Abstract
This paper presents the application of a depth estimation method for scenes acquired using a Synthetic Aperture Integral Imaging (SAII) technique. SAII is an autostereoscopic technique consisting of an array of cameras that acquires images from different perspectives. The depth estimation method combines a defocus and a correspondence measure. This approach obtains consistent results and shows noticeable improvement in the depth estimation as compared to a minimum variance minimisation strategy, also tested in our scenes. Further improvements are obtained for both methods when they are fed into a regularisation approach that takes into account the depth in the spatial neighbourhood of a pixel.
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Acknowledgments
This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the projects SEOSAT (ESP2013-48458-C4-3-P) and MTM2013-48371-C2-2-P, by the Generalitat Valenciana through the project PROMETEO-II-2014-062, and by the University Jaume I through the project UJI-P11B2014-09. B. Javidi would like to acknowledge support under NSF/IIS-1422179.
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Espinos-Morato, H., Latorre-Carmona, P., Martinez Sotoca, J., Pla, F., Javidi, B. (2017). Combining Defocus and Photoconsistency for Depth Map Estimation in 3D Integral Imaging. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_13
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DOI: https://doi.org/10.1007/978-3-319-58838-4_13
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