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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Light Field Segmentation Using a Ray-Based Graph Structure

verfasst von : Matthieu Hog, Neus Sabater, Christine Guillemot

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

In this paper, we introduce a novel graph representation for interactive light field segmentation using Markov Random Field (MRF). The greatest barrier to the adoption of MRF for light field processing is the large volume of input data. The proposed graph structure exploits the redundancy in the ray space in order to reduce the graph size, decreasing the running time of MRF-based optimisation tasks. Concepts of free rays and ray bundles with corresponding neighbourhood relationships are defined to construct the simplified graph-based light field representation. We then propose a light field interactive segmentation algorithm using graph-cuts based on such ray space graph structure, that guarantees the segmentation consistency across all views. Our experiments with several datasets show results that are very close to the ground truth, competing with state of the art light field segmentation methods in terms of accuracy and with a significantly lower complexity. They also show that our method performs well on both densely and sparsely sampled light fields.

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Vorheriges Kapitel Sparse Recovery of Hyperspectral Signal from Natural RGB Images
Nächstes Kapitel Design of Kernels in Convolutional Neural Networks for Image Classification
Fußnoten
1
This is the approach of [14], published in parallel to this work. They use a full graph structure and as a consequence, the authors report memory and computational time issues and experiment with only 25 of the 81 available views.
 
Literatur
1.
Ng, R., Levoy, M., Brédif, M., Duval, G., Horowitz, M., Hanrahan, P.: Light field photography with a hand-held plenoptic camera. Comput. Sci. Tech. Rep. 2(11), 1–11 (2005)
2.
Lumsdaine, A., Georgiev, T.: The focused plenoptic camera. In: ICCP, pp. 1–8. IEEE (2009)
3.
Zhang, C., Chen, T.: A self-reconfigurable camera array. In: SIGGRAPH Sketches, p. 151. ACM (2004)
4.
Wilburn, B., Joshi, N., Vaish, V., Levoy, M., Horowitz, M.: High-speed videography using a dense camera array. In: CVPR, vol. 2, p. II-294. IEEE (2004)
5.
6.
Tao, M.W., Hadap, S., Malik, J., Ramamoorthi, R.: Depth from combining defocus and correspondence using light-field cameras. In: ICCV, December 2013
7.
Wanner, S., Goldluecke, B.: Globally consistent depth labeling of 4D light fields. In: CVPR, pp. 41–48. IEEE (2012)
8.
Bishop, T.E., Zanetti, S., Favaro, P.: Light field superresolution. In: ICCP, pp. 1–9. IEEE (2009)
9.
Wanner, S., Goldluecke, B.: Variational light field analysis for disparity estimation and super-resolution. PAMI 36(3), 606–619 (2014)CrossRef
10.
Hochbaum, D.S., Singh, V.: An efficient algorithm for co-segmentation. In: ICCV, pp. 269–276. IEEE (2009)
11.
Djelouah, A., Franco, J.S., Boyer, E., Clerc, F., Pérez, P.: Multi-view object segmentation in space and time. In: ICCV, pp. 2640–2647 (2013)
12.
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. PAMI 23(11), 1222–1239 (2001)CrossRef
13.
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient ND image segmentation. IJCV 70(2), 109–131 (2006)CrossRef
14.
Mihara, H., Funatomi, T., Tanaka, K., Kubo, H., Nagahara, H., Mukaigawa, Y.: 4D light-field segmentation with spatial and angular consistencies. In: ICCP (2016)
15.
Wanner, S., Meister, S., Goldluecke, B.: Datasets and benchmarks for densely sampled 4D light fields. In: VMV Workshop, pp. 225–226 (2013)
16.
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47(1–3), 7–42 (2002)CrossRefMATH
17.
18.
Wanner, S., Straehle, C., Goldluecke, B.: Globally consistent multi-label assignment on the ray space of 4D light fields. In: CVPR, pp. 1011–1018. IEEE (2013)
19.
Jarabo, A., Masia, B., Gutierrez, D.: Efficient propagation of light field edits. In: SIACG (2011)
20.
21.
Berent, J., Dragotti, P.L.: Unsupervised extraction of coherent regions for image based rendering. In: BMVC, pp. 1–10 (2007)
22.
Dragotti, P.L., Brookes, M.: Efficient segmentation and representation of multi-view images. In: SEAS-DTC Workshop, Edinburgh (2007)
23.
Berent, J., Dragotti, P.L.: Plenoptic manifolds-exploiting structure and coherence in multiview images. Sig. Process. Mag. 24, 34–44 (2007)CrossRef
24.
Rother, C., Minka, T., Blake, A., Kolmogorov, V.: Cosegmentation of image pairs by histogram matching-incorporating a global constraint into MRFS. In: CVPR, vol. 1, pp. 993–1000. IEEE (2006)
25.
Mukherjee, L., Singh, V., Peng, J.: Scale invariant cosegmentation for image groups. In: CVPR, pp. 1881–1888. IEEE (2011)
26.
Reinbacher, C., Rüther, M., Bischof, H.: Fast variational multi-view segmentation through backprojection of spatial constraints. Image Vis. Comput. 30(11), 797–807 (2012)CrossRef
27.
Campbell, N.D., Vogiatzis, G., Hernández, C., Cipolla, R.: Automatic object segmentation from calibrated images. In: CVMP, pp. 126–137. IEEE (2011)
28.
Sormann, M., Zach, C., Karner, K.: Graph cut based multiple view segmentation for 3D reconstruction. In: 3DPVT, pp. 1085–1092. IEEE (2006)
29.
Gortler, S.J., Grzeszczuk, R., Szeliski, R., Cohen, M.F.: The lumigraph. In: SIGGRAPH, pp. 43–54. ACM (1996)
30.
Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? PAMI 26(2), 147–159 (2004)CrossRef
31.
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. PAMI 26(9), 1124–1137 (2004)CrossRefMATH
32.
Dal Mutto, C., Zanuttigh, P., Cortelazzo, G.M.: Scene segmentation by color and depth information and its applications. University of Padova (2010)
33.
Mutto, C.D., Zanuttigh, P., Cortelazzo, G.M.: Fusion of geometry and color information for scene segmentation. J-STSP 6(5), 505–521 (2012)
34.
Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. TOG 23, 309–314 (2004). ACMCrossRef
35.
Bilmes, J.A., et al.: A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. ICSI 4(510), 126 (1998)
36.
Harville, M., Gordon, G., Woodfill, J.: Foreground segmentation using adaptive mixture models in color and depth. In: Workshop on Detection and Recognition of Events in Video, pp. 3–11. IEEE (2001)
37.
Hasnat, M.A., Alata, O., Trémeau, A.: Unsupervised RGB-D image segmentation using joint clustering and region merging. J-STSP 6(5), 505–521 (2012)
38.
Drazic, V., Sabater, N.: A precise real-time stereo algorithm. In: IVCNZ, pp. 138–143. ACM (2012)
39.
Yang, T., Zhang, Y., Yu, J., Li, J., Ma, W., Tong, X., Yu, R., Ran, L.: All-in-focus synthetic aperture imaging. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 1–15. Springer, Heidelberg (2014)
40.
Vineet, V., Narayanan, P.: CUDA cuts: Fast graph cuts on the gpu. In: CVPR, pp. 1–8. IEEE (2008)
41.
Bishop, T.E., Favaro, P.: Plenoptic depth estimation from multiple aliased views. In: ICCV Workshops, pp. 1622–1629. IEEE (2009)
Metadaten
Titel
Light Field Segmentation Using a Ray-Based Graph Structure
verfasst von
Matthieu Hog
Neus Sabater
Christine Guillemot
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46477-0

Electronic ISBN: 978-3-319-46478-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46478-7_3