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Image capture pattern optimization for panoramic photography

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Abstract

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.

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References

  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 3755–3763

  2. Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74(1):59–73

    Article  Google Scholar 

  3. Chang C, Chen C, Chuang Y (2014) Spatially-varying image warps for scene alignment. In: International conference on pattern recognition. IEEE, pp 64–69

  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 1–9

  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: GRAPP

  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):641–657

    Article  Google Scholar 

  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 112:83–97

    Article  Google Scholar 

  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 Represent

  9. Google jump. https://vr.google.com/jump/

  10. Hormann K, Agathos A (2001) The point in polygon problem for arbitrary polygons. Comput Geom 20(3):131–144

    Article  MathSciNet  MATH  Google Scholar 

  11. Kauff P, Eisert P, Schuessler J, Weissig C, Arne F (2016) Capturing panoramic or semi-panoramic 3d scenes. US Patent 9 462:184

    Google Scholar 

  12. Kent BR (2017) Spherical panoramas for astrophysical data visualization. Publ Astron Soc Pacific 129(975):058004

    Article  Google Scholar 

  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):2103–2116

    Article  MathSciNet  Google Scholar 

  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):148

    Article  Google Scholar 

  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):13

    Article  Google Scholar 

  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 4503–4510

  17. Panono camera. https://www.panono.com/en/

  18. Peleg S, Ben-Ezra M, Pritch Y (2001) Omnistereo: panoramic stereo imaging. IEEE Trans Pattern Anal Mach Intell 23(3):279–290

    Article  MATH  Google Scholar 

  19. Ramalingam S, Sturm P (2017) A unifying model for camera calibration. IEEE Trans Pattern Anal Mach Intell 39(7):1309–1319

    Article  Google Scholar 

  20. Ryan M (2001) Narrative as virtual reality: immersion and interactivity in literature and electronic media. Johns Hopkins University Press

  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 1256–1263

  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):418–428

    Article  Google Scholar 

  23. Sheppard K, Cassella JP, Fieldhouse S (2017) A comparative study of photogrammetric methods using panoramic photography in a forensic context. Forensic Sci Int 273:29–38

    Article  Google Scholar 

  24. Szeliski R (2006) Image alignment and stitching: a tutorial. Foundations and Trends®;, in Computer Graphics and Vision 2(1):1–104

    Article  MathSciNet  MATH  Google Scholar 

  25. Szeliski R (2010) Computer vision: algorithms and applications. Springer Science & Business Media

  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– 1573

  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):723–742

    Article  Google Scholar 

  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):572–581

    Article  Google Scholar 

  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 861–865

  30. Zach C (2014) Robust bundle adjustment revisited. In: European Conference on computer vision. Springer, pp 772–787

  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 2809–2817

  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 781–790

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Correspondence to Chao Wang.

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Yuanhao Guo and Chao Wang are equally contributed.

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Guo, Y., Zhao, R., Wu, S. et al. Image capture pattern optimization for panoramic photography. Multimed Tools Appl 77, 22299–22318 (2018). https://doi.org/10.1007/s11042-018-5948-y

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  • DOI: https://doi.org/10.1007/s11042-018-5948-y

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