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+/*M///////////////////////////////////////////////////////////////////////////////////////
+ //
+ //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+ //
+ //  By downloading, copying, installing or using the software you agree to this license.
+ //  If you do not agree to this license, do not download, install,
+ //  copy or use the software.
+ //
+ //
+ //                           License Agreement
+ //                For Open Source Computer Vision Library
+ //
+ // Copyright (C) 2014, OpenCV Foundation, all rights reserved.
+ // Third party copyrights are property of their respective owners.
+ //
+ // Redistribution and use in source and binary forms, with or without modification,
+ // are permitted provided that the following conditions are met:
+ //
+ //   * Redistribution's of source code must retain the above copyright notice,
+ //     this list of conditions and the following disclaimer.
+ //
+ //   * Redistribution's in binary form must reproduce the above copyright notice,
+ //     this list of conditions and the following disclaimer in the documentation
+ //     and/or other materials provided with the distribution.
+ //
+ //   * The name of the copyright holders may not be used to endorse or promote products
+ //     derived from this software without specific prior written permission.
+ //
+ // This software is provided by the copyright holders and contributors "as is" and
+ // any express or implied warranties, including, but not limited to, the implied
+ // warranties of merchantability and fitness for a particular purpose are disclaimed.
+ // In no event shall the Intel Corporation or contributors be liable for any direct,
+ // indirect, incidental, special, exemplary, or consequential damages
+ // (including, but not limited to, procurement of substitute goods or services;
+ // loss of use, data, or profits; or business interruption) however caused
+ // and on any theory of liability, whether in contract, strict liability,
+ // or tort (including negligence or otherwise) arising in any way out of
+ // the use of this software, even if advised of the possibility of such damage.
+ //
+ //M*/
+
+#ifndef __OPENCV_SALIENCY_BASE_CLASSES_HPP__
+#define __OPENCV_SALIENCY_BASE_CLASSES_HPP__
+
+#include "opencv2/core.hpp"
+#include <opencv2/core/persistence.hpp>
+#include "opencv2/imgproc.hpp"
+#include <iostream>
+#include <sstream>
+#include <complex>
+
+namespace cv
+{
+namespace saliency
+{
+
+//! @addtogroup saliency
+//! @{
+
+/************************************ Saliency Base Class ************************************/
+
+class CV_EXPORTS_W Saliency : public virtual Algorithm
+{
+ public:
+  /**
+   * \brief Destructor
+   */
+  virtual ~Saliency();
+
+  /**
+   * \brief Create Saliency by saliency type.
+   */
+  static Ptr<Saliency> create( const String& saliencyType );
+
+  /**
+   * \brief Compute the saliency
+   * \param image        The image.
+   * \param saliencyMap      The computed saliency map.
+   * \return true if the saliency map is computed, false otherwise
+   */
+  CV_WRAP bool computeSaliency( InputArray image, OutputArray saliencyMap );
+
+  /**
+   * \brief Get the name of the specific saliency type
+   * \return The name of the tracker initializer
+   */
+  CV_WRAP String getClassName() const;
+
+ protected:
+
+  virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap ) = 0;
+  String className;
+};
+
+/************************************ Static Saliency Base Class ************************************/
+class CV_EXPORTS_W StaticSaliency : public virtual Saliency
+{
+ public:
+
+    /** @brief 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.
+
+    @param _saliencyMap the saliency map obtained through one of the specialized algorithms
+    @param _binaryMap the binary map
+     */
+  CV_WRAP bool computeBinaryMap( InputArray _saliencyMap, OutputArray _binaryMap );
+ protected:
+  virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;
+
+};
+
+/************************************ Motion Saliency Base Class ************************************/
+class CV_EXPORTS_W MotionSaliency : public virtual Saliency
+{
+
+ protected:
+  virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;
+
+};
+
+/************************************ Objectness Base Class ************************************/
+class CV_EXPORTS_W Objectness : public virtual Saliency
+{
+
+ protected:
+  virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;
+
+};
+
+//! @}
+
+} /* namespace saliency */
+} /* namespace cv */
+
+#endif