Hamming Distance Matcher

Implement feature matching using Hamming distance for binary descriptors. This task involves finding the best matches between two sets of binary feature descriptors.

The Hamming distance is a measure of the number of positions at which two binary strings differ, given by $d_H(a, b) = \sum_{i} a_i \oplus b_i$. It is used to compare the similarity between binary descriptors.

To perform matching:

1. For each descriptor in set A, find the nearest neighbor in set B
2. Apply the ratio test: accept if $best\_dist / second\_best\_dist < threshold$

This technique is widely used in computer vision for feature matching and object recognition.