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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:

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

d_H(a, b) = \sum_{i} a_i \oplus b_i

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

Example:

Input:

desc1 = [[1,0,1,0], [0,1,0,1]]
desc2 = [[1,0,1,1], [0,0,0,0], [0,1,0,1]]
ratio_threshold = 0.75

Output:

[(0, 0, 1), (1, 2, 0)]

Reasoning:

Descriptor 0 from image 1:

dist to desc2[0]: 1 bit differs
dist to desc2[1]: 2 bits differ
dist to desc2[2]: 4 bits differ Best=1, second=2, ratio=0.5 < 0.75 ✓ Match: (0,0)

Descriptor 1 from image 1:

dist to desc2[2]: 0 bits differ (exact match!) Ratio test passes trivially for exact matches.

Constraints:

desc1: Binary descriptors from image 1 (N1, D)
desc2: Binary descriptors from image 2 (N2, D)
ratio_threshold: Ratio test threshold (default 0.75)
Return: List of (idx1, idx2, distance) matches

Editor

Python 3.13.1

Test Results

0/0

Run code to see test results.