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

pub trait StaticSaliencySpectralResidualTrait: StaticSaliency {
    pub fn as_raw_StaticSaliencySpectralResidual(&self) -> *const c_void;

    pub fn as_raw_mut_StaticSaliencySpectralResidual(&mut self) -> *mut c_void;

    pub fn compute_saliency(
        &mut self, 
        image: &dyn ToInputArray, 
        saliency_map: &mut dyn ToOutputArray
    ) -> Result<bool> { ... }

    pub fn read(&mut self, fn_: &FileNode) -> Result<()> { ... }

    pub fn write(&self, fs: &mut FileStorage) -> Result<()> { ... }

    pub fn get_image_width(&self) -> Result<i32> { ... }

    pub fn set_image_width(&mut self, val: i32) -> Result<()> { ... }

    pub fn get_image_height(&self) -> Result<i32> { ... }

    pub fn set_image_height(&mut self, val: i32) -> Result<()> { ... }
}

the Spectral Residual approach from SR

Starting from the principle of natural image statistics, this method simulate the behavior of pre-attentive visual search. The algorithm analyze the log spectrum of each image and obtain the spectral residual. Then transform the spectral residual to spatial domain to obtain the saliency map, which suggests the positions of proto-objects.

Required methods

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

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

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Provided methods

pub fn compute_saliency(
    &mut self,
    image: &dyn ToInputArray,
    saliency_map: &mut dyn ToOutputArray
) -> Result<bool>[src]

pub fn read(&mut self, fn_: &FileNode) -> Result<()>[src]

pub fn write(&self, fs: &mut FileStorage) -> Result<()>[src]

pub fn get_image_width(&self) -> Result<i32>[src]

pub fn set_image_width(&mut self, val: i32) -> Result<()>[src]

pub fn get_image_height(&self) -> Result<i32>[src]

pub fn set_image_height(&mut self, val: i32) -> Result<()>[src]

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Implementors

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