExtractor()

Ptr< HistogramCostExtractor > cv::createNormHistogramCostExtractor	(	int	flag = DIST_L2,
		int	nDummies = 25,
		float	defaultCost = 0.2f )

Python:
	cv.createNormHistogramCostExtractor(	[, flag[, nDummies[, defaultCost]]]	) ->	retval

#include <opencv2/shape/hist_cost.hpp>

createShapeContextDistanceExtractor()

Ptr< ShapeContextDistanceExtractor > cv::createShapeContextDistanceExtractor	(	int	nAngularBins = 12,
		int	nRadialBins = 4,
		float	innerRadius = 0.2f,
		float	outerRadius = 2,
		int	iterations = 3,
		const Ptr< HistogramCostExtractor > &	comparer = createChiHistogramCostExtractor(),
		const Ptr< ShapeTransformer > &	transformer = createThinPlateSplineShapeTransformer() )

Python:
	cv.createShapeContextDistanceExtractor(	[, nAngularBins[, nRadialBins[, innerRadius[, outerRadius[, iterations[, comparer[, transformer]]]]]]]	) ->	retval

#include <opencv2/shape/shape_distance.hpp>

例

modules/shape/samples/shape_example.cpp.

createThinPlateSplineShapeTransformer()

Ptr< ThinPlateSplineShapeTransformer > cv::createThinPlateSplineShapeTransformer

(

double

regularizationParameter = 0

)

Python:
	cv.createThinPlateSplineShapeTransformer(	[, regularizationParameter]	) ->	retval

#include <opencv2/shape/shape_transformer.hpp>

完全なコンストラクタ

EMDL1()

float cv::EMDL1	(	InputArray	signature1,
		InputArray	signature2 )

#include <opencv2/shape/emdL1.hpp>

Haibin Ling と Kazunori Okuda による論文「EMD-L1: An efficient and Robust Algorithm for comparing histogram-based descriptors」、および Elizaveta Levina と Peter Bickel による論文「The Earth Mover's Distance is the Mallows Distance: Some Insights from Statistics」に基づき、重み付けされた2つの点配置間の「最小仕事量」距離を計算する。

引数

signature1	1つ目のシグネチャ、単一列の浮動小数点行列。各行は各ビンにおけるヒストグラムの値である。
signature2	signature1 と同じ形式・サイズの2つ目のシグネチャ。