- able to handle 360 panorama.
- Random sequence of images input is welcomed.
- use color blending and smoothing to make the image more continuous.
how to run
Prerequisite: matlab 2014b or higher
images sets are already in ./imgs
- If you want to see results directly, go to ./results folder
- If you want to test all images sets with only one click,run RunAllDatasets.m.(10 image sets, about 1 minute)
- If you want to specify the image folder, run main.m with path to images folder as argument as follow:
Note that, this currently support image sets in
imgsfolder. If you use your own image set, you need to set focus length and other parameters in
######details of my algorithms are shown below:
- mapping image to cylindrical coordinate
recognize panorama(random inputs)
I select two random sequence images set:family_house, and west_campus1
They are already shuffled. You can see them in imgs folder.
Or you can run shuffle.bash to shuffle them again.
As described in Brown's paper, I use $N_inlier>k*N_pairs+b$ to compute whether a pair of images match or not
k,b are const. Set to 5.9 and 0.22 respectively.
See recognizing panorama for details
merging and blending
- homography transformation.
- translation transformation.( This is more robust)
- exposure matching
- end to end adjustment(comput shift and subtract shift/n to each image)
- bundle adjustment(difficult way)
- use SIFT features(using VLFeat library, professor allowed)
- SURF features, (SIFT is better)
- I resize image larger than 400 pixel in width
A nice tutorial on panorama I find useful.