Abstract

In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.

© 2011 Optical Society of America

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References

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2008

2006

F. Okano, J. Arai, K. Mitani, and M. Okui, Proc. IEEE 94, 490 (2006).
[CrossRef]

A. Stern and B. Javidi, Proc. IEEE 94, 591 (2006).
[CrossRef]

2005

2004

2003

A. Van Nevel and A. Mahalanobis, Opt. Eng. 42, 541 (2003).
[CrossRef]

2002

1998

1995

1990

1908

G. Lippmann, C. R. Acad. Sci. Paris Ser. IV 146, 446 (1908).

Arai, J.

F. Okano, J. Arai, K. Mitani, and M. Okui, Proc. IEEE 94, 490 (2006).
[CrossRef]

Goodman, J. W.

J. W. Goodman, Statistical Optics (Wiley, 1985).

Guillanume, M.

Hong, S.-H.

Jang, J.-S.

Javidi, B.

Lippmann, G.

G. Lippmann, C. R. Acad. Sci. Paris Ser. IV 146, 446 (1908).

Mahalanobis, A.

A. Van Nevel and A. Mahalanobis, Opt. Eng. 42, 541 (2003).
[CrossRef]

Melon, P.

Mitani, K.

F. Okano, J. Arai, K. Mitani, and M. Okui, Proc. IEEE 94, 490 (2006).
[CrossRef]

Nevel, A. Van

A. Van Nevel and A. Mahalanobis, Opt. Eng. 42, 541 (2003).
[CrossRef]

Okano, F.

F. Okano, J. Arai, K. Mitani, and M. Okui, Proc. IEEE 94, 490 (2006).
[CrossRef]

Okui, M.

F. Okano, J. Arai, K. Mitani, and M. Okui, Proc. IEEE 94, 490 (2006).
[CrossRef]

Refregier, P.

Stern, A.

A. Stern and B. Javidi, Proc. IEEE 94, 591 (2006).
[CrossRef]

Tavakoli, B.

Wang, J.

Watson, E.

Yeom, S.

C. R. Acad. Sci. Paris Ser. IV

G. Lippmann, C. R. Acad. Sci. Paris Ser. IV 146, 446 (1908).

J. Opt. Soc. Am. A

Opt. Eng.

A. Van Nevel and A. Mahalanobis, Opt. Eng. 42, 541 (2003).
[CrossRef]

Opt. Express

Opt. Lett.

Proc. IEEE

F. Okano, J. Arai, K. Mitani, and M. Okui, Proc. IEEE 94, 490 (2006).
[CrossRef]

A. Stern and B. Javidi, Proc. IEEE 94, 591 (2006).
[CrossRef]

Other

J. W. Goodman, Statistical Optics (Wiley, 1985).

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Figures (6)

Fig. 1
Fig. 1

Synthetic aperture integral imaging.

Fig. 2
Fig. 2

Objects used in experiments. (a), (c) True class object without and with occlusion. (b), (d) False class object without and with occlusion.

Fig. 3
Fig. 3

Ideal training images obtained by reconstructing the object at depths of (a) 230, (b) 260, and (c)  300 mm .

Fig. 4
Fig. 4

Reconstruction results of photon counting integral imaging using 500 photons. (a), (c) True class without and with occlusion. (b), (d) False class without and with occlusion.

Fig. 5
Fig. 5

PSR and correlation results for true class (a), (b) without occlusion and (c), (d) with occlusion.

Fig. 6
Fig. 6

PSR results for true and false classes with occlusion.

Equations (3)

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C k l ( x , y ) | λ k l ( x , y ) Poisson ( N p λ k l ( x , y ) ) ,
λ ^ k l ( x , y ) = C k l , I ^ ( x , y , z r ) = 1 N p K L k = 0 K 1 l = 0 L 1 λ ^ k l ( x , y ) ,
H ( u , v ) = M ( u , v ) α · D ( u , v ) + β · S ( u , v ) + γ · C ( u , v )

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