Panoramic Image Stitching with Efficient Brightness Fusion Using RANSAC Algorithm

  • Authors

    • Jungpil Shin
    • Md Abdur Rahim
    • Keun Soo Yun
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.34.18981
  • Panorama, Brightness Fusion, Image stitching, RANSAC Algorithm, Homography

  • Background/Objectives: Image stitching can enhance the picture very pleasant by modifying and mixing the different aspects.Therefore, we present panoramic image stitching with efficient brightness fusion which is challenging in different bright sequences taken from different angles.

    Methods/Statistical analysis:For the problem of brightness, the input image is mixed with sequential images in different brightness.In this works, we proposed atechnique that blends multiple brightness using simple quality measures like color, saturation and contrast.The resulting image quality is good, and most important thing is, the method is efficient since it is simple. Thanthe resulting fused images is applied for panorama image stitching. We used multiband blending to prevent the blurring, and BRIEF (binary robust independent elementary features) method for feature descriptors. We solved the multi-image matching problem using Hamming Distanceusing the binary string based descriptors which is most similar features compare with the second most similar images.We proposed FLANN based matcher to get the more accurate results for using large datasets. We estimate Homography with the matching images using RANSAC algorithms.

    Findings:An effective structure is performed when we are able to resolve the brightness correction in expose too much or expose for too short a time and the appearance ghost. To solve the unification of brightness, we have collected the input images in different exposures, and selection of the good parts of each picture to an input image for stitching.We removed the blurring from input images, and solved multi-image matching using Hamming distance method. We found better results comparing other methods. For large dataset, we used FLANN based matcher, and estimated the Homography using RANSAC algorithm.

    Improvements/Applications:We have shown the performance of panoramic image stitching with efficient brightness fusion. We performed stitching with high regulationimages. Finally, we were able to create a panoramic image with efficient brightness fusion.

     

     

  • References

    1. [1] Pranoti Kale and K.R. Singh (2015). ‘A Technical Analysis of Image Stitching Algorithm’, ISCIT, vol. 6, no. 1. Retrieved fromhttps://pdfs.semanticscholar.org/3554/0a11cb0d04ae53ea9c793b675a5b68eac768.pdf

      [2] Chengming Zou, Pei Wu and Zeqian Xu (2017). "Research on Seamless Image Stitching based on Depth Map." In ICPRAM, pp. 341-350. Retrieved from http://www.scitepress.org/Papers/2017/61463/61463.pdf

      [3] Shimon Daniel Cohen, Noga Zieber, Rotem Littman, and Udy Danino (2017). "System and method for panoramic image processing." U.S. Patent Application, 15/115, 381 Retrieved from https://patents.google.com/patent/US20170011488A1/en

      [4] Ebtsam Adetet. al. (2014), “Image Stitching based on Feature Extraction Techniques: A Surveyâ€, International Journal of Computer Applications, vol 99, no 6, 2014. Retrieved from https://pdfs.semanticscholar.org/c859/ad210de8965b12bb1c9b5e2b34ad2aa4e964.pdf

      [5] Parul. M. Jain, Prof. Vijaya. K. Shandliya (2003), “A Review Paper on Various Approaches for Image Mosaicingâ€, International Journal of Computational Engineering Research, vol 3, no. 4, pp. 106-109 Retrieved from https://pdfs.semanticscholar.org/a0e3/7be2d69719e38ead32d813d3b8d5362118ff.pdf

      [6] Matthew Brown and David G. Lowe (2007). "Automatic panoramic image stitching using invariant features." International journal of computer visionvol. 74, no. 1, pp. 59-73. Retrieved from https://link.springer.com/article/10.1007/s11263-006-0002-3

      [7] Zhicheng Wang, Yufei Chen, Zewei Zhu, and Weidong Zhao (2016). "An automatic panoramic image mosaic method based on graph model." Multimedia Tools and Applications, vol. 75, no. 5, pp. 2725-2740. Retrieved fromhttps://link.springer.com/article/10.1007/s11042-015-2619-0

      [8] S. Pravenaa and R. Menaka (2016). "A methodical review on image stitching and video stitching techniques." International Journal of Applied Engineering Research, vol. 11, no. 5, pp. 3442-3448 Retrieved fromhttp://www.ripublication.com/ijaer16/ijaerv11n5_80.pdf

      [9] Shreyas Mistry and Arpita Patel (2016). "Image Stitching using Harris Feature Detection." International Research Journal of Engineering and Technology (IRJET), vol. 3, no. 04, pp. 2220 - 2226 Retrieved from https://irjet.net/archives/V3/i4/IRJET-V3I4270.pdf

      [10] David G. Lowe (2004). “Distinctive Image Features from Scale Invariant Key-pointsâ€, International Journal of Computer Vision, vol. 60 Retrieved from https://www.robots.ox.ac.uk/~vgg/research/affine/det_eval_files/lowe_ijcv2004.pdf

      [11] Weijie Huang, and Xiaowei Han (2013). "An improved RANSAC algorithm of color image stitching." In Proceedings of 2013 Chinese intelligent automation Conference, pp. 21-28. Springer, Berlin, Heidelberg, 2013. Retrieved fromhttps://link.springer.com/chapter/10.1007/978-3-642-38466-0_3

      [12] Jing Zhang, Guangxue Chen and Zhaoyang Jia (2017). "An image stitching algorithm based on histogram matching and SIFT algorithm." International Journal of Pattern Recognition and Artificial Intelligence, vol. 31, no. 04, pp. 1754006. Retrieved from http://www.worldscientific.com/doi/abs/10.1142/S0218001417540064

      [13] Diptiben Patel, Bhoomika Sonane, and Shanmuganathan Raman (2017). "Multi-exposure Image Fusion Using Propagated Image Filtering." In Proceedings of International Conference on Computer Vision and Image Processing, pp. 431-441. Retrieved from https://link.springer.com/chapter/10.1007/978-981-10-2104-6_39

      [14] Yu Tang and Huiyan Jiang (2009). "Highly efficient image stitching based on energy map." In Image and Signal Processing, CISP'09. 2nd International Congress on, pp. 1-5. Retrieved fromhttp://ieeexplore.ieee.org/abstract/document/5304214/

      [15] Yu Tang and Jungpil Shin (2010). "De-ghosting for image stitching with automatic content-awareness." In Pattern Recognition (ICPR), 20th International Conference on, pp. 2210-2213. Retrieved from http://ieeexplore.ieee.org/abstract/document/5595971/

      [16] Yu Tang and Jungpil Shin (2014). "Image stitching with efficient brightness fusion and automatic content awareness." In Signal Processing and Multimedia Applications (SIGMAP), International Conference on IEEE, pp. 60-66. Retrieved fromhttp://ieeexplore.ieee.org/abstract/document/7514477/

      [17] Yuanzhen Li, Lavanya Sharan and Edward H. Adelson (2005). “Compressing and Companding High Dynamic Range Images with Subband Architecturesâ€, ACM Transactions on Graphics, vol. 24, no. 3, pp. 836-844 Retrieved from http://www.mit.edu/~yzli/hdr05.pdf

      [18] "PASSTA Datasets", Retrieved fromhttp://www.cvl.isy.liu.se/en/research/datasets/passta/.

      [19] M. A. Fischler, R. C. Bolles (1981). “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartographyâ€, Comm. of the ACM, vol 24, pp 381-395.Retrieved fromhttps://www.sri.com/sites/default/files/publications/ransac-publication.pdf

  • Downloads

  • How to Cite

    Shin, J., Abdur Rahim, M., & Soo Yun, K. (2018). Panoramic Image Stitching with Efficient Brightness Fusion Using RANSAC Algorithm. International Journal of Engineering & Technology, 7(3.34), 267-272. https://doi.org/10.14419/ijet.v7i3.34.18981