Abstract

We experimentally demonstrated object recognition through scattering media based on direct machine learning of a number of speckle intensity images. In the experiments, speckle intensity images of amplitude or phase objects on a spatial light modulator between scattering plates were captured by a camera. We used the support vector machine for binary classification of the captured speckle intensity images of face and non-face data. The experimental results showed that speckles are sufficient for machine learning.

© 2015 Optical Society of America

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References

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2015 (5)

2014 (5)

D. Shin, J.-J. Lee, and B.-G. Lee, “Recognition of a scattering 3D object using axially distributed image sensing technique,” ARPN J. Eng. Appl. Sci. 9, 2085–2088 (2014).

A. Zdunek, A. Adamiak, P. M. Pieczywek, and A. Kurenda, “The biospeckle method for the investigation of agricultural crops: a review,” Opt. Lasers Eng. 52, 276–285 (2014).
[Crossref]

R. Nassif, C. A. Nader, C. Afif, F. Pellen, G. Le Brun, B. Le Jeune, and M. Abboud, “Detection of golden apples’ climacteric peak by laser biospeckle measurements,” Appl. Opt. 53, 8276–8282 (2014).
[Crossref]

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784–790 (2014).
[Crossref]

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

2012 (3)

J. Bertolotti, E. G. van Putten, C. Blum, A. Lagendijk, W. L. Vos, and A. P. Mosk, “Non-invasive imaging through opaque scattering layers,” Nature 491, 232–234 (2012).
[Crossref] [PubMed]

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283–292 (2012).
[Crossref]

D. Shin and B. Javidi, “Visualization of 3D objects in scattering medium using axially distributed sensing,” J. Disp. Technol. 8, 317–320 (2012).
[Crossref]

2009 (2)

2008 (1)

2006 (2)

S. K. Nayar, G. Krishnan, M. D. Grossberg, and R. Raskar, “Fast separation of direct and global components of a scene using high frequency illumination,” ACM Trans. Graph. 25, 935–944 (2006).
[Crossref]

D. Fradkin and I. Muchnik, “Support vector machines for classification,” Discret. Methods Epidemiol. 70, 13–20 (2006).

2005 (1)

2003 (1)

2002 (1)

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).
[Crossref]

2001 (1)

M. G. Genton, “Classes of kernels for machine learning: a statistics perspective,” J. Mach. Learn. Res. 2, 299–312 (2001).

1998 (1)

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Discov. 2, 121–167 (1998).
[Crossref]

1997 (1)

T. G. Dietterich, “Machine-learning research,” AI Mag. 18, 97–136 (1997).

1996 (2)

V. Blanz, B. Schslkopf, H. Bulthoff, C. Burges, V. Vapnik, and T. Vetter, “Comparison of view-based object recognition algorithms using realistic 3D models,” Icann 1112, 251–256 (1996).

J. S. Tyo, M. P. Rowe, E. N. Pugh, and N. Engheta, “Target detection in optically scattering media by polarization-difference imaging,” Appl. Opt. 35, 1855–1870 (1996).
[Crossref] [PubMed]

1967 (1)

Abboud, M.

Adamiak, A.

A. Zdunek, A. Adamiak, P. M. Pieczywek, and A. Kurenda, “The biospeckle method for the investigation of agricultural crops: a review,” Opt. Lasers Eng. 52, 276–285 (2014).
[Crossref]

Afif, C.

Ando, T.

T. Ando, R. Horisaki, T. Nakamura, and J. Tanida, “Single-shot acquisition of optical direct and global components using single coded pattern projection,” J. Jpn. Appl. Phys. 54, 042501 (2015).
[Crossref]

T. Ando, R. Horisaki, and J. Tanida, “Three-dimensional imaging through scattering media using three-dimensionally coded pattern projection,” Appl. Opt. 54, 7316–7322 (2015).
[Crossref] [PubMed]

Andrés, P.

Beiderman, Y.

Bertolotti, J.

J. Bertolotti, E. G. van Putten, C. Blum, A. Lagendijk, W. L. Vos, and A. P. Mosk, “Non-invasive imaging through opaque scattering layers,” Nature 491, 232–234 (2012).
[Crossref] [PubMed]

Bevilacqua, F.

Bishitz, Y.

Bishop, C. M.

C. M. Bishop, Pattern Recognition and Machine Learning (Springer-Verlag New York, Inc., 2006).

Blanz, V.

V. Blanz, B. Schslkopf, H. Bulthoff, C. Burges, V. Vapnik, and T. Vetter, “Comparison of view-based object recognition algorithms using realistic 3D models,” Icann 1112, 251–256 (1996).

Blum, C.

J. Bertolotti, E. G. van Putten, C. Blum, A. Lagendijk, W. L. Vos, and A. P. Mosk, “Non-invasive imaging through opaque scattering layers,” Nature 491, 232–234 (2012).
[Crossref] [PubMed]

Bousquet, O.

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).
[Crossref]

Bulthoff, H.

V. Blanz, B. Schslkopf, H. Bulthoff, C. Burges, V. Vapnik, and T. Vetter, “Comparison of view-based object recognition algorithms using realistic 3D models,” Icann 1112, 251–256 (1996).

Burges, C.

V. Blanz, B. Schslkopf, H. Bulthoff, C. Burges, V. Vapnik, and T. Vetter, “Comparison of view-based object recognition algorithms using realistic 3D models,” Icann 1112, 251–256 (1996).

Burges, C. J. C.

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Discov. 2, 121–167 (1998).
[Crossref]

Carron, I.

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

Chapelle, O.

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).
[Crossref]

Chardon, G.

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

Choi, W.

Choi, Y.

Clemente, P.

Cuccia, D. J.

Daudet, L.

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

Dietterich, T. G.

T. G. Dietterich, “Machine-learning research,” AI Mag. 18, 97–136 (1997).

Durán, V.

Durkin, A. J.

Engheta, N.

Fink, M.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784–790 (2014).
[Crossref]

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283–292 (2012).
[Crossref]

Fradkin, D.

D. Fradkin and I. Muchnik, “Support vector machines for classification,” Discret. Methods Epidemiol. 70, 13–20 (2006).

Freund, R.

E. Osuna, R. Freund, and F. Girosi, “Training support vector machines: an application to face detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1997), pp. 130–136.
[Crossref]

Garcia, J.

Genton, M. G.

M. G. Genton, “Classes of kernels for machine learning: a statistics perspective,” J. Mach. Learn. Res. 2, 299–312 (2001).

Gigan, S.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784–790 (2014).
[Crossref]

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

Gilbert, G. D.

Gingold, S.

Girosi, F.

E. Osuna, R. Freund, and F. Girosi, “Training support vector machines: an application to face detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1997), pp. 130–136.
[Crossref]

Goodman, J. W.

J. W. Goodman, Introduction to Fourier Optics (Roberts & Co, 2004).

Grossberg, M. D.

S. K. Nayar, G. Krishnan, M. D. Grossberg, and R. Raskar, “Fast separation of direct and global components of a scene using high frequency illumination,” ACM Trans. Graph. 25, 935–944 (2006).
[Crossref]

Heidmann, P.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784–790 (2014).
[Crossref]

Horisaki, R.

T. Ando, R. Horisaki, and J. Tanida, “Three-dimensional imaging through scattering media using three-dimensionally coded pattern projection,” Appl. Opt. 54, 7316–7322 (2015).
[Crossref] [PubMed]

T. Ando, R. Horisaki, T. Nakamura, and J. Tanida, “Single-shot acquisition of optical direct and global components using single coded pattern projection,” J. Jpn. Appl. Phys. 54, 042501 (2015).
[Crossref]

Irles, E.

Javidi, B.

D. Shin and B. Javidi, “Visualization of 3D objects in scattering medium using axially distributed sensing,” J. Disp. Technol. 8, 317–320 (2012).
[Crossref]

I. Moon and B. Javidi, “Three-dimensional visualization of objects inscattering medium by use of computational integral imaging,” Opt. Express 16, 13080–13089 (2008).
[Crossref] [PubMed]

Katz, O.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784–790 (2014).
[Crossref]

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

Kim, M.

Krishnan, G.

S. K. Nayar, G. Krishnan, M. D. Grossberg, and R. Raskar, “Fast separation of direct and global components of a scene using high frequency illumination,” ACM Trans. Graph. 25, 935–944 (2006).
[Crossref]

Kurenda, A.

A. Zdunek, A. Adamiak, P. M. Pieczywek, and A. Kurenda, “The biospeckle method for the investigation of agricultural crops: a review,” Opt. Lasers Eng. 52, 276–285 (2014).
[Crossref]

Lagendijk, A.

J. Bertolotti, E. G. van Putten, C. Blum, A. Lagendijk, W. L. Vos, and A. P. Mosk, “Non-invasive imaging through opaque scattering layers,” Nature 491, 232–234 (2012).
[Crossref] [PubMed]

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283–292 (2012).
[Crossref]

Lancis, J.

Le Brun, G.

Le Jeune, B.

Lee, B.-G.

D. Shin, J.-J. Lee, and B.-G. Lee, “Recognition of a scattering 3D object using axially distributed image sensing technique,” ARPN J. Eng. Appl. Sci. 9, 2085–2088 (2014).

Lee, J.-J.

D. Shin, J.-J. Lee, and B.-G. Lee, “Recognition of a scattering 3D object using axially distributed image sensing technique,” ARPN J. Eng. Appl. Sci. 9, 2085–2088 (2014).

Lerosey, G.

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283–292 (2012).
[Crossref]

Liutkus, A.

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

Margalit, I.

Martina, D.

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

Mico, V.

Moon, I.

Mosk, A. P.

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283–292 (2012).
[Crossref]

J. Bertolotti, E. G. van Putten, C. Blum, A. Lagendijk, W. L. Vos, and A. P. Mosk, “Non-invasive imaging through opaque scattering layers,” Nature 491, 232–234 (2012).
[Crossref] [PubMed]

Muchnik, I.

D. Fradkin and I. Muchnik, “Support vector machines for classification,” Discret. Methods Epidemiol. 70, 13–20 (2006).

Mukherjee, S.

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).
[Crossref]

Nader, C. A.

Nakamura, T.

T. Ando, R. Horisaki, T. Nakamura, and J. Tanida, “Single-shot acquisition of optical direct and global components using single coded pattern projection,” J. Jpn. Appl. Phys. 54, 042501 (2015).
[Crossref]

Narasimhan, S. G.

Nassif, R.

Nayar, S. K.

S. K. Nayar, G. Krishnan, M. D. Grossberg, and R. Raskar, “Fast separation of direct and global components of a scene using high frequency illumination,” ACM Trans. Graph. 25, 935–944 (2006).
[Crossref]

Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, “Polarization-based vision through haze,” Appl. Opt. 42, 511–525 (2003).
[Crossref] [PubMed]

Osuna, E.

E. Osuna, R. Freund, and F. Girosi, “Training support vector machines: an application to face detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1997), pp. 130–136.
[Crossref]

Ozana, N.

Pellen, F.

Pernicka, J. C.

Pieczywek, P. M.

A. Zdunek, A. Adamiak, P. M. Pieczywek, and A. Kurenda, “The biospeckle method for the investigation of agricultural crops: a review,” Opt. Lasers Eng. 52, 276–285 (2014).
[Crossref]

Popoff, S.

A. Liutkus, D. Martina, S. Popoff, G. Chardon, O. Katz, G. Lerosey, S. Gigan, L. Daudet, and I. Carron, “Imaging with nature: compressive imaging using a multiply scattering medium,” Sci. Rep. 4, 5552 (2014).
[Crossref] [PubMed]

Pugh, E. N.

Raskar, R.

S. K. Nayar, G. Krishnan, M. D. Grossberg, and R. Raskar, “Fast separation of direct and global components of a scene using high frequency illumination,” ACM Trans. Graph. 25, 935–944 (2006).
[Crossref]

Rowe, M. P.

Schechner, Y. Y.

T. Treibitz and Y. Y. Schechner, “Active polarization descattering,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 385–399 (2009).
[Crossref] [PubMed]

Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, “Polarization-based vision through haze,” Appl. Opt. 42, 511–525 (2003).
[Crossref] [PubMed]

Schmidt, M.

Schslkopf, B.

V. Blanz, B. Schslkopf, H. Bulthoff, C. Burges, V. Vapnik, and T. Vetter, “Comparison of view-based object recognition algorithms using realistic 3D models,” Icann 1112, 251–256 (1996).

Shin, D.

D. Shin, J.-J. Lee, and B.-G. Lee, “Recognition of a scattering 3D object using axially distributed image sensing technique,” ARPN J. Eng. Appl. Sci. 9, 2085–2088 (2014).

D. Shin and B. Javidi, “Visualization of 3D objects in scattering medium using axially distributed sensing,” J. Disp. Technol. 8, 317–320 (2012).
[Crossref]

Soldevila, F.

Suykens, J. A. K.

J. A. K. Suykens, “Nonlinear modelling and support vector machines,” in Proceedings of Instrumentation and Measurement Technology (IEEE, 2001), pp. 287–294.

Tajahuerce, E.

Tanida, J.

T. Ando, R. Horisaki, and J. Tanida, “Three-dimensional imaging through scattering media using three-dimensionally coded pattern projection,” Appl. Opt. 54, 7316–7322 (2015).
[Crossref] [PubMed]

T. Ando, R. Horisaki, T. Nakamura, and J. Tanida, “Single-shot acquisition of optical direct and global components using single coded pattern projection,” J. Jpn. Appl. Phys. 54, 042501 (2015).
[Crossref]

Teicher, M.

Tenner, F.

Treibitz, T.

T. Treibitz and Y. Y. Schechner, “Active polarization descattering,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 385–399 (2009).
[Crossref] [PubMed]

Tromberg, B. J.

Tyo, J. S.

van Putten, E. G.

J. Bertolotti, E. G. van Putten, C. Blum, A. Lagendijk, W. L. Vos, and A. P. Mosk, “Non-invasive imaging through opaque scattering layers,” Nature 491, 232–234 (2012).
[Crossref] [PubMed]

Vapnik, V.

O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing multiple parameters for support vector machines,” Mach. Learn. 46, 131–159 (2002).
[Crossref]

V. Blanz, B. Schslkopf, H. Bulthoff, C. Burges, V. Vapnik, and T. Vetter, “Comparison of view-based object recognition algorithms using realistic 3D models,” Icann 1112, 251–256 (1996).

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Figures (8)

Fig. 1
Fig. 1

Schematic diagrams of (a) conventional and (b) proposed methods for object recognition through scattering media.

Fig. 2
Fig. 2

Setup for the experiment.

Fig. 3
Fig. 3

Examples of (a)–(j) face and (k)–(t) non-face images.

Fig. 4
Fig. 4

Scattering plates for the experiment. Two sets of each scattering plate were prepared as front and rear scattering plates, as shown in Fig. 2.

Fig. 5
Fig. 5

Examples of experimentally captured speckle intensity images of the face and non-face images shown in Figs. 3(a) and 3(k). The speckles captured through the scattering plate 2 of the amplitude target of (a) the face image and (b) the non-face image. The speckles captured through the scattering plate 2 of the phase target of (c) the face image and (d) the non-face image. The subfigures at the right in Figs. (a)–(d) are close-ups of the central 50 ×50 pixels in each of the left figures.

Fig. 6
Fig. 6

Correlations between speckle intensity images from Figs. 3(a)–3(e) and Figs. 3(k)–3(o). Results with the scattering plate 1 from the (a) amplitude and (b) phase targets. Results with the scattering plate 2 from the (c) amplitude and (d) phase targets. The indices of the row and column in (a) show the subindices of Figs. 3(a)–3(e) and Figs. 3(k)–3(o).

Fig. 7
Fig. 7

Accuracy rates with different numbers of sampling pixels.

Fig. 8
Fig. 8

Accuracy rates with different numbers of training images.

Equations (4)

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u out = 𝒫 ( u in ) = exp ( j k z ) j λ z u in ( x in , y in ) exp [ j k 2 z [ ( x out x in ) 2 + ( y out y in ) 2 ] ] d x in d y in ,
g i = | 𝒫 ( r 2 𝒫 ( r 1 f i ) ) | 2 ,
D = { 𝒮 ( g i ) , l i | 𝒮 ( g i ) N s , l i { 1 , 1 } } i = 1 N g ,
min w , b 1 2 w 2 subject to l i ( w 𝒮 ( g i ) b ) 1 , i ,

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