From 3ece65ad622a01f1c0f9c0b30d56b9fb0adb8fd8 Mon Sep 17 00:00:00 2001 From: PhilLab Date: Tue, 24 Jun 2014 11:35:22 +0200 Subject: [PATCH] Updates python feature matching tutorial The given ORB parameter was misspelled --- doc/py_tutorials/py_feature2d/py_matcher/py_matcher.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.rst b/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.rst index 358a963..986facc 100644 --- a/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.rst +++ b/doc/py_tutorials/py_feature2d/py_matcher/py_matcher.rst @@ -16,7 +16,7 @@ Basics of Brute-Force Matcher Brute-Force matcher is simple. It takes the descriptor of one feature in first set and is matched with all other features in second set using some distance calculation. And the closest one is returned. -For BF matcher, first we have to create the BFMatcher object using **cv2.BFMatcher()**. It takes two optional params. First one is ``normType``. It specifies the distance measurement to be used. By default, it is ``cv2.NORM_L2``. It is good for SIFT, SURF etc (``cv2.NORM_L1`` is also there). For binary string based descriptors like ORB, BRIEF, BRISK etc, ``cv2.NORM_HAMMING`` should be used, which used Hamming distance as measurement. If ORB is using ``VTA_K == 3 or 4``, ``cv2.NORM_HAMMING2`` should be used. +For BF matcher, first we have to create the BFMatcher object using **cv2.BFMatcher()**. It takes two optional params. First one is ``normType``. It specifies the distance measurement to be used. By default, it is ``cv2.NORM_L2``. It is good for SIFT, SURF etc (``cv2.NORM_L1`` is also there). For binary string based descriptors like ORB, BRIEF, BRISK etc, ``cv2.NORM_HAMMING`` should be used, which used Hamming distance as measurement. If ORB is using ``WTA_K == 3 or 4``, ``cv2.NORM_HAMMING2`` should be used. Second param is boolean variable, ``crossCheck`` which is false by default. If it is true, Matcher returns only those matches with value (i,j) such that i-th descriptor in set A has j-th descriptor in set B as the best match and vice-versa. That is, the two features in both sets should match each other. It provides consistant result, and is a good alternative to ratio test proposed by D.Lowe in SIFT paper. -- 2.7.4