Merge pull request #417 from ogreen:covariance

Author: vpisarev

modules/ximgproc/include/opencv2/ximgproc.hpp

@@ -43,6 +43,7 @@
 #include "ximgproc/structured_edge_detection.hpp"
 #include "ximgproc/seeds.hpp"
 #include "ximgproc/fast_hough_transform.hpp"
+#include "ximgproc/estimated_covariance.hpp"
 
 /** @defgroup ximgproc Extended Image Processing
   @{

modules/ximgproc/include/opencv2/ximgproc/estimated_covariance.hpp

@@ -0,0 +1,82 @@
+/*
+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
+                       (3-clause BSD License)
+
+Copyright (C) 2000-2015, Intel Corporation, all rights reserved.
+Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
+Copyright (C) 2009-2015, NVIDIA Corporation, all rights reserved.
+Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
+Copyright (C) 2015, OpenCV Foundation, all rights reserved.
+Copyright (C) 2015, Itseez Inc., 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:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions 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.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may 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 copyright holders 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.
+
+Algorithmic details of this algorithm can be found at:
+ * O. Green, Y. Birk, "A Computationally Efficient Algorithm for the 2D Covariance Method", ACM/IEEE International Conference on High Performance Computing, Networking, Storage and Analysis, Denver, Colorado, 2013
+A previous and less efficient version of the algorithm can be found:
+ * O. Green, L. David, A. Galperin, Y. Birk, "Efficient parallel computation of the estimated covariance matrix", arXiv, 2013
+
+
+*/
+#ifndef __OPENCV_ESTIMATECOVARIANCE_HPP__
+#define __OPENCV_ESTIMATECOVARIANCE_HPP__
+#ifdef __cplusplus
+
+#include <opencv2/core.hpp>
+
+namespace cv
+{
+namespace ximgproc
+{
+
+/** @brief Computes the estimated covariance matrix of an image using the sliding
+window forumlation.
+
+@param src The source image. Input image must be of a complex type.
+@param dst The destination estimated covariance matrix. Output matrix will be size (windowRows*windowCols, windowRows*windowCols).
+@param windowRows The number of rows in the window.
+@param windowCols The number of cols in the window.
+The window size parameters control the accuracy of the estimation.
+The sliding window moves over the entire image from the top-left corner
+to the bottom right corner. Each location of the window represents a sample.
+If the window is the size of the image, then this gives the exact covariance matrix.
+For all other cases, the sizes of the window will impact the number of samples
+and the number of elements in the estimated covariance matrix.
+*/
+
+CV_EXPORTS_W void covarianceEstimation(InputArray src, OutputArray dst, int windowRows, int windowCols);
+
+}
+}
+#endif
+#endif