# NISwGSP **Repository Path**: awk_bushin95/NISwGSP ## Basic Information - **Project Name**: NISwGSP - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2022-01-10 - **Last Updated**: 2022-01-10 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Natural Image Stitching with the Global Similarity Prior Ubuntu: [![Build Status](https://www.travis-ci.org/Yannnnnnnnnnnn/NISwGSP.svg?branch=master)](https://www.travis-ci.org/Yannnnnnnnnnnn/NISwGSP) ### [[Project page]](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/) [[Paper]](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/ECCV-2016-NISwGSP.pdf) [[Supplementary]](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/ECCV-2016-NISwGSP-supplementary-material.pdf) 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. ### Download 1. [Poster](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/Poster.pdf), [Short Presentation](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/Short-Presentation.pdf) and [Thesis Presentation](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/Thesis-Presentation.pdf) 2. [Paper](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/ECCV-2016-NISwGSP.pdf) 3. [Supplementary](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/ECCV-2016-NISwGSP-supplementary-material.pdf) * 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](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/input-42-data.zip) 5. [All our results](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/0_results.zip) ### Building 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" ### Usage 1. Download code and compile. * You need **Eigen**, **VLFeat**, **OpenCV 3.0.0** and [**OpenMP**](https://github.com/nothinglo/NISwGSP/issues/8) (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](http://www.cmlab.csie.ntu.edu.tw/project/stitching-wGSP/input-42-data.zip). * 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). ![workding](https://github.com/Yannnnnnnnnnnn/NISwGSP/blob/master/UglyMan_NISwGSP_Stitching/UglyMan_NISwGSP_Stitching/Screenshot%20from%202017-12-27%2022-27-39.bmp) 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. ![exe](https://github.com/Yannnnnnnnnnnn/NISwGSP/blob/master/UglyMan_NISwGSP_Stitching/UglyMan_NISwGSP_Stitching/Screenshot%20from%202017-12-27%2022-28-08.bmp) 5. Optional: * You can control the parameters in **Configure.h** or **xxx-STITCH-GRAPH.txt** ### Results For More Results please look to https://github.com/nothinglo/NISwGSP ### Speed If you want to speed up, **MATLAB** solver is significantly faster than **Eigen**. ### Publication [Yu-Sheng Chen](http://www.cmlab.csie.ntu.edu.tw/~nothinglo/) and [Yung-Yu Chuang](http://www.csie.ntu.edu.tw/~cyy/). [National Taiwan University](http://www.ntu.edu.tw) 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. ### Citation ``` @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}, } ``` ### Reference > 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)* ### Contact Feel free to contact me if there is any question (Yu-Sheng Chen nothinglo@cmlab.csie.ntu.edu.tw).