function $[$matched$\_$points$1,$ matched$\_$points$2]=$ sift$\_$matching$($image$1,$ image$2)\%$ Convert images to grayscale image$1\_$gray $=$ rgb$2$gray$($image$1)$; image$2\_$gray $=$ rgb$2$gray$($image$2)$; $\%$ Detect keypoints and extract descriptors $[$keypoints$1,$ descriptors$1]=$ sift$($image$1\_$gray$)$; $[$keypoints$2,$ descriptors$2]=$ sift$($image$2\_$gray$)$; $\%$ Match descriptors between the two images matches $=$ matchDescriptors$($descriptors$1,$ descriptors$2)$; $\%$ Extract matched keypoints matched$\_$points$1=$ keypoints$1($matches$($:$,1),1$:$2)$; matched$\_$points$2=$ keypoints$2($matches$($:$,2),1$:$2)$; $\%$ Visualize matched keypoints figure; showMatchedFeatures$($image$1,$ image$2,$ matched$\_$points$1,$ matched$\_$points$2,$ 'montage'$)$; title$(\text{'}$Matched Keypoints'$)$;endfunction matches $=$ matchDescriptors$($descriptors$1,$ descriptors$2)\%$ Initialize variables num$\_$keypoints$1=$ size$($descriptors$1,1)$; num$\_$keypoints$2=$ size$($descriptors$2,1)$; matches $=[]$; $\%$ Define a threshold for matching threshold $=0\mathrm{.}7$; for i $=1$:num$\_$keypoints$1\%$ Compute Euclidean distances between the current descriptor and all descriptors in the second image distances $=$ sqrt$($sum$(($repmat$($descriptors$1($i$,$ :$),$ num$\_$keypoints$2,1)-$ descriptors$2).^2,2))$; $\%$ Find the closest match $[$min$\_$distance, min$\_$index$]=$ min$($distances$)$; $\%$ Check if the ratio test is satisfied $($Lowe$\text{'}$s ratio test$)$ second$\_$min$\_$distance $=$ min$($distances$($setdiff$(1$:num$\_$keypoints$2,$ min$\_$index$)))$; ratio $=$ min$\_$distance $/$ second$\_$min$\_$distance; if ratio $<$ threshold matches $=[$matches; i$,$ min$\_$index$]$; end endend

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