IEEE Signal Processing - March 2018 - 98

[36] C. Song, Z. Liu, Y. Wang, X. Li, and G. Hu, "Multi-waveform classification for
seismic facies analysis," Computers Geosci., vol. 101, pp. 1-9, Apr. 2017.

[59] J. Lomask, R. G. Clapp, and B. Biondi, "Application of image segmentation to
tracking 3D salt boundaries," Geophysics, vol. 72, no. 4, pp. P47-P56, 2007.

[37] T. Zhao, V. Jayaram, A. Roy, and K. J. Marfurt, "A comparison of classification
techniques for seismic facies recognition," Interpretation, vol. 3, no. 4, pp. SAE29-
SAE58, 2015.

[60] R. Zabih and V. Kolmogorov, "Spatially coherent clustering using graph cuts,"
in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition,
27 June-2 July 2004, Washington, D.C., 2004, pp. 437-444.

[38] P. Meldahl, R. Heggland, B. Bril, and P. de Groot, "The chimney cube, an example of semi-automated detection of seismic objects by directive attributes and neural networks: Part I; methodology," in Proc. Society Exploration Geophysicists Tech.
Program Expanded Abstracts, 1999, pp. 931-934.

[61] L. Vincent and P. Soille, "Watersheds in digital spaces: An efficient algorithm
based on immersion simulations," IEEE Trans. Pattern Anal. Mach. Intell., vol. 13,
no. 6, pp. 583-598, June 1991.

[39] H. Hashemi, D. Tax, R. Duin, A. Javaherian, and P. De Groot, "Gas chimney
detection based on improving the performance of combined multilayer perceptron and
support vector classifier," Nonlinear Processes Geophys., vol. 15, no. 6, pp. 863,
2008.
[40] D. Singh, P. C. Kumar, and K. Sain, "Interpretation of gas chimney from seismic
data using artificial neural network: A study from MAARI 3D prospect in the Taranaki
basin, New Zealand," J. Natural Gas Sci. Eng., vol. 36, pp. 339-357, Dec. 2016.
[41] W. Xiong, Z. Wan, X. Bai, H. Xing, H. Zuo, K. Zhu, and S. Yang, "Adaboostbased multi-attribute classification technology and its application," in Proc. 76th
European Association Geoscientists Engineers Conf. and Exhibition, 2014. doi:
10.3997/2214-4609.20141200.
[42] P. Xu, W. Lu, and B. Wang, "Multi-attribute classification based on sparse autoencoder: A gas chimney detection example," in Proc. 79th European Association
Geoscientists and Engineers Conf. and Exhibition, 2017. doi: 10.3997/22144609.201700920.
[43] H. Zhu, F. Meng, J. Cai, and S. Lu. (2016). Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation, J. Vis.
Commun. Image Represent., vol. 34, pp. 12-27. [Online]. Available: http://dx.doi
.org/10.1016/j.jvcir.2015.10.012
[44] E. Shelhamer, J. Long, and T. Darrell, "Fully convolutional networks for semantic
segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640-651,
Apr. 2017.
[45] A. Bansal, X. Chen, B. Russell, A. Gupta, and D. Ramanan. (2016). PixelNet:
Towards a general pixel-level architecture. [Online]. Available: http://arxiv.org/
abs/1609.06694
[46] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille,
"DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs," arXiv Preprint 1606.00915, June 2016.
[47] C. Farabet, C. Couprie, L. Najman, and Y. LeCun, "Learning hierarchical features
for scene labeling," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1915-
1929, Aug. 2013.
[48] (2015). LANDMASS: Large north-sea dataset of migrated aggregated seismic
structures, CeGP. [Online]. Available: http://cegp.ece.gatech.edu/codedata/
LANDMASS/default.htm
[49] (1987). dGB Earth Sciences, The Netherlands Offshore, The North Sea, F3 Block -
Complete. [Online]. Available: https://opendtect.org/osr/pmwiki.php/Main/
Netherlands/OffshoreF3BlockComplete4GB
[50] Y. Alaudah and G. AlRegib, "Weakly-supervised labeling of seismic volumes
using reference exemplars," in Proc. IEEE Int. Conf. Image Processing, Sept. 2016,
pp. 4373-4377.
[51] Z. Long, et al., "A comparative study of texture attributes for characterizing subsurface structures in migrated seismic volumes," CeGP Tech. Rep., 2017. [Online].
Available: https://ghassanalregibdotcom.files.wordpress.com/2018/01/long2017_
survey.pdf
[52] M. Alfarraj, Y. Alaudah, Z. Long, G. AlRegib, and M. Deriche, "A survey of
multiresolution texture attributes for computational seismic interpretation," CeGP
Tech. Rep., 2017. [Online]. Available: https://ghassanalregibdotcom.files.wordpress.
com/2018/01/alfarraj2017_survey.pdf

[62] A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K.
Siddiqi, "Turbopixels: Fast superpixels using geometric flows," IEEE Trans.
Pattern Anal. Mach. Intell., vol. 31, no. 12, pp. 2290-2297, Dec. 2009.
[63] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Ssstrunk, "SLIC
superpixels compared to state-of-the-art superpixel methods," IEEE Trans. Pattern
Anal. Mach. Intell., vol. 34, no. 11, pp. 2274-2282, Nov. 2012.
[64] Y. Alaudah and G. AlRegib. (2017). A weakly supervised approach to seismic
structure labeling, in Proc. Society Exploration Geophysicists Tech. Program
Expanded Abstracts, pp. 2158-2163. [Online]. Available: http://library.seg.org/doi/
abs/10.1190/segam2017-17793533.1
[65] D. Lee and H. Seung. (2001). Algorithms for non-negative matrix factorization,
Adv. Neural Inform. Process. Syst., no. 1, pp. 556-562. [Online]. Available: http://
papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization
[66] C. Ding, X. He, and H. D. Simon. (2005). On the equivalence of nonnegative
matrix factorization and spectral clustering, in Proc. 5th Society Indian Automobile
Manufactures Int. Conf. Data Mining, no. 4, pp. 606-610. [Online]. Available:
http://www.siam.org/proceedings/datamining/2005/dm05.php
[67] D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative
matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, 1999.
[68] P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints," J.
Mach. Learn. Res., vol. 5, pp. 1457-1469, Nov. 2004.
[69] A. Waldeland and A. Solberg, "Salt classification using deep learning," in
Proc. 79th European Association Geoscientists and Engineers Conf. and
Exhibition, 2017. doi: 10.3997/2214-4609.201700918.
[70] L. Huang, X. Dong, and T. E. Clee, "A scalable deep learning platform for
identifying geologic features from seismic attributes," Leading Edge, vol. 36, no. 3,
pp. 249-256, 2017.
[71] K. Hami-Eddine, B. de Ribert, P. Durand, and P. Gascue, "A growing machine
learning approach to optimize use of prestack and poststack seismic data," in Proc.
Society Exploration Geophysicists Tech. Program Expanded Abstracts, 2017, pp.
2149-2152.
[72] M. Araya-Polo, T. Dahlke, C. Frogner, C. Zhang, T. Poggio, and D. Hohl,
"Automated fault detection without seismic processing," Leading Edge, vol. 36, no.
3, pp. 208-214, 2017.
[73] Y. Lin, G. Guthrie, D. Coblentz, S. Wang, and J. Thiagarajan. (2017). Towards
real-time geologic feature detection from seismic measurements using a randomized
machine-learning algorithm, pp. 2143-2148. [Online]. Available: https://library.seg
.org/doi/abs/10.1190/segam2017-17775517.1
[74] K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber,
"LSTM: A search space odyssey," in Proc. IEEE Trans. Neural Networks and
Learning Systems, 2016, pp. 2222-2232.
[75] A. Graves, G. Wayne, and I. Danihelka, "Neural turing machines," arXiv
Preprint, arXiv:1410.5401, 2014.
[76] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, "Unpaired image-to-image translation using cycle-consistent adversarial networks," arXiv Preprint, arXiv:1703.10593,
2017.
[77] C. Ledig et al. "Photo-realistic single image super-resolution using a generative
adversarial network," arXiv Preprint, arXiv:1609.04802, 2016.

[53] J. Zujovic, T. N. Pappas, and D. L. Neuhoff, "Structural texture similarity metrics for image analysis and retrieval," IEEE Trans. Image Process., vol. 22, no. 7,
pp. 2545-2558, 2013.

[78] A. Radford, L. Metz, and S. Chintala, "Unsupervised representation learning
with deep convolutional generative adversarial networks," arXiv Preprint,
arXiv:1511.06434, 2015.

[54] Y. Alaudah and G. AlRegib, "A curvelet-based distance measure for seismic
images," in Proc. IEEE Int. Conf. Image Processing, 2015, pp. 4200-4204.

[79] W. Lotter, G. Kreiman, and D. Cox, "Unsupervised learning of visual structure using predictive generative networks," arXiv Preprint, arXiv:1511.06380,
2015.

[55] M. Alfarraj, Y. Alaudah, and G. AlRegib, "Content-adaptive non-parametric
texture similarity measure," in Proc. IEEE 18th Int. Workshop Multimedia Signal
Processing, 2016, pp. 1-6.

[80] I. Goodfellow, "NIPS 2016 tutorial: Generative adversarial networks," arXiv
Preprint, arXiv:1701.00160, 2016.

[56] J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans.
Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000.

[81] J. T. Springenberg, "Unsupervised and semi-supervised learning with categorical generative adversarial networks," arXiv Preprint, arXiv:1511.06390, 2015.

[57] A. D. Halpert, R. G. Clapp, and B. Biondi, "Salt delineation via interpreterguided 3D seismic image segmentation," Interpretation, vol. 2, no. 2, pp. T79-T88,
2014.

[82] M. Shafiq, T. Alshawi, Z. Long, and G. AlRegib, "The role of visual saliency
in the automation of seismic interpretation," Geophys. Prospect., 2017. [Online].
Available: http://onlinelibrary.wiley.com/doi/10.1111/1365-2478.12570/full. doi.
10.1111/1365-2478.12570.

[58] D. Hale and J. Emanuel. (2005). Seismic interpretation using global image segmentation, pp. 2410-2413. [Online]. Available: http://library.seg.org/doi/
abs/10.1190/1.1817860

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http://library.seg.org/doi/abs/10.1190/segam2017-17793533.1 http://library.seg.org/doi/abs/10.1190/segam2017-17793533.1 http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization http://www.siam.org/proceedings/datamining/2005/dm05.php http://www.dx.doi.org/10.1016/j.jvcir.2015.10.012 http://www.dx.doi.org/10.1016/j.jvcir.2015.10.012 http://www.arxiv.org/abs/1609.06694 http://www.arxiv.org/abs/1609.06694 http://cegp.ece.gatech.edu/codedata/LANDMASS/default.htm http://cegp.ece.gatech.edu/codedata/LANDMASS/default.htm https://opendtect.org/osr/pmwiki.php/Main/Netherlands/OffshoreF3BlockComplete4GB https://library.seg.org/doi/abs/10.1190/segam2017-17775517.1 https://opendtect.org/osr/pmwiki.php/Main/Netherlands/OffshoreF3BlockComplete4GB https://library.seg.org/doi/abs/10.1190/segam2017-17775517.1 https://ghassanalregibdotcom.files.wordpress.com/2018/01/long2017_survey.pdf https://ghassanalregibdotcom.files.wordpress.com/2018/01/long2017_survey.pdf https://ghassanalregibdotcom.files.wordpress.com/2018/01/alfarraj2017_survey.pdf https://ghassanalregibdotcom.files.wordpress.com/2018/01/alfarraj2017_survey.pdf http://onlinelibrary.wiley.com/doi/10.1111/1365-2478.12570/full http://library.seg.org/doi/abs/10.1190/1.1817860 http://library.seg.org/doi/abs/10.1190/1.1817860

Table of Contents for the Digital Edition of IEEE Signal Processing - March 2018