🤖 AIによる機械翻訳（非公式） — これは OpenCV 5.0.0 公式リファレンス（英語）を AI (Claude) で自動翻訳したものです。訳に誤りを含む場合があります。正確な情報は公式英語版（原文）を参照してください。

java.lang.Object
- org.opencv.core.Algorithm
- - org.opencv.ximgproc.SparseMatchInterpolator
  - - org.opencv.ximgproc.RICInterpolator

```
public class RICInterpolator extends SparseMatchInterpolator
```
CITE: Hu2017 の Robust Interpolation method of Correspondences (RIC) と呼ばれる、修正された区分的局所重み付きアフィン推定器に基づくスパースマッチ補間アルゴリズムと、後処理フィルタとしての Variational and Fast Global Smoother を用いる。RICInterpolator は EdgeAwareInterpolator の拡張である。この拡張の主な概念は、SLIC スーパーピクセル推定による過分割に基づく区分的アフィンモデルである。本手法は、区分的モデル間で推定を行う効率的な伝播機構を備える。

フィールドの要約
- クラス org.opencv.core.Algorithm から継承されたフィールド
  nativeObj

コンストラクタの要約

コンストラクタ
Modifier Constructor Description

protected RICInterpolator(long addr)

メソッドの要約

すべてのメソッド静的メソッドインスタンスメソッド具象メソッド
Modifier and Type	Method	Description
`static RICInterpolator`	`__fromPtr__(long addr)`
`float`	`getAlpha()`	setAlpha SEE: setAlpha
`float`	`getFGSLambda()`	setFGSLambda SEE: setFGSLambda
`float`	`getFGSSigma()`	setFGSSigma SEE: setFGSSigma
`int`	`getK()`	setK SEE: setK
`float`	`getMaxFlow()`	setMaxFlow SEE: setMaxFlow
`int`	`getModelIter()`	setModelIter SEE: setModelIter
`boolean`	`getRefineModels()`	setRefineModels SEE: setRefineModels
`int`	`getSuperpixelMode()`	setSuperpixelMode SEE: setSuperpixelMode
`int`	`getSuperpixelNNCnt()`	setSuperpixelNNCnt SEE: setSuperpixelNNCnt
`float`	`getSuperpixelRuler()`	setSuperpixelRuler SEE: setSuperpixelRuler
`int`	`getSuperpixelSize()`	setSuperpixelSize SEE: setSuperpixelSize
`boolean`	`getUseGlobalSmootherFilter()`	setUseGlobalSmootherFilter SEE: setUseGlobalSmootherFilter
`boolean`	`getUseVariationalRefinement()`	setUseVariationalRefinement SEE: setUseVariationalRefinement
`void`	`setAlpha()`	Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
`void`	`setAlpha(float alpha)`	Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
`void`	`setCostMap(Mat costMap)`	Interface to provide a more elaborated cost map, i.e.
`void`	`setFGSLambda()`	Sets the respective fastGlobalSmootherFilter() parameter.
`void`	`setFGSLambda(float lambda)`	Sets the respective fastGlobalSmootherFilter() parameter.
`void`	`setFGSSigma()`	Sets the respective fastGlobalSmootherFilter() parameter.
`void`	`setFGSSigma(float sigma)`	Sets the respective fastGlobalSmootherFilter() parameter.
`void`	`setK()`	K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment.
`void`	`setK(int k)`	K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment.
`void`	`setMaxFlow()`	MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model.
`void`	`setMaxFlow(float maxFlow)`	MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model.
`void`	`setModelIter()`	Parameter defining the number of iterations for piece-wise affine model estimation.
`void`	`setModelIter(int modelIter)`	Parameter defining the number of iterations for piece-wise affine model estimation.
`void`	`setRefineModels()`	Parameter to choose wether additional refinement of the piece-wise affine models is employed.
`void`	`setRefineModels(boolean refineModles)`	Parameter to choose wether additional refinement of the piece-wise affine models is employed.
`void`	`setSuperpixelMode()`	Parameter to choose superpixel algorithm variant to use: - cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100) - cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101) - cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102).
`void`	`setSuperpixelMode(int mode)`	Parameter to choose superpixel algorithm variant to use: - cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100) - cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101) - cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102).
`void`	`setSuperpixelNNCnt()`	Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
`void`	`setSuperpixelNNCnt(int spNN)`	Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
`void`	`setSuperpixelRuler()`	Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.
`void`	`setSuperpixelRuler(float ruler)`	Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation.
`void`	`setSuperpixelSize()`	Get the internal cost, i.e.
`void`	`setSuperpixelSize(int spSize)`	Get the internal cost, i.e.
`void`	`setUseGlobalSmootherFilter()`	Sets whether the fastGlobalSmootherFilter() post-processing is employed.
`void`	`setUseGlobalSmootherFilter(boolean use_FGS)`	Sets whether the fastGlobalSmootherFilter() post-processing is employed.
`void`	`setUseVariationalRefinement()`	Parameter to choose wether the VariationalRefinement post-processing is employed.
`void`	`setUseVariationalRefinement(boolean use_variational_refinement)`	Parameter to choose wether the VariationalRefinement post-processing is employed.

クラス org.opencv.ximgproc.SparseMatchInterpolator から継承したメソッド
interpolate

クラス org.opencv.core.Algorithm から継承されたメソッド
clear, empty, getDefaultName, getNativeObjAddr, save

クラス java.lang.Object から継承したメソッド
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- コンストラクタの詳細
  - RICInterpolator
```
protected RICInterpolator(long addr)
```
- メソッドの詳細
  - __fromPtr__
```
public static RICInterpolator __fromPtr__(long addr)
```
  - setK
```
public void setK(int k)
```
    K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of CITE: Hu2017 uses 32.
    
    引数:
    
    k - 自動生成
  - setK
```
public void setK()
```
    K is a number of nearest-neighbor matches considered, when fitting a locally affine model for a superpixel segment. However, lower values would make the interpolation noticeably faster. The original implementation of CITE: Hu2017 uses 32.
  - getK
```
public int getK()
```
    setK SEE: setK
    
    戻り値:
    
    自動生成
  - setCostMap
```
public void setCostMap(Mat costMap)
```
    Interface to provide a more elaborated cost map, i.e. edge map, for the edge-aware term. This implementation is based on a rather simple gradient-based edge map estimation. To used more complex edge map estimator (e.g. StructuredEdgeDetection that has been used in the original publication) that may lead to improved accuracies, the internal edge map estimation can be bypassed here.
    
    引数:
    
    costMap - CV_32FC1 型の Mat が必要。SEE: cv::ximgproc::createSuperpixelSLIC
  - setSuperpixelSize
```
public void setSuperpixelSize(int spSize)
```
    Get the internal cost, i.e. edge map, used for estimating the edge-aware term. SEE: setCostMap
    
    引数:
    
    spSize - 自動生成
  - setSuperpixelSize
```
public void setSuperpixelSize()
```
    Get the internal cost, i.e. edge map, used for estimating the edge-aware term. SEE: setCostMap
  - getSuperpixelSize
```
public int getSuperpixelSize()
```
    setSuperpixelSize SEE: setSuperpixelSize
    
    戻り値:
    
    自動生成
  - setSuperpixelNNCnt
```
public void setSuperpixelNNCnt(int spNN)
```
    Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
    
    引数:
    
    spNN - 自動生成
  - setSuperpixelNNCnt
```
public void setSuperpixelNNCnt()
```
    Parameter defines the number of nearest-neighbor matches for each superpixel considered, when fitting a locally affine model.
  - getSuperpixelNNCnt
```
public int getSuperpixelNNCnt()
```
    setSuperpixelNNCnt SEE: setSuperpixelNNCnt
    
    戻り値:
    
    自動生成
  - setSuperpixelRuler
```
public void setSuperpixelRuler(float ruler)
```
    Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation. SEE: cv::ximgproc::createSuperpixelSLIC
    
    引数:
    
    ruler - 自動生成
  - setSuperpixelRuler
```
public void setSuperpixelRuler()
```
    Parameter to tune enforcement of superpixel smoothness factor used for oversegmentation. SEE: cv::ximgproc::createSuperpixelSLIC
  - getSuperpixelRuler
```
public float getSuperpixelRuler()
```
    setSuperpixelRuler SEE: setSuperpixelRuler
    
    戻り値:
    
    自動生成
  - setSuperpixelMode
```
public void setSuperpixelMode(int mode)
```
    Parameter to choose superpixel algorithm variant to use: - cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100) - cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101) - cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102). SEE: cv::ximgproc::createSuperpixelSLIC
    
    引数:
    
    mode - 自動生成
  - setSuperpixelMode
```
public void setSuperpixelMode()
```
    Parameter to choose superpixel algorithm variant to use: - cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100) - cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101) - cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102). SEE: cv::ximgproc::createSuperpixelSLIC
  - getSuperpixelMode
```
public int getSuperpixelMode()
```
    setSuperpixelMode SEE: setSuperpixelMode
    
    戻り値:
    
    自動生成
  - setAlpha
```
public void setAlpha(float alpha)
```
    Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
    
    引数:
    
    alpha - 自動生成
  - setAlpha
```
public void setAlpha()
```
    Alpha is a parameter defining a global weight for transforming geodesic distance into weight.
  - getAlpha
```
public float getAlpha()
```
    setAlpha SEE: setAlpha
    
    戻り値:
    
    自動生成
  - setModelIter
```
public void setModelIter(int modelIter)
```
    Parameter defining the number of iterations for piece-wise affine model estimation.
    
    引数:
    
    modelIter - 自動生成
  - setModelIter
```
public void setModelIter()
```
    Parameter defining the number of iterations for piece-wise affine model estimation.
  - getModelIter
```
public int getModelIter()
```
    setModelIter SEE: setModelIter
    
    戻り値:
    
    自動生成
  - setRefineModels
```
public void setRefineModels(boolean refineModles)
```
    Parameter to choose wether additional refinement of the piece-wise affine models is employed.
    
    引数:
    
    refineModles - 自動生成
  - setRefineModels
```
public void setRefineModels()
```
    Parameter to choose wether additional refinement of the piece-wise affine models is employed.
  - getRefineModels
```
public boolean getRefineModels()
```
    setRefineModels SEE: setRefineModels
    
    戻り値:
    
    自動生成
  - setMaxFlow
```
public void setMaxFlow(float maxFlow)
```
    MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead.
    
    引数:
    
    maxFlow - 自動生成
  - setMaxFlow
```
public void setMaxFlow()
```
    MaxFlow is a threshold to validate the predictions using a certain piece-wise affine model. If the prediction exceeds the treshold the translational model will be applied instead.
  - getMaxFlow
```
public float getMaxFlow()
```
    setMaxFlow SEE: setMaxFlow
    
    戻り値:
    
    自動生成
  - setUseVariationalRefinement
```
public void setUseVariationalRefinement(boolean use_variational_refinement)
```
    Parameter to choose wether the VariationalRefinement post-processing is employed.
    
    引数:
    
    use_variational_refinement - 自動生成
  - setUseVariationalRefinement
```
public void setUseVariationalRefinement()
```
    Parameter to choose wether the VariationalRefinement post-processing is employed.
  - getUseVariationalRefinement
```
public boolean getUseVariationalRefinement()
```
    setUseVariationalRefinement SEE: setUseVariationalRefinement
    
    戻り値:
    
    自動生成
  - setUseGlobalSmootherFilter
```
public void setUseGlobalSmootherFilter(boolean use_FGS)
```
    Sets whether the fastGlobalSmootherFilter() post-processing is employed.
    
    引数:
    
    use_FGS - 自動生成
  - setUseGlobalSmootherFilter
```
public void setUseGlobalSmootherFilter()
```
    Sets whether the fastGlobalSmootherFilter() post-processing is employed.
  - getUseGlobalSmootherFilter
```
public boolean getUseGlobalSmootherFilter()
```
    setUseGlobalSmootherFilter SEE: setUseGlobalSmootherFilter
    
    戻り値:
    
    自動生成
  - setFGSLambda
```
public void setFGSLambda(float lambda)
```
    Sets the respective fastGlobalSmootherFilter() parameter.
    
    引数:
    
    lambda - 自動生成
  - setFGSLambda
```
public void setFGSLambda()
```
    Sets the respective fastGlobalSmootherFilter() parameter.
  - getFGSLambda
```
public float getFGSLambda()
```
    setFGSLambda SEE: setFGSLambda
    
    戻り値:
    
    自動生成
  - setFGSSigma
```
public void setFGSSigma(float sigma)
```
    Sets the respective fastGlobalSmootherFilter() parameter.
    
    引数:
    
    sigma - 自動生成
  - setFGSSigma
```
public void setFGSSigma()
```
    Sets the respective fastGlobalSmootherFilter() parameter.
  - getFGSSigma
```
public float getFGSSigma()
```
    setFGSSigma SEE: setFGSSigma
    
    戻り値:
    
    自動生成

クラス RICInterpolator

フィールドの要約

クラス org.opencv.core.Algorithm から継承されたフィールド

コンストラクタの要約

メソッドの要約

クラス org.opencv.ximgproc.SparseMatchInterpolator から継承したメソッド

クラス org.opencv.core.Algorithm から継承されたメソッド

クラス java.lang.Object から継承したメソッド

コンストラクタの詳細

RICInterpolator

メソッドの詳細

__fromPtr__

setK

setK

getK

setCostMap

setSuperpixelSize

setSuperpixelSize

getSuperpixelSize

setSuperpixelNNCnt

setSuperpixelNNCnt

getSuperpixelNNCnt

setSuperpixelRuler

setSuperpixelRuler

getSuperpixelRuler

setSuperpixelMode

setSuperpixelMode

getSuperpixelMode

setAlpha

setAlpha

getAlpha

setModelIter

setModelIter

getModelIter

setRefineModels

setRefineModels

getRefineModels

setMaxFlow

setMaxFlow

getMaxFlow

setUseVariationalRefinement

setUseVariationalRefinement

getUseVariationalRefinement

setUseGlobalSmootherFilter

setUseGlobalSmootherFilter

getUseGlobalSmootherFilter

setFGSLambda

setFGSLambda

getFGSLambda

setFGSSigma

setFGSSigma

getFGSSigma

fromPtr