StaticSaliency (OpenCV 4.13.0 Java documentation)

java.lang.Object
- org.opencv.core.Algorithm
- - org.opencv.saliency.Saliency
  - - org.opencv.saliency.StaticSaliency

Direct Known Subclasses:

StaticSaliencyFineGrained, StaticSaliencySpectralResidual
```
public class StaticSaliency
extends Saliency
```
******************************* Static Saliency Base Class *********************************

Field Summary
- Fields inherited from class org.opencv.core.Algorithm
  nativeObj

Constructor Summary

Constructors
Modifier Constructor Description

protected StaticSaliency(long addr)

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`static StaticSaliency`	`__fromPtr__(long addr)`
`boolean`	`computeBinaryMap(Mat _saliencyMap, Mat _binaryMap)`	This function perform a binary map of given saliency map.
`protected void`	`finalize()`

Methods inherited from class org.opencv.saliency.Saliency
computeSaliency

Methods inherited from class org.opencv.core.Algorithm
clear, empty, getDefaultName, getNativeObjAddr, save

Methods inherited from class java.lang.Object
clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - StaticSaliency
```
protected StaticSaliency(long addr)
```
- Method Detail
  - __fromPtr__
```
public static StaticSaliency __fromPtr__(long addr)
```
  - computeBinaryMap
```
public boolean computeBinaryMap(Mat _saliencyMap,
                                Mat _binaryMap)
```
    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.
    
    Parameters:
    
    _saliencyMap - the saliency map obtained through one of the specialized algorithms
    
    _binaryMap - the binary map
    
    Returns:
    
    automatically generated
  - finalize
```
protected void finalize()
                 throws java.lang.Throwable
```
    Overrides:
    
    finalize in class Saliency
    
    Throws:
    
    java.lang.Throwable