Image feature points matching with RANSAC algorithm
Image blending with matched feature points
3. Intermediate Results
1) Image feature extraction with SIFT algorithm
relevant code: MySift.h and MySift.cpp
results of key feature points (each with a feature descriptor of 128 dimention) of two images:
2) Image feature points matching with RANSAC algorithm
relevant code: MyMatching.h and MyMatching.cpp
First do a coarse-grained feature points matching by calculating the distance of two feature descriptors, and regard the two points as matched if the distance is lower than some threshold. The matched points are lined together as shown below:
Clearly there exist many outliers, which can be removed by RANSAC algorithm as shown below. The algorithm works on selecting the main transforming direction with most inliers:
Removed the outliers which are conflicted with the selected transforming direction:
3) Image blending with matched feature points
relevant code: MyBlending.h and MyBlending.cpp
First use a simple translation method:
becomes
Then apply a RGB interpolation at fusion region A/B:
Stitched Result of two images
Repeat this procedure and get the stitched Result of all images
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