Rename peak_min_dist to peak_local_max.

skimage/feature/__init__.py

@@ -1,3 +1,3 @@
 from hog import hog
 from greycomatrix import greycomatrix, greycoprops
-from peak import peak_min_dist
+from peak import peak_local_max

skimage/feature/peak.py

@@ -2,12 +2,11 @@ import numpy as np
 from scipy import ndimage
 
 
-def peak_min_dist(image, min_distance=10, threshold=0.1):
+def peak_local_max(image, min_distance=10, threshold=0.1):
     """Return coordinates of peaks in an image.
 
-    Candidate peaks are determined by a relative `threshold`, and peaks that
-    are too close (as determined by `min_distance`) to larger peaks are
-    rejected.
+    Peaks are the local maxima in a region of `2 * min_distance + 1`
+    (i.e. peaks are separated by at least `min_distance`).
 
     Parameters
     ----------

skimage/feature/tests/test_peak.py

@@ -11,7 +11,7 @@ def test_noisy_peaks():
     for r, c in peak_locations:
         image[r, c] = 1
 
-    peaks_detected = feature.peak_min_dist(image, min_distance=5)
+    peaks_detected = feature.peak_local_max(image, min_distance=5)
 
     assert len(peaks_detected) == len(peak_locations)
     for loc in peaks_detected:

skimage/filter/harris.py

@@ -79,7 +79,7 @@ def harris(image, min_distance=10, threshold=0.1, eps=1e-6,
     """
     harrisim = _compute_harris_response(image, eps=eps,
                     gaussian_deviation=gaussian_deviation)
-    coordinates = feature.peak_min_dist(harrisim, min_distance=min_distance,
+    coordinates = feature.peak_local_max(harrisim, min_distance=min_distance,
                                         threshold=threshold)
     return coordinates