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OpenCV Kaze Feature Parameter reference in libs

Parameters
extended Set to enable extraction of extended (128-byte) descriptor.
upright Set to enable use of upright descriptors (non rotation-invariant).
threshold Detector response threshold to accept point
nOctaves Maximum octave evolution of the image
nOctaveLayers Default number of sublevels per scale level
diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER

bool extended=false, bool upright=false, float threshold=0.001f, int nOctaves=4, int nOctaveLayers=4, int diffusivity=KAZE::DIFF_PM_G2

Kaze github reference

/// This function computes the Perona and Malik conductivity coefficient g1
/// g1 = exp(-|dL|^2/k^2)
/// @param Lx First order image derivative in X-direction (horizontal)
/// @param Ly First order image derivative in Y-direction (vertical)
/// @param dst Output image
/// @param k Contrast factor parameter
void pm_g1(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float k);

/// This function computes the Perona and Malik conductivity coefficient g2
/// g2 = 1 / (1 + dL^2 / k^2)
/// @param Lx First order image derivative in X-direction (horizontal)
/// @param Ly First order image derivative in Y-direction (vertical)
/// @param dst Output image
/// @param k Contrast factor parameter
void pm_g2(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float k);

/// This function computes Weickert conductivity coefficient gw
/// @param Lx First order image derivative in X-direction (horizontal)
/// @param Ly First order image derivative in Y-direction (vertical)
/// @param dst Output image
/// @param k Contrast factor parameter
/// @note For more information check the following paper: J. Weickert
/// Applications of nonlinear diffusion in image processing and computer vision,
/// Proceedings of Algorithmy 2000
void weickert_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float k);

/// This function computes Charbonnier conductivity coefficient gc
/// gc = 1 / sqrt(1 + dL^2 / k^2)
/// @param Lx First order image derivative in X-direction (horizontal)
/// @param Ly First order image derivative in Y-direction (vertical)
/// @param dst Output image
/// @param k Contrast factor parameter
/// @note For more information check the following paper: J. Weickert
/// Applications of nonlinear diffusion in image processing and computer vision,
/// Proceedings of Algorithmy 2000
void charbonnier_diffusivity(const cv::Mat& Lx, const cv::Mat& Ly, cv::Mat& dst, const float k);

Published Dec 18, 2023

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