1. Image stitching for
panoramic view using
optical flow
Nick Chehade, JosephLee,andDezhenSong

2. System Design
2. Key Frame
Selection
2.1 Compute
Homography
2.2 Select Key
Frame
4. Texture Mapping
4.1 Texture
Map Panorama
to Cylinder
3. Stitching
3.3 Cylinder to
Panorama
Texture
3.1 Compute
Camera Pose
3.2 Project
Image to
Cylinder
1. Feature Detection
and Matching
1.1 Shi-Tomasi
Corner
Detection
1.2 Optical
Flow Matching
Video Stream
3D Panoramic View

3. Camera Calibration
 Camera has unique intrinsic parameters
 Must calibrate to obtain K matrix and undistort images

4. Feature Detection and Matching
 Shi-Tomasi Corner Detection

5. Feature Detection and Matching
 Track features using optical flow
 Least sum of squares flow of gradient among frames

6.  Must choose frame I’ that overlaps well with previous frame I
 When tracking n features, once the successfully tracked
features n’ in the current frame is less than n/2, save I’
Key Frame Selection

7. Image Stitching
 Homography H is a projective transformation that relates
image points x’ = Hx, x and x’ are image points in I and I’
 From H and K, the camera rotation R is estimated
 Image point x in the world space of the cylinder is X= K-1R-1x
where X= (x,y,z)
 World coordinates mapped to UV texture coordinates, u =
arctan(x/z) and v = y/√(x2 + z2)
 Texture map cylinder x = r*cos(u), y = v, z = r*sin(u), r is the
radius of the cylinder.

8. Image Stitching
Stitched images with planar projection

9. Image Stitching
Stitched images with cylindrical projection

10. Results

11. Results
Shi-Tomasi and Optical Flow SIFT and FLANN

12. Questions