Multimedia Tools and Applications

, Volume 77, Issue 17, pp 22299–22318 | Cite as

Image capture pattern optimization for panoramic photography

  • Yuanhao Guo
  • Rongkai Zhao
  • Song Wu
  • Chao WangEmail author


Panoramic photography requires intensive operations of image stitching. A large quantity of images may lead to a rather expensive image stitching; while a sparse imaging may cause a poor-quality panorama due to the insufficient correlation between adjacent images. So, a good study for the balance between image quantity and image correlation may improve the efficiency and quality of panoramic photography. Therefore, in this work, we are motivated to present a novel approach to estimate the optimal image capture patterns for panoramic photography. We aim at the minimization of the image quantity which still preserves sufficient image correlation. We represent the image correlation as overlap area between the view range that can be separately observed from adjacent images. Moreover, a time-consuming imaging process of panoramic photography will result in a considerable illumination variation of the scene in images. Subsequently, the image stitching will be more challenged. To solve this problem, we design a series of imaging routines for our image capture patterns to preserve the content consistency, ensuring the generalization of our method to various cameras. Experimental results show that the proposed method can obtain the optimal image capture pattern in a very efficient manner. In these patterns, we can obtain a balanced image quantity but still achieve good results of panoramic photography.


Panoramic photography Image capture pattern Image quantity Image correlation Focal length 


  1. 1.
    Aggarwal R, Vohra A, Namboodiri AM (2016) Panoramic stereo videos with a single camera. In: IEEE Conference on computer vision and pattern recognition, pp 3755–3763Google Scholar
  2. 2.
    Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74(1):59–73CrossRefGoogle Scholar
  3. 3.
    Chang C, Chen C, Chuang Y (2014) Spatially-varying image warps for scene alignment. In: International conference on pattern recognition. IEEE, pp 64–69Google Scholar
  4. 4.
    Chapdelaine-Couture V, Roy S (2013) The omnipolar camera: a new approach to stereo immersive capture. In: IEEE Conference on computational photography. IEEE, pp 1–9Google Scholar
  5. 5.
    Galetzka M, Glauner P (2017) A simple and correct even-odd algorithm for the point-in-polygon problem for complex polygons. In: International joint conference on computer vision, imaging and computer graphics theory and applications (VISIGRAPP 2017), vol 1: GRAPPGoogle Scholar
  6. 6.
    Gao Z, Zhang L, Chen M, Hauptmann A, Zhang H, Cai A (2014) Enhanced and hierarchical structure algorithm for data imbalance problem in semantic extraction under massive video dataset. Multimed Tools Appl 68(3):641–657CrossRefGoogle Scholar
  7. 7.
    Gao Z, Zhang H, Xu GP, Xue YB, Hauptmann AG (2015) Multi-view discriminative and structured dictionary learning with group sparsity for human action recognition. Signal Process 112:83–97CrossRefGoogle Scholar
  8. 8.
    Gao Z, Li SH, Zhu YJ, Wang C, Zhang H (2017) Collaborative sparse representation leaning model for rgbd action recognition. J Vis Commun Image RepresentGoogle Scholar
  9. 9.
  10. 10.
    Hormann K, Agathos A (2001) The point in polygon problem for arbitrary polygons. Comput Geom 20(3):131–144MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Kauff P, Eisert P, Schuessler J, Weissig C, Arne F (2016) Capturing panoramic or semi-panoramic 3d scenes. US Patent 9 462:184Google Scholar
  12. 12.
    Kent BR (2017) Spherical panoramas for astrophysical data visualization. Publ Astron Soc Pacific 129(975):058004CrossRefGoogle Scholar
  13. 13.
    Liu A, Nie W, Gao Y, Su Y (2016) Multi-modal clique-graph matching for view-based 3d model retrieval. IEEE Trans Image Process 25(5):2103–2116MathSciNetCrossRefGoogle Scholar
  14. 14.
    Matzen K, Cohen MF, Evans B, Kopf J, Szeliski R (2017) Low-cost 360 stereo photography and video capture. ACM Trans Graph (TOG) 36(4):148CrossRefGoogle Scholar
  15. 15.
    Nie L, Wang M, Zha Z, Chua T (2012) Oracle in image search: a content-based approach to performance prediction. ACM Trans Inf Syst (TOIS) 30 (2):13CrossRefGoogle Scholar
  16. 16.
    Nie W, Liu A, Gao Z, Su Y (2015) Clique-graph matching by preserving global & local structure. In: IEEE Conference on computer vision and pattern recognition, pp 4503–4510Google Scholar
  17. 17.
  18. 18.
    Peleg S, Ben-Ezra M, Pritch Y (2001) Omnistereo: panoramic stereo imaging. IEEE Trans Pattern Anal Mach Intell 23(3):279–290CrossRefzbMATHGoogle Scholar
  19. 19.
    Ramalingam S, Sturm P (2017) A unifying model for camera calibration. IEEE Trans Pattern Anal Mach Intell 39(7):1309–1319CrossRefGoogle Scholar
  20. 20.
    Ryan M (2001) Narrative as virtual reality: immersion and interactivity in literature and electronic media. Johns Hopkins University PressGoogle Scholar
  21. 21.
    Richardt C, Pritch Y, Zimmer H, Sorkine-Hornung A (2013) Megastereo: constructing high-resolution stereo panoramas. In: IEEE Conference on computer vision and pattern recognition, pp 1256–1263Google Scholar
  22. 22.
    Schraml S, Belbachir AN, Bischof H (2016) An event-driven stereo system for real-time 3-d 360panoramic vision. IEEE Trans Ind Electron 63(1):418–428CrossRefGoogle Scholar
  23. 23.
    Sheppard K, Cassella JP, Fieldhouse S (2017) A comparative study of photogrammetric methods using panoramic photography in a forensic context. Forensic Sci Int 273:29–38CrossRefGoogle Scholar
  24. 24.
    Szeliski R (2006) Image alignment and stitching: a tutorial. Foundations and Trends®;, in Computer Graphics and Vision 2(1):1–104MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Szeliski R (2010) Computer vision: algorithms and applications. Springer Science & Business MediaGoogle Scholar
  26. 26.
    Thatte J, Boin J, Lakshman H, Wetzstein G, Girod B (2016) Depth augmented stereo panorama for cinematic virtual reality with focus cues. In: IEEE Conference on image processing. IEEE, pp 1569– 1573Google Scholar
  27. 27.
    Yang Y, Nie F, Xu D, Luo J, Zhuang Y, Pan Y (2012) A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans Pattern Anal Mach Intell 34(4):723–742CrossRefGoogle Scholar
  28. 28.
    Yang Y, Song J, Huang Z, Ma Z, Sebe N, Hauptmann AG (2013) Multi-feature fusion via hierarchical regression for multimedia analysis. IEEE Trans Multimed 15(3):572–581CrossRefGoogle Scholar
  29. 29.
    Yi S, Ahuja N (2006) An omnidirectional stereo vision system using a single camera. In: IEEE Conference on pattern recognition, vol 4. IEEE, pp 861–865Google Scholar
  30. 30.
    Zach C (2014) Robust bundle adjustment revisited. In: European Conference on computer vision. Springer, pp 772–787Google Scholar
  31. 31.
    Zhang H, Shang X, Yang W, Xu H, Luan H, Chua T (2016) Online collaborative learning for open-vocabulary visual classifiers. In: IEEE Conference on computer vision and pattern recognition, pp 2809–2817Google Scholar
  32. 32.
    Zhang H, Wang M, Hong R, Chua T (2016) Play and rewind: optimizing binary representations of videos by self-supervised temporal hashing. In: ACM on multimedia conference. ACM, pp 781–790Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yuanhao Guo
    • 1
  • Rongkai Zhao
    • 1
  • Song Wu
    • 2
  • Chao Wang
    • 1
    • 3
    Email author
  1. 1.MoboPanChangshaChina
  2. 2.College of Computer and Information ScienceSouthwest UniversityChongqingChina
  3. 3.School of Information Science and EngineeringShandong UniversityQingdaoChina

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