V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: a survey,” ACM Comput. Surv. 41, 1–58 (2009).

[CrossRef]

S. A. Karout, M. A. Gdeisat, D. R. Burton, and M. J. Lalor, “Residue vector, an approach to branch-cut placement in phase unwrapping: theoretical study,” Appl. Opt. 46, 4712–4727 (2007).

[CrossRef]
[PubMed]

H. Du and Z. Y. Wang, “Three-dimensional shape measurement with an arbitrarily arranged fringe projection profilometry system,” Opt. Lett. 32, 2438–2440 (2007).

[CrossRef]
[PubMed]

G. Fornaro, A. Pauciullo, and E. Sansosti, “Phase difference-based multichannel phase unwrapping,” IEEE Trans. Image Process. 14, 960–972 (2005).

[CrossRef]
[PubMed]

F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).

[CrossRef]

G. Nico, G. Palubinskas, and M. Datcu, “Bayesian approaches to phase unwrapping: theoretical study,” IEEE Trans. Signal Process. 48, 2545–2556 (2000).

[CrossRef]

L. An, Q. S. Xiang, and S. Chavez, “A fast implementation of the minimum spanning tree method for phase unwrapping,” IEEE Trans. Med. Imaging 19, 805–808 (2000).

[CrossRef]
[PubMed]

G. Nico, “Noise-residue filtering of interferometric phase images,” J. Opt. Soc. Am. A. 17, 1962–1974 (2000).

[CrossRef]

J. S. Lee, K. P. Papathanassiou, T. L. Ainsworth, M. R. Grunes, and A. Reigber, “A new technique for noise filtering of SAR interferometric phase images,” IEEE Trans. Geosci. Remote Sensing 36, 1173 (1998).

[CrossRef]

J. M. N. Leitão and M. A. T. Figueiredo, “Absolute phase image reconstruction: a stochastic nonlinear filtering approach,” IEEE Trans. Image Process. 7, 868–882 (1998).

[CrossRef]

M. Costantini, “A novel phase unwrapping method based on network programming,” IEEE Trans. Geosci. Remote Sensing 36, 813–821 (1998).

[CrossRef]

H. A. Zebker and Y. P. Lu, “Phase unwrapping algorithms for radar interferometry: residue-cut, least-squares, and synthesis algorithms,” J. Opt. Soc. Am. A 15, 586–598 (1998).

[CrossRef]

A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36, 2466–2472 (1997).

[CrossRef]

J. S. Lee, K. P. Papathanassiou, T. L. Ainsworth, M. R. Grunes, and A. Reigber, “A new technique for noise filtering of SAR interferometric phase images,” IEEE Trans. Geosci. Remote Sensing 36, 1173 (1998).

[CrossRef]

L. An, Q. S. Xiang, and S. Chavez, “A fast implementation of the minimum spanning tree method for phase unwrapping,” IEEE Trans. Med. Imaging 19, 805–808 (2000).

[CrossRef]
[PubMed]

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: a survey,” ACM Comput. Surv. 41, 1–58 (2009).

[CrossRef]

P. Berkhin, Survey of Clustering Data Mining Techniques (Springer, 2002).

A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36, 2466–2472 (1997).

[CrossRef]

F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).

[CrossRef]

A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36, 2466–2472 (1997).

[CrossRef]

A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36, 2466–2472 (1997).

[CrossRef]

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: a survey,” ACM Comput. Surv. 41, 1–58 (2009).

[CrossRef]

L. An, Q. S. Xiang, and S. Chavez, “A fast implementation of the minimum spanning tree method for phase unwrapping,” IEEE Trans. Med. Imaging 19, 805–808 (2000).

[CrossRef]
[PubMed]

F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).

[CrossRef]

J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt. 49, 2143–2150 (2010).

[CrossRef]
[PubMed]

J. Jiang and J. Cheng, “Noise-residue filtering based on unsupervised clustering for phase unwrapping,” in Proceedings of 5th International Symposium on Visual Computing, M.Z.Nashed, ed. (Springer, 2009), pp. 719–727.

M. Costantini, “A novel phase unwrapping method based on network programming,” IEEE Trans. Geosci. Remote Sensing 36, 813–821 (1998).

[CrossRef]

G. Nico, G. Palubinskas, and M. Datcu, “Bayesian approaches to phase unwrapping: theoretical study,” IEEE Trans. Signal Process. 48, 2545–2556 (2000).

[CrossRef]

J. M. N. Leitão and M. A. T. Figueiredo, “Absolute phase image reconstruction: a stochastic nonlinear filtering approach,” IEEE Trans. Image Process. 7, 868–882 (1998).

[CrossRef]

G. Fornaro, A. Pauciullo, and E. Sansosti, “Phase difference-based multichannel phase unwrapping,” IEEE Trans. Image Process. 14, 960–972 (2005).

[CrossRef]
[PubMed]

A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36, 2466–2472 (1997).

[CrossRef]

S. S. Gorthi and P. Rastogi, “Fringe projection techniques: whither we are?” Opt. Lasers Eng. 48, 133–140 (2010).

[CrossRef]

J. S. Lee, K. P. Papathanassiou, T. L. Ainsworth, M. R. Grunes, and A. Reigber, “A new technique for noise filtering of SAR interferometric phase images,” IEEE Trans. Geosci. Remote Sensing 36, 1173 (1998).

[CrossRef]

J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt. 49, 2143–2150 (2010).

[CrossRef]
[PubMed]

J. Jiang and J. Cheng, “Noise-residue filtering based on unsupervised clustering for phase unwrapping,” in Proceedings of 5th International Symposium on Visual Computing, M.Z.Nashed, ed. (Springer, 2009), pp. 719–727.

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: a survey,” ACM Comput. Surv. 41, 1–58 (2009).

[CrossRef]

J. S. Lee, K. P. Papathanassiou, T. L. Ainsworth, M. R. Grunes, and A. Reigber, “A new technique for noise filtering of SAR interferometric phase images,” IEEE Trans. Geosci. Remote Sensing 36, 1173 (1998).

[CrossRef]

J. M. N. Leitão and M. A. T. Figueiredo, “Absolute phase image reconstruction: a stochastic nonlinear filtering approach,” IEEE Trans. Image Process. 7, 868–882 (1998).

[CrossRef]

Z. P. Liang, “A model-based method for phase unwrapping,” IEEE Trans. Med. Imaging 15, 893–897 (1996).

[CrossRef]
[PubMed]

G. Nico, G. Palubinskas, and M. Datcu, “Bayesian approaches to phase unwrapping: theoretical study,” IEEE Trans. Signal Process. 48, 2545–2556 (2000).

[CrossRef]

G. Nico, “Noise-residue filtering of interferometric phase images,” J. Opt. Soc. Am. A. 17, 1962–1974 (2000).

[CrossRef]

G. Nico, G. Palubinskas, and M. Datcu, “Bayesian approaches to phase unwrapping: theoretical study,” IEEE Trans. Signal Process. 48, 2545–2556 (2000).

[CrossRef]

J. S. Lee, K. P. Papathanassiou, T. L. Ainsworth, M. R. Grunes, and A. Reigber, “A new technique for noise filtering of SAR interferometric phase images,” IEEE Trans. Geosci. Remote Sensing 36, 1173 (1998).

[CrossRef]

G. Fornaro, A. Pauciullo, and E. Sansosti, “Phase difference-based multichannel phase unwrapping,” IEEE Trans. Image Process. 14, 960–972 (2005).

[CrossRef]
[PubMed]

A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36, 2466–2472 (1997).

[CrossRef]

D. C. Ghiglia and M. D. Pritt, Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software (Wiley, 1998).

S. S. Gorthi and P. Rastogi, “Fringe projection techniques: whither we are?” Opt. Lasers Eng. 48, 133–140 (2010).

[CrossRef]

J. S. Lee, K. P. Papathanassiou, T. L. Ainsworth, M. R. Grunes, and A. Reigber, “A new technique for noise filtering of SAR interferometric phase images,” IEEE Trans. Geosci. Remote Sensing 36, 1173 (1998).

[CrossRef]

G. Fornaro, A. Pauciullo, and E. Sansosti, “Phase difference-based multichannel phase unwrapping,” IEEE Trans. Image Process. 14, 960–972 (2005).

[CrossRef]
[PubMed]

F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).

[CrossRef]

L. An, Q. S. Xiang, and S. Chavez, “A fast implementation of the minimum spanning tree method for phase unwrapping,” IEEE Trans. Med. Imaging 19, 805–808 (2000).

[CrossRef]
[PubMed]

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: a survey,” ACM Comput. Surv. 41, 1–58 (2009).

[CrossRef]

K. Itoh, “Analysis of the phase unwrapping problem,” Appl. Opt. 21, 2470–2470 (1982).

[CrossRef]
[PubMed]

A. Baldi, “Phase unwrapping by region growing,” Appl. Opt. 42, 2498–2505 (2003).

[CrossRef]
[PubMed]

S. A. Karout, M. A. Gdeisat, D. R. Burton, and M. J. Lalor, “Residue vector, an approach to branch-cut placement in phase unwrapping: theoretical study,” Appl. Opt. 46, 4712–4727 (2007).

[CrossRef]
[PubMed]

K. M.Qian, W. Gao, and H. Wang, “Windowed Fourier-filtered and quality-guided phase-unwrapping algorithm,” Appl. Opt. 47, 5408 (2008).

[CrossRef]

J. Jiang, J. Cheng, and B. Luong, “Unsupervised-clustering-driven noise-residue filter for phase images,” Appl. Opt. 49, 2143–2150 (2010).

[CrossRef]
[PubMed]

J. S. Lee, K. P. Papathanassiou, T. L. Ainsworth, M. R. Grunes, and A. Reigber, “A new technique for noise filtering of SAR interferometric phase images,” IEEE Trans. Geosci. Remote Sensing 36, 1173 (1998).

[CrossRef]

M. Costantini, “A novel phase unwrapping method based on network programming,” IEEE Trans. Geosci. Remote Sensing 36, 813–821 (1998).

[CrossRef]

J. M. N. Leitão and M. A. T. Figueiredo, “Absolute phase image reconstruction: a stochastic nonlinear filtering approach,” IEEE Trans. Image Process. 7, 868–882 (1998).

[CrossRef]

G. Fornaro, A. Pauciullo, and E. Sansosti, “Phase difference-based multichannel phase unwrapping,” IEEE Trans. Image Process. 14, 960–972 (2005).

[CrossRef]
[PubMed]

L. An, Q. S. Xiang, and S. Chavez, “A fast implementation of the minimum spanning tree method for phase unwrapping,” IEEE Trans. Med. Imaging 19, 805–808 (2000).

[CrossRef]
[PubMed]

Z. P. Liang, “A model-based method for phase unwrapping,” IEEE Trans. Med. Imaging 15, 893–897 (1996).

[CrossRef]
[PubMed]

G. Nico, G. Palubinskas, and M. Datcu, “Bayesian approaches to phase unwrapping: theoretical study,” IEEE Trans. Signal Process. 48, 2545–2556 (2000).

[CrossRef]

M. Hubig, S. Suchandt, and N. Adam, “A class of solution-invariant transformations of cost functions for minimum cost flow phase unwrapping,” J. Opt. Soc. Am. A 21, 1975–1987(2004).

[CrossRef]

D. C. Ghiglia and L. A. Romero, “Minimum Lp-norm two-dimensional phase unwrapping,” J. Opt. Soc. Am. A 13, 1999–2013 (1996).

[CrossRef]

H. A. Zebker and Y. P. Lu, “Phase unwrapping algorithms for radar interferometry: residue-cut, least-squares, and synthesis algorithms,” J. Opt. Soc. Am. A 15, 586–598 (1998).

[CrossRef]

G. Nico, “Noise-residue filtering of interferometric phase images,” J. Opt. Soc. Am. A. 17, 1962–1974 (2000).

[CrossRef]

A. Capanni, L. Pezzati, D. Bertani, M. Cetica, and F. Francini, “Phase-shifting speckle interferometry: a noise reduction filter for phase unwrapping,” Opt. Eng. 36, 2466–2472 (1997).

[CrossRef]

F. Chen, G. M. Brown, and M. Song, “Overview of three-dimensional shape measurement using optical methods,” Opt. Eng. 39, 10–22 (2000).

[CrossRef]

S. S. Gorthi and P. Rastogi, “Fringe projection techniques: whither we are?” Opt. Lasers Eng. 48, 133–140 (2010).

[CrossRef]

cmax is the cluster in which the number of members is biggest.

D. C. Ghiglia and M. D. Pritt, Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software (Wiley, 1998).

J. Jiang and J. Cheng, “Noise-residue filtering based on unsupervised clustering for phase unwrapping,” in Proceedings of 5th International Symposium on Visual Computing, M.Z.Nashed, ed. (Springer, 2009), pp. 719–727.

P. Berkhin, Survey of Clustering Data Mining Techniques (Springer, 2002).

cmin is the cluster in which the number of members is smallest.