Class SIFT


  • public class SIFT
    extends Feature2D
    Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe CITE: Lowe04 .
    • Constructor Detail

      • SIFT

        protected SIFT​(long addr)
    • Method Detail

      • __fromPtr__

        public static SIFT __fromPtr__​(long addr)
      • create

        public static SIFT create​(int nfeatures,
                                  int nOctaveLayers,
                                  double contrastThreshold,
                                  double edgeThreshold,
                                  double sigma,
                                  boolean enable_precise_upscale)
        Parameters:
        nfeatures - The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
        nOctaveLayers - The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
        contrastThreshold - The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector. Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.
        edgeThreshold - The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).
        sigma - The sigma of the Gaussian applied to the input image at the octave \#0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.
        enable_precise_upscale - Whether to enable precise upscaling in the scale pyramid, which maps index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.
        Returns:
        automatically generated
      • create

        public static SIFT create​(int nfeatures,
                                  int nOctaveLayers,
                                  double contrastThreshold,
                                  double edgeThreshold,
                                  double sigma)
        Parameters:
        nfeatures - The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
        nOctaveLayers - The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
        contrastThreshold - The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector. Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.
        edgeThreshold - The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).
        sigma - The sigma of the Gaussian applied to the input image at the octave \#0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number. index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.
        Returns:
        automatically generated
      • create

        public static SIFT create​(int nfeatures,
                                  int nOctaveLayers,
                                  double contrastThreshold,
                                  double edgeThreshold)
        Parameters:
        nfeatures - The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
        nOctaveLayers - The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
        contrastThreshold - The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector. Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.
        edgeThreshold - The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained). is captured with a weak camera with soft lenses, you might want to reduce the number. index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.
        Returns:
        automatically generated
      • create

        public static SIFT create​(int nfeatures,
                                  int nOctaveLayers,
                                  double contrastThreshold)
        Parameters:
        nfeatures - The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
        nOctaveLayers - The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
        contrastThreshold - The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector. Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09. is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained). is captured with a weak camera with soft lenses, you might want to reduce the number. index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.
        Returns:
        automatically generated
      • create

        public static SIFT create​(int nfeatures,
                                  int nOctaveLayers)
        Parameters:
        nfeatures - The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
        nOctaveLayers - The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution. (low-contrast) regions. The larger the threshold, the less features are produced by the detector. Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09. is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained). is captured with a weak camera with soft lenses, you might want to reduce the number. index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.
        Returns:
        automatically generated
      • create

        public static SIFT create​(int nfeatures)
        Parameters:
        nfeatures - The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast) number of octaves is computed automatically from the image resolution. (low-contrast) regions. The larger the threshold, the less features are produced by the detector. Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09. is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained). is captured with a weak camera with soft lenses, you might want to reduce the number. index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.
        Returns:
        automatically generated
      • create

        public static SIFT create()
        (measured in SIFT algorithm as the local contrast) number of octaves is computed automatically from the image resolution. (low-contrast) regions. The larger the threshold, the less features are produced by the detector. Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09. is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained). is captured with a weak camera with soft lenses, you might want to reduce the number. index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.
        Returns:
        automatically generated
      • create

        public static SIFT create​(int nfeatures,
                                  int nOctaveLayers,
                                  double contrastThreshold,
                                  double edgeThreshold,
                                  double sigma,
                                  int descriptorType,
                                  boolean enable_precise_upscale)
        Create SIFT with specified descriptorType.
        Parameters:
        nfeatures - The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
        nOctaveLayers - The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
        contrastThreshold - The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector. Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.
        edgeThreshold - The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).
        sigma - The sigma of the Gaussian applied to the input image at the octave \#0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.
        descriptorType - The type of descriptors. Only CV_32F and CV_8U are supported.
        enable_precise_upscale - Whether to enable precise upscaling in the scale pyramid, which maps index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.
        Returns:
        automatically generated
      • create

        public static SIFT create​(int nfeatures,
                                  int nOctaveLayers,
                                  double contrastThreshold,
                                  double edgeThreshold,
                                  double sigma,
                                  int descriptorType)
        Create SIFT with specified descriptorType.
        Parameters:
        nfeatures - The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
        nOctaveLayers - The number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
        contrastThreshold - The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector. Note: The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.
        edgeThreshold - The threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).
        sigma - The sigma of the Gaussian applied to the input image at the octave \#0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.
        descriptorType - The type of descriptors. Only CV_32F and CV_8U are supported. index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.
        Returns:
        automatically generated
      • getDefaultName

        public java.lang.String getDefaultName()
        Description copied from class: Algorithm
        Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.
        Overrides:
        getDefaultName in class Feature2D
        Returns:
        automatically generated
      • setNFeatures

        public void setNFeatures​(int maxFeatures)
      • getNFeatures

        public int getNFeatures()
      • setNOctaveLayers

        public void setNOctaveLayers​(int nOctaveLayers)
      • getNOctaveLayers

        public int getNOctaveLayers()
      • setContrastThreshold

        public void setContrastThreshold​(double contrastThreshold)
      • getContrastThreshold

        public double getContrastThreshold()
      • setEdgeThreshold

        public void setEdgeThreshold​(double edgeThreshold)
      • getEdgeThreshold

        public double getEdgeThreshold()
      • setSigma

        public void setSigma​(double sigma)
      • getSigma

        public double getSigma()
      • finalize

        protected void finalize()
                         throws java.lang.Throwable
        Overrides:
        finalize in class Feature2D
        Throws:
        java.lang.Throwable