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
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
Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74(1):59–73
Chang C, Chen C, Chuang Y (2014) Spatially-varying image warps for scene alignment. In: International conference on pattern recognition. IEEE, pp 64–69
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
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
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
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
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
Google jump. https://vr.google.com/jump/
Hormann K, Agathos A (2001) The point in polygon problem for arbitrary polygons. Comput Geom 20(3):131–144
Kauff P, Eisert P, Schuessler J, Weissig C, Arne F (2016) Capturing panoramic or semi-panoramic 3d scenes. US Patent 9 462:184
Kent BR (2017) Spherical panoramas for astrophysical data visualization. Publ Astron Soc Pacific 129(975):058004
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
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
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
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
Panono camera. https://www.panono.com/en/
Peleg S, Ben-Ezra M, Pritch Y (2001) Omnistereo: panoramic stereo imaging. IEEE Trans Pattern Anal Mach Intell 23(3):279–290
Ramalingam S, Sturm P (2017) A unifying model for camera calibration. IEEE Trans Pattern Anal Mach Intell 39(7):1309–1319
Ryan M (2001) Narrative as virtual reality: immersion and interactivity in literature and electronic media. Johns Hopkins University Press
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
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
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
Szeliski R (2006) Image alignment and stitching: a tutorial. Foundations and Trends®;, in Computer Graphics and Vision 2(1):1–104
Szeliski R (2010) Computer vision: algorithms and applications. Springer Science & Business Media
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
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
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
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
Zach C (2014) Robust bundle adjustment revisited. In: European Conference on computer vision. Springer, pp 772–787
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
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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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