[−][src]Trait opencv::hub_prelude::StaticSaliency

pub trait StaticSaliency: Saliency {
    pub fn as_raw_StaticSaliency(&self) -> *const c_void;
    pub fn as_raw_mut_StaticSaliency(&mut self) -> *mut c_void;

    pub fn compute_binary_map(
        &mut self, 
        _saliency_map: &dyn ToInputArray, 
        _binary_map: &mut dyn ToOutputArray
    ) -> Result<bool> { ... }
}

********************************* Static Saliency Base Class ***********************************

Required methods

`pub fn as_raw_StaticSaliency(&self) -> *const c_void`[src]

`pub fn as_raw_mut_StaticSaliency(&mut self) -> *mut c_void`[src]

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Provided methods

`pub fn compute_binary_map( &mut self, _saliency_map: &dyn ToInputArray, _binary_map: &mut dyn ToOutputArray ) -> Result<bool>`[src]

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.