From b61b3303559da8c8ca4018c09228f1c32b933d2b Mon Sep 17 00:00:00 2001 From: Ankit Agrawal Date: Wed, 14 Aug 2013 14:25:15 +0530 Subject: [PATCH] Adding a short description of the algorithm --- skimage/feature/censure.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/skimage/feature/censure.py b/skimage/feature/censure.py index d52583c2..272218f6 100644 --- a/skimage/feature/censure.py +++ b/skimage/feature/censure.py @@ -187,7 +187,17 @@ def keypoints_censure(image, min_scale=1, max_scale=7, mode='DoB', http://www.jamris.org/01_2013/saveas.php?QUEST=JAMRIS_No01_2013_P_11-20.pdf """ - + # (1) First we generate the required scales on the input grayscale image + # using a bilevel filter and stack them up in `filter_response`. + # (2) We then perform Non-Maximal suppression in 3 x 3 x 3 window on the + # filter_response to suppress points that are neither minima or maxima in + # 3 x 3 x 3 neighbourhood. We obtain a boolean ndarray `feature_mask` + # containing all the minimas and maximas in `filter_response` as True. + # (3) Then we suppress all the points in the `feature_mask` for which the + # corresponding point in the image at a particular scale has the ratio of + # principal curvatures greater than `line_threshold`. + # (4) Finally, we remove the border keypoints and return the keypoints + # along with its corresponding scale. image = np.squeeze(image) if image.ndim != 2: raise ValueError("Only 2-D gray-scale images supported.")