added two images to show impact of epsilon and cluster_epsilon

changed title

Point_set_shape_detection_3/doc/Point_set_shape_detection_3/Doxyfile.in

@@ -1,6 +1,6 @@
 @INCLUDE = ${CGAL_DOC_PACKAGE_DEFAULTS}
 
-PROJECT_NAME                       =  "CGAL ${CGAL_CREATED_VERSION_NUM} - Shape detection in Point Sets"
+PROJECT_NAME                       =  "CGAL ${CGAL_CREATED_VERSION_NUM} - Point Set Shape Detection"
 
 INPUT                      =  ${CMAKE_SOURCE_DIR}/Point_set_shape_detection_3/doc/Point_set_shape_detection_3/ \ 
                               ${CMAKE_SOURCE_DIR}/Point_set_shape_detection_3/include

Point_set_shape_detection_3/doc/Point_set_shape_detection_3/PackageDescription.txt

@@ -1,14 +1,14 @@
-/// \defgroup PkgPointSetShapeDetection3 Shape Detection in Point Sets Reference
+/// \defgroup PkgPointSetShapeDetection3 Point Set Shape Detection Reference
 /*!
 \addtogroup PkgPointSetShapeDetection3
 
-\cgalPkgDescriptionBegin{Shape Detection in Point Sets, PkgPointSetShapeDetection3Summary}
+\cgalPkgDescriptionBegin{Point Set Shape Detection, PkgPointSetShapeDetection3Summary}
 \cgalPkgPicture{shapes_detail.png}
 
 \cgalPkgSummaryBegin
 \cgalPkgAuthors{Sven Oesau, Yannick Verdie, Clément Jamin}
 \cgalPkgDesc{This package implements a ransac-based shape detection algorithm for point sets with unoriented normals.}
-\cgalPkgManuals{Chapter_Shape Detection in Point Sets, PkgPointSetShapeDetection3}
+\cgalPkgManuals{Chapter_Point Set Shape Detection, PkgPointSetShapeDetection3}
 \cgalPkgSummaryEnd
 
 \cgalPkgShortInfoBegin

Point_set_shape_detection_3/doc/Point_set_shape_detection_3/Shape_detection_3.txt

@@ -2,7 +2,7 @@ namespace CGAL {
 /*!
 
 \mainpage User Manual 
-\anchor Chapter_Shape_Detection_In_Point_Sets
+\anchor Chapter_Point_Set_Shape_Detection
 \cgalAutoToc
 
 \authors Sven Oesau, Yannick Verdie, Clément Jamin
@@ -21,7 +21,7 @@ The method follows a ransac approach, i.e. random sample consensus. Points are r
 
 However, testing all possible shape candidates against all input data to determine the largest shape is impractical. Therefore, only a certain amount of shape candidates is tested against a subset of the input data. The search thoroughness is controlled by a parameter that specifies the probability to miss the largest candidate. A lower probability provides a higher reliability and determinism at the cost of running time.
 
-The metric to decide whether a point supports a shape is based on the euclidean distance to the shape and the deviation from the point normal to the normal of the shape in the point projected onto the surface. Two user-specified parameters, epsilon and normal_threshold, control the tolerance for distance and deviation. Additionally there is a connectivity criteria. The number of supporting points of a shape corresponds to the largest cluster of points fitting the metric. In a cluster of points, each point has a neighbor in the cluster within a certain user-specified distance, called cluster_epsilon.
+The metric to decide whether a point supports a shape is based on the Euclidean distance to the shape and the deviation from the point normal to the normal of the shape in the point projected onto the surface. Two user-specified parameters, epsilon and normal_threshold, control the tolerance for distance and deviation. Additionally there is a connectivity criteria. The number of supporting points of a shape corresponds to the largest cluster of points fitting the metric. In a cluster of points, each point has a neighbor in the cluster within a certain user-specified distance, called cluster_epsilon.
 
 The candidate with the highest number of supporting points is repeatedly extracted until no candidate can be found with a minimum number of supporting points specified by the user.
 The algorithm is described in full detail in \cite cgal:-erpcsd-07.