Ubuntu:

This repository is our C++ implementation of the ECCV 2016 paper, Natural Image Stitching with the Global Similarity Prior. If you use any code or data from our work, please cite our paper.

1. Poster, Short Presentation and Thesis Presentation
2. Paper
3. Supplementary
   • We tested four state-of-the-art methods and ours on 42 sets of images in same setting (grid size, feature points and parameters).
4. Input-42-data
5. All our results

1. cd to the ./vlfeat-0.9.20 and build the vlfeat; in ubuntu, "make" is enough for this task
2. use the CMake to configure the project and make sure set the VLFEAT_LIBRARY with the "./vlfeat-0.9.20/bin/***(the name depend on you system)/libvl"
3. mkdir build
4. cd build && cmake .. && make
5. BE AWARE THE DEAFULT BUILD-TYPE IS "debug"

1. Download code and compile.

   • You need Eigen, VLFeat, OpenCV 3.0.0 and OpenMP (if you don't need to use omp.h, you can ignore it.)
   • My GCC_VRSION is Apple LLVM 6.0

   GCC_C_LANGUAGE_STANDARD = GNU99 [-std=gnu99]
   CLANG_CXX_LANGUAGE_STANDARD = GNU++14 [-std=gnu++14]
   CLANG_CXX_LIBRARY = libc++ (LLVM C++ standard library with C++11 support)
   

   • My Eigen version is 3.2.7 (development branch). You need to make sure you can use "LeastSquaresConjugateGradient" class.

2. Download input-42-data.

   • 42 sets of images: 6 from [1], 3 from [2], 3 from [3], 7 from [4], 4 from [5] and 19 collected by ourselves.

3. Move [input-42-data] folder to your working directory(the working directory is where the executable file is).

4. Run the command:

   ./exe folder_name_in_[input-42-data]_folder
   

example:

```
./NISwGSP AANAP-building
```

The results can be found in [0_results] folder under [input-42-data] folder.

5. Optional:
   • You can control the parameters in Configure.h or xxx-STITCH-GRAPH.txt

For More Results please look to https://github.com/nothinglo/NISwGSP

If you want to speed up, MATLAB solver is significantly faster than Eigen.

Yu-Sheng Chen and Yung-Yu Chuang.

National Taiwan University

Natural Image Stitching with Global Similarity Prior. Proceedings of European Conference on Computer Vision 2016 (ECCV 2016), Part V, pp. 186-201, October 2016, Amsterdam, Netherland.

@INPROCEEDINGS{Chen:2016:NIS,
	AUTHOR		= {Yu-Sheng Chen and Yung-Yu Chuang},
	TITLE		= {Natural Image Stitching with the Global Similarity Prior}, 
	YEAR		= {2016},
	MONTH		= {October},
	BOOKTITLE	= {Proceedings of European Conference on Computer Vision (ECCV 2016)},
	PAGES		= {V186--201},
	LOCATION	= {Amsterdam},
}

1. Chang, C.H., Sato, Y., Chuang, Y.Y.: Shape-preserving half-projective warps for image stitching. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp. 3254-3261. CVPR'14 (2014)
2. Gao, J., Kim, S.J., Brown, M.S.: Constructing image panoramas using dual-homography warping. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. pp. 49-56. CVPR'11 (2011)
3. Lin, C., Pankanti, S., Ramamurthy, K.N., Aravkin, A.Y.: Adaptive as-natural-as-possible image stitching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015. pp. 1155-1163 (2015)
4. Nomura, Y., Zhang, L., Nayar, S.K.: Scene collages and flexible camera arrays. In: Proceedings of the 18th Eurographics Conference on Rendering Techniques. pp. 127-138. EGSR'07 (2007)
5. Zaragoza, J., Chin, T.J., Brown, M.S., Suter, D.: As-projective-as-possible image stitching with moving dlt. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. pp. 2339-2346. CVPR'13 (2013)

Feel free to contact me if there is any question (Yu-Sheng Chen [email protected]).