Edge Drawing Improvements

modules/ximgproc/include/opencv2/ximgproc.hpp

@@ -78,6 +78,17 @@ i.e. algorithms which somehow takes into account pixel affinities in natural ima
 
     @defgroup ximgproc_edge_drawing EdgeDrawing
 
+EDGE DRAWING LIBRARY FOR GEOMETRIC FEATURE EXTRACTION AND VALIDATION
+
+Edge Drawing (ED) algorithm is an proactive approach on edge detection problem. In contrast to many other existing edge detection algorithms which follow a subtractive
+approach (i.e. after applying gradient filters onto an image eliminating pixels w.r.t. several rules, e.g. non-maximal suppression and hysteresis in Canny), ED algorithm
+works via an additive strategy, i.e. it picks edge pixels one by one, hence the name Edge Drawing. Then we process those random shaped edge segments to extract higher level
+edge features, i.e. lines, circles, ellipses, etc. The popular method of extraction edge pixels from the thresholded gradient magnitudes is non-maximal supression that tests
+every pixel whether it has the maximum gradient response along its gradient direction and eliminates if it does not. However, this method does not check status of the
+neighboring pixels, and therefore might result low quality (in terms of edge continuity, smoothness, thinness, localization) edge segments. Instead of non-maximal supression,
+ED points a set of edge pixels and join them by maximizing the total gradient response of edge segments. Therefore it can extract high quality edge segments without need for
+an additional hysteresis step.
+
     @defgroup ximgproc_fourier Fourier descriptors
     @}
 */

modules/ximgproc/include/opencv2/ximgproc/edge_drawing.hpp

@@ -16,17 +16,6 @@ namespace ximgproc
 //! @{
 
 /** @brief Class implementing the ED (EdgeDrawing) @cite topal2012edge, EDLines @cite akinlar2011edlines, EDPF @cite akinlar2012edpf and EDCircles @cite akinlar2013edcircles algorithms
-
-EDGE DRAWING LIBRARY FOR GEOMETRIC FEATURE EXTRACTION AND VALIDATION
-
-Edge Drawing (ED) algorithm is an proactive approach on edge detection problem. In contrast to many other existing edge detection algorithms which follow a subtractive
-approach (i.e. after applying gradient filters onto an image eliminating pixels w.r.t. several rules, e.g. non-maximal suppression and hysteresis in Canny), ED algorithm
-works via an additive strategy, i.e. it picks edge pixels one by one, hence the name Edge Drawing. Then we process those random shaped edge segments to extract higher level
-edge features, i.e. lines, circles, ellipses, etc. The popular method of extraction edge pixels from the thresholded gradient magnitudes is non-maximal supressiun that tests
-every pixel whether it has the maximum gradient response along its gradient direction and eliminates if it does not. However, this method does not check status of the
-neighboring pixels, and therefore might result low quality (in terms of edge continuity, smoothness, thinness, localization) edge segments. Instead of non-maximal supression,
-ED points a set of edge pixels and join them by maximizing the total gradient response of edge segments. Therefore it can extract high quality edge segments without need for
-an additional hysteresis step.
 */
 
 class CV_EXPORTS_W EdgeDrawing : public Algorithm