This document presents an image stitching algorithm that uses SURF and RANSAC methods. SURF finds interest points and descriptors in images, and RANSAC obtains a homography to project multiple images onto a single plane. The images are then blended together to create a single stitched image. This algorithm can be applied to tasks like medical imaging, microscopy, remote sensing, astronomy, and materials science.

1. An Image Stitching Algorithm
Based On
SURF And RANSAC Methods

2. Main Points
•SURF algorithm
•RANSAC method for obtaining a
homography
•Image blending process

3. Image Analysis Applications
• Medicine (MRI scans)
• Microscopy
• Remote sensing (detecting intruders in a
house, and producing land cover/land use
maps)
• Astronomy (calculating the size of a
planet)
• Materials science
• Many others 

4. SURF Algorithm
SURF finds interest points and builds their
descriptors

5. After finding features they are matched
between different images
SURF Algorithm

6. RANdom SAmple Consensus
Simple 2D example

7. RANSAC is used to obtain a homography to project
a set of images onto one plane
RANdom SAmple Consensus

8. Image Blending
Now we just need to unite our images

9. Image Stitching
•SURF algorithm finds image features
•RANSAC obtains a projective
transformation
•Projected images blended into a
single image

10. An image stitching algorithm
on basis of
SURF and RANSAC methods