Feature matching between images in Computer Vision

Introduction

Feature matching helps find similar points in two pictures. This is useful to understand how images relate or overlap.

When stitching photos to make a panorama.

To track objects moving between video frames.

To recognize places or objects from different views.

When aligning images for 3D reconstruction.

To compare two images for similarities or changes.

Syntax

Computer Vision

import cv2

# Detect features
feature_detector = cv2.SIFT_create()
keypoints1, descriptors1 = feature_detector.detectAndCompute(image1, None)
keypoints2, descriptors2 = feature_detector.detectAndCompute(image2, None)

# Match features
matcher = cv2.BFMatcher()
matches = matcher.knnMatch(descriptors1, descriptors2, k=2)

# Apply ratio test to keep good matches
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append(m)

Use a feature detector like SIFT or ORB to find keypoints and descriptors.

Use a matcher like BFMatcher or FLANN to find matching features between images.

Examples

Using ORB detector instead of SIFT for faster, free alternative.

Computer Vision

feature_detector = cv2.ORB_create()
keypoints1, descriptors1 = feature_detector.detectAndCompute(image1, None)
keypoints2, descriptors2 = feature_detector.detectAndCompute(image2, None)

Using BFMatcher with Hamming norm for ORB descriptors.

Computer Vision

matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
matches = matcher.match(descriptors1, descriptors2)

Applying Lowe's ratio test to filter good matches.

Computer Vision

good_matches = []
for m, n in matches:
    if m.distance < 0.7 * n.distance:
        good_matches.append(m)

Sample Model

This program loads two images, finds keypoints and descriptors using SIFT, matches them with BFMatcher, applies Lowe's ratio test, and prints how many good matches were found.

Computer Vision

import cv2
import numpy as np

# Load two images in grayscale
image1 = cv2.imread('image1.jpg', cv2.IMREAD_GRAYSCALE)
image2 = cv2.imread('image2.jpg', cv2.IMREAD_GRAYSCALE)

# Check if images loaded
if image1 is None or image2 is None:
    print('Error loading images')
    exit()

# Create SIFT detector
sift = cv2.SIFT_create()

# Detect keypoints and descriptors
kp1, des1 = sift.detectAndCompute(image1, None)
kp2, des2 = sift.detectAndCompute(image2, None)

# Create BFMatcher object
bf = cv2.BFMatcher()

# Match descriptors using k-NN with k=2
matches = bf.knnMatch(des1, des2, k=2)

# Apply ratio test
good_matches = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good_matches.append(m)

# Print number of good matches
print(f'Number of good matches: {len(good_matches)}')

Important Notes

Good matches mean points that likely correspond to the same real-world spot in both images.

Ratio test helps remove false matches by comparing the best and second-best matches.

Feature matching works best on images with enough texture and distinct points.

Summary

Feature matching finds similar points between two images.

Use detectors like SIFT or ORB to get keypoints and descriptors.

Match descriptors and filter matches with ratio test for better accuracy.