cv::saliency::Saliencyを継承しています。
cv::saliency::StaticSaliencyFineGrained, cv::saliency::StaticSaliencySpectralResidualに継承されています。
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| CV_WRAP bool | computeBinaryMap (InputArray _saliencyMap, OutputArray _binaryMap) |
| | This function perform a binary map of given saliency map. This is obtained in this way: [詳解]
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virtual | ~Saliency () |
| | Destructor
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| CV_WRAP bool | computeSaliency (InputArray image, OutputArray saliencyMap) |
| | Compute the saliency [詳解]
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| virtual CV_WRAP void | clear () |
| | Clears the algorithm state [詳解]
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| virtual void | write (FileStorage &fs) const |
| | Stores algorithm parameters in a file storage [詳解]
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CV_WRAP void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
| | simplified API for language bindings これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。
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| virtual CV_WRAP void | read (const FileNode &fn) |
| | Reads algorithm parameters from a file storage [詳解]
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| virtual CV_WRAP bool | empty () const |
| | Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read [詳解]
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| virtual CV_WRAP void | save (const String &filename) const |
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| virtual CV_WRAP String | getDefaultName () const |
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| template<typename _Tp > |
| static Ptr< _Tp > | read (const FileNode &fn) |
| | Reads algorithm from the file node [詳解]
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| static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| | Loads algorithm from the file [詳解]
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| template<typename _Tp > |
| static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| | Loads algorithm from a String [詳解]
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String | className |
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◆ computeBinaryMap()
| CV_WRAP bool cv::saliency::StaticSaliency::computeBinaryMap |
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InputArray |
_saliencyMap, |
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OutputArray |
_binaryMap |
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This function perform a binary map of given saliency map. This is obtained in this way:
In a first step, to improve the definition of interest areas and facilitate identification of targets, a segmentation by clustering is performed, using K-means algorithm. Then, to gain a binary representation of clustered saliency map, since values of the map can vary according to the characteristics of frame under analysis, it is not convenient to use a fixed threshold. So, Otsu's algorithm* is used, which assumes that the image to be thresholded contains two classes of pixels or bi-modal histograms (e.g. foreground and back-ground pixels); later on, the algorithm calculates the optimal threshold separating those two classes, so that their intra-class variance is minimal.
- 引数
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| _saliencyMap | the saliency map obtained through one of the specialized algorithms |
| _binaryMap | the binary map |
◆ computeSaliencyImpl()
| virtual bool cv::saliency::StaticSaliency::computeSaliencyImpl |
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InputArray |
image, |
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OutputArray |
saliencyMap |
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protectedpure virtual |
このクラス詳解は次のファイルから抽出されました: