From d5f9ebfc8f28484c865dd2e52e9eec2ad6d499ce Mon Sep 17 00:00:00 2001 From: Vighnesh Birodkar Date: Sat, 15 Mar 2014 01:10:58 +0530 Subject: [PATCH] minor doc changes --- skimage/feature/_hessian_det_appx.pyx | 4 ++-- skimage/feature/blob.py | 5 ++--- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/skimage/feature/_hessian_det_appx.pyx b/skimage/feature/_hessian_det_appx.pyx index f4bd213d..225f340c 100644 --- a/skimage/feature/_hessian_det_appx.pyx +++ b/skimage/feature/_hessian_det_appx.pyx @@ -22,7 +22,7 @@ cdef inline int _clip(np.int_t x, np.int_t low, np.int_t high): Returns ------- x : int - `x` clipped between 'high' and `low`. + `x` clipped between `high` and `low`. """ @@ -77,7 +77,7 @@ def _hessian_det_appx(np.ndarray[np.int_t, ndim=2] image, float sigma): """Computes the approximate Hessian Determinant over an image. This method uses box filters over integral images to compute the - approximate Hessian Determinant as described in [1]. + approximate Hessian Determinant as described in [1]_. Parameters ---------- diff --git a/skimage/feature/blob.py b/skimage/feature/blob.py index 2bb03db8..c0126a52 100644 --- a/skimage/feature/blob.py +++ b/skimage/feature/blob.py @@ -309,13 +309,12 @@ def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=500, Blobs are found using the Determinant of Hessian method [1]_. For each blob found, the method returns its coordinates and the standard deviation of the Gaussian Kernel used for the Hessian matrix whose determinant - detected the blob. Determinant of Hessians is approximated using [2]_ + detected the blob. Determinant of Hessians is approximated using [2]_. Parameters ---------- image : ndarray - Input grayscale image, blobs are assumed to be light on dark - background (white on black). + Input grayscale image.Blobs can either be light on dark or vice versa. min_sigma : float, optional The minimum standard deviation for Gaussian Kernel used to compute Hessian matrix. Keep this low to detect smaller blobs.