Struct opencv::xfeatures2d::MSDDetector
source · pub struct MSDDetector { /* private fields */ }Expand description
Class implementing the MSD (Maximal Self-Dissimilarity) keypoint detector, described in Tombari14.
The algorithm implements a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of “contextual self-dissimilarity” reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover, it extends to contextual information the local self-dissimilarity notion embedded in established detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and localization accuracy.
Implementations§
source§impl MSDDetector
impl MSDDetector
sourcepub fn create(
m_patch_radius: i32,
m_search_area_radius: i32,
m_nms_radius: i32,
m_nms_scale_radius: i32,
m_th_saliency: f32,
m_k_nn: i32,
m_scale_factor: f32,
m_n_scales: i32,
m_compute_orientation: bool
) -> Result<Ptr<MSDDetector>>
pub fn create( m_patch_radius: i32, m_search_area_radius: i32, m_nms_radius: i32, m_nms_scale_radius: i32, m_th_saliency: f32, m_k_nn: i32, m_scale_factor: f32, m_n_scales: i32, m_compute_orientation: bool ) -> Result<Ptr<MSDDetector>>
C++ default parameters
- m_patch_radius: 3
- m_search_area_radius: 5
- m_nms_radius: 5
- m_nms_scale_radius: 0
- m_th_saliency: 250.0f
- m_k_nn: 4
- m_scale_factor: 1.25f
- m_n_scales: -1
- m_compute_orientation: false