diff --git a/skimage/feature/brief.py b/skimage/feature/brief.py index 45c9fa11..b7744b4f 100644 --- a/skimage/feature/brief.py +++ b/skimage/feature/brief.py @@ -24,22 +24,22 @@ class BRIEF(DescriptorExtractor): Parameters ---------- - descriptor_size : int + descriptor_size : int, optional Size of BRIEF descriptor for each keypoint. Sizes 128, 256 and 512 recommended by the authors. Default is 256. - patch_size : int + patch_size : int, optional Length of the two dimensional square patch sampling region around the keypoints. Default is 49. - mode : {'normal', 'uniform'} + mode : {'normal', 'uniform'}, optional Probability distribution for sampling location of decision pixel-pairs around keypoints. - sample_seed : int + sample_seed : int, optional Seed for the random sampling of the decision pixel-pairs. From a square window with length patch_size, pixel pairs are sampled using the `mode` parameter to build the descriptors using intensity comparison. The value of `sample_seed` must be the same for the images to be matched while building the descriptors. - sigma : float + sigma : float, optional Standard deviation of the Gaussian low pass filter applied to the image to alleviate noise sensitivity, which is strongly recommended to obtain discriminative and good descriptors. diff --git a/skimage/feature/censure.py b/skimage/feature/censure.py index f201e12f..991875df 100644 --- a/skimage/feature/censure.py +++ b/skimage/feature/censure.py @@ -115,15 +115,15 @@ class CenSurE(FeatureDetector): """CenSurE keypoint detector. - min_scale : int + min_scale : int, optional Minimum scale to extract keypoints from. - max_scale : int + max_scale : int, optional Maximum scale to extract keypoints from. The keypoints will be extracted from all the scales except the first and the last i.e. from the scales in the range [min_scale + 1, max_scale - 1]. The filter sizes for different scales is such that the two adjacent scales comprise of an octave. - mode : {'DoB', 'Octagon', 'STAR'} + mode : {'DoB', 'Octagon', 'STAR'}, optional Type of bi-level filter used to get the scales of the input image. Possible values are 'DoB', 'Octagon' and 'STAR'. The three modes represent the shape of the bi-level filters i.e. box(square), octagon @@ -132,10 +132,10 @@ class CenSurE(FeatureDetector): weights being uniformly negative in both the inner octagon while uniformly positive in the difference region. Use STAR and Octagon for better features and DoB for better performance. - non_max_threshold : float + non_max_threshold : float, optional Threshold value used to suppress maximas and minimas with a weak magnitude response obtained after Non-Maximal Suppression. - line_threshold : float + line_threshold : float, optional Threshold for rejecting interest points which have ratio of principal curvatures greater than this value. diff --git a/skimage/feature/orb.py b/skimage/feature/orb.py index a743962b..e7ab22e1 100644 --- a/skimage/feature/orb.py +++ b/skimage/feature/orb.py @@ -25,32 +25,32 @@ class ORB(FeatureDetector, DescriptorExtractor): Parameters ---------- - n_keypoints : int + n_keypoints : int, optional Number of keypoints to be returned. The function will return the best ``n_keypoints`` according to the Harris corner response if more than ``n_keypoints`` are detected. If not, then all the detected keypoints are returned. - fast_n : int + fast_n : int, optional The ``n`` parameter in ``feature.corner_fast``. Minimum number of consecutive pixels out of 16 pixels on the circle that should all be either brighter or darker w.r.t test-pixel. A point c on the circle is darker w.r.t test pixel p if ``Ic < Ip - threshold`` and brighter if ``Ic > Ip + threshold``. Also stands for the n in ``FAST-n`` corner detector. - fast_threshold : float + fast_threshold : float, optional The ``threshold`` parameter in ``feature.corner_fast``. Threshold used to decide whether the pixels on the circle are brighter, darker or similar w.r.t. the test pixel. Decrease the threshold when more corners are desired and vice-versa. - harris_k : float + harris_k : float, optional The ``k`` parameter in ``feature.corner_harris``. Sensitivity factor to separate corners from edges, typically in range ``[0, 0.2]``. Small values of k result in detection of sharp corners. - downscale : float + downscale : float, optional Downscale factor for the image pyramid. Default value 1.2 is chosen so that there are more dense scales which enable robust scale invariance for a subsequent feature description. - n_scales : int + n_scales : int, optional Maximum number of scales from the bottom of the image pyramid to extract the features from. diff --git a/skimage/feature/util.py b/skimage/feature/util.py index c3970766..b5faa565 100644 --- a/skimage/feature/util.py +++ b/skimage/feature/util.py @@ -53,12 +53,12 @@ def plot_matches(ax, image1, image2, keypoints1, keypoints2, matches, Indices of corresponding matches in first and second set of descriptors, where ``matches[:, 0]`` denote the indices in the first and ``matches[:, 1]`` the indices in the second set of descriptors. - keypoints_color : matplotlib color + keypoints_color : matplotlib color, optional Color for keypoint locations. - matches_color : matplotlib color + matches_color : matplotlib color, optional Color for lines which connect keypoint matches. By default the color is chosen randomly. - only_matches : bool + only_matches : bool, optional Whether to only plot matches and not plot the keypoint locations. """