documentation added (second part)

modules/saliency/doc/motion_saliency_algorithms.rst

+Motion Saliency Algorithms
+============================
+
+.. highlight:: cpp
+

modules/saliency/doc/objectness_algorithms.rst

+Objectness Algorithms
+============================
+
+.. highlight:: cpp
+

modules/saliency/doc/saliency_categories.rst

@@ -3,4 +3,70 @@ Saliency categories
 
 .. highlight:: cpp
 
+Base classes which give a general interface for each specialized type of saliency algorithm and provide utility methods for each algorithm in its class.
+
+StaticSaliency
+--------------
+
+.. ocv:class:: TrackerSampler
+
+StaticSaliency class::
+
+   class CV_EXPORTS_W StaticSaliency : public virtual Saliency
+   {
+    public:
+
+     bool computeBinaryMap( const Mat& saliencyMap, Mat& binaryMap );
+
+    protected:
+     virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0;
+   };
+
+StaticSaliency::computeBinaryMap
+-------------------------------
+
+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.
+
+.. ocv:function::  bool computeBinaryMap( const Mat& saliencyMap, Mat& binaryMap );
+
+   :param saliencyMap: the saliency map obtained through one of the specialized algorithms
+   :param binaryMap  : the binary map
+
+
+MotionSaliency
+--------------
+
+.. ocv:class:: MotionSaliency
+
+MotionSaliency class::
+
+   class CV_EXPORTS_W MotionSaliency : public virtual Saliency
+   {
+
+
+    protected:
+     virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0;
+   };
+
+
+Objectness
+--------------
+
+.. ocv:class:: Objectness
+
+Objectness class::
+
+   class CV_EXPORTS_W Objectness : public virtual Saliency
+   {
+
+    protected:
+     virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0;
+   };
+
+
 

modules/saliency/doc/saliency_categories.rst~

-Common Interfaces of Tracker
+Saliency categories
 ============================
 
 .. highlight:: cpp
 
+Base classes which give a general interface for each specialized type of saliency algorithm and provide utility methods for each algorithm in its class.
+
+StaticSaliency
+--------------
+
+.. ocv:class:: TrackerSampler
+
+StaticSaliency class::
+
+   class CV_EXPORTS_W StaticSaliency : public virtual Saliency
+   {
+    public:
+
+     bool computeBinaryMap( const Mat& saliencyMap, Mat& binaryMap );
+
+    protected:
+     virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0;
+   };
+
+StaticSaliency::computeBinaryMap
+-------------------------------
+
+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.
+
+.. ocv:function::  bool computeBinaryMap( const Mat& saliencyMap, Mat& binaryMap );
+
+   :param saliencyMap: the saliency map obtained through one of the specialized algorithms
+   :param binaryMap  : the binary map
+
+
+MotionSaliency
+--------------
+
+.. ocv:class:: MotionSaliency
+
+MotionSaliency class::
+
+   class CV_EXPORTS_W MotionSaliency : public virtual Saliency
+   {
+
+
+    protected:
+     virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0;
+   };
+
+
+Objectness
+--------------
+
+.. ocv:class:: Objectness
+
+Objectness class::
+
+   class CV_EXPORTS_W Objectness : public virtual Saliency
+   {
+
+    protected:
+     virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0;
+   };
+
+