Abstract

A hybrid method using the support vector machine (SVM) correlation filter and the phase-shift interferometry (PSI) holography is proposed to recognize 3D object, which can improve the correct decision rate and resist the distortion of object rotation and noise. The different images of two types of both in-plane and out-of-plane rotated object recorded by digital holography are reconstructed. The reconstructed images of two types are selected to synthesize the SVM correlation filter, respectively. To compare the correct decision rates of the SVM correlation filter with other three ones, it is found that the experimental result is better in rotation resistance and noise tolerance.

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References

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2011 (1)

2010 (1)

I. Barman, C. R. Kong, N. C. Dingari, R. R. Dasari, and M. S. Feld, “Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose,” Anal. Chem. 82(23), 9719–9726 (2010).
[Crossref] [PubMed]

2008 (1)

2007 (1)

2001 (1)

E. Tajahuerce, O. Matoba, Y. Frauel, M. A. Castro, and B. Javidi, “New approaches to 3D image recognition,” Proc. SPIE 81, 170–185 (2001).

2000 (1)

B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, “New support vector algorithms,” Neural Comput. 12(5), 1207–1245 (2000).
[Crossref] [PubMed]

1999 (1)

V. N. Vapnik, “An overview of statistical learning theory,” IEEE Trans. Neural Netw. 10(5), 988–999 (1999).
[Crossref] [PubMed]

1997 (1)

1992 (1)

1980 (1)

Barman, I.

I. Barman, N. C. Dingari, N. Rajaram, J. W. Tunnell, R. R. Dasari, and M. S. Feld, “Rapid and accurate determination of tissue optical properties using least-squares support vector machines,” Biomed. Opt. Express 2(3), 592–599 (2011).
[Crossref] [PubMed]

I. Barman, C. R. Kong, N. C. Dingari, R. R. Dasari, and M. S. Feld, “Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose,” Anal. Chem. 82(23), 9719–9726 (2010).
[Crossref] [PubMed]

Bartlett, P. L.

B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, “New support vector algorithms,” Neural Comput. 12(5), 1207–1245 (2000).
[Crossref] [PubMed]

Birch, P. M.

Casasent, D.

Castro, M. A.

E. Tajahuerce, O. Matoba, Y. Frauel, M. A. Castro, and B. Javidi, “New approaches to 3D image recognition,” Proc. SPIE 81, 170–185 (2001).

Chatwin, C. R.

Dasari, R. R.

I. Barman, N. C. Dingari, N. Rajaram, J. W. Tunnell, R. R. Dasari, and M. S. Feld, “Rapid and accurate determination of tissue optical properties using least-squares support vector machines,” Biomed. Opt. Express 2(3), 592–599 (2011).
[Crossref] [PubMed]

I. Barman, C. R. Kong, N. C. Dingari, R. R. Dasari, and M. S. Feld, “Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose,” Anal. Chem. 82(23), 9719–9726 (2010).
[Crossref] [PubMed]

Dingari, N. C.

I. Barman, N. C. Dingari, N. Rajaram, J. W. Tunnell, R. R. Dasari, and M. S. Feld, “Rapid and accurate determination of tissue optical properties using least-squares support vector machines,” Biomed. Opt. Express 2(3), 592–599 (2011).
[Crossref] [PubMed]

I. Barman, C. R. Kong, N. C. Dingari, R. R. Dasari, and M. S. Feld, “Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose,” Anal. Chem. 82(23), 9719–9726 (2010).
[Crossref] [PubMed]

Feld, M. S.

I. Barman, N. C. Dingari, N. Rajaram, J. W. Tunnell, R. R. Dasari, and M. S. Feld, “Rapid and accurate determination of tissue optical properties using least-squares support vector machines,” Biomed. Opt. Express 2(3), 592–599 (2011).
[Crossref] [PubMed]

I. Barman, C. R. Kong, N. C. Dingari, R. R. Dasari, and M. S. Feld, “Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose,” Anal. Chem. 82(23), 9719–9726 (2010).
[Crossref] [PubMed]

Frauel, Y.

E. Tajahuerce, O. Matoba, Y. Frauel, M. A. Castro, and B. Javidi, “New approaches to 3D image recognition,” Proc. SPIE 81, 170–185 (2001).

Hester, C. F.

Javidi, B.

E. Tajahuerce, O. Matoba, Y. Frauel, M. A. Castro, and B. Javidi, “New approaches to 3D image recognition,” Proc. SPIE 81, 170–185 (2001).

Kong, C. R.

I. Barman, C. R. Kong, N. C. Dingari, R. R. Dasari, and M. S. Feld, “Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose,” Anal. Chem. 82(23), 9719–9726 (2010).
[Crossref] [PubMed]

Kumar, B. V. K. V.

Kypraios, I.

Lei, P.

Li, Q.

Lu, W.

Matoba, O.

E. Tajahuerce, O. Matoba, Y. Frauel, M. A. Castro, and B. Javidi, “New approaches to 3D image recognition,” Proc. SPIE 81, 170–185 (2001).

Rajaram, N.

Schölkopf, B.

B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, “New support vector algorithms,” Neural Comput. 12(5), 1207–1245 (2000).
[Crossref] [PubMed]

Smola, A. J.

B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, “New support vector algorithms,” Neural Comput. 12(5), 1207–1245 (2000).
[Crossref] [PubMed]

Sun, J.

Tajahuerce, E.

E. Tajahuerce, O. Matoba, Y. Frauel, M. A. Castro, and B. Javidi, “New approaches to 3D image recognition,” Proc. SPIE 81, 170–185 (2001).

Tunnell, J. W.

Vapnik, V. N.

V. N. Vapnik, “An overview of statistical learning theory,” IEEE Trans. Neural Netw. 10(5), 988–999 (1999).
[Crossref] [PubMed]

Wang, Q.

Williamson, R. C.

B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, “New support vector algorithms,” Neural Comput. 12(5), 1207–1245 (2000).
[Crossref] [PubMed]

Yamaguchi, I.

Young, R. C. D.

Zhang, T.

Anal. Chem. (1)

I. Barman, C. R. Kong, N. C. Dingari, R. R. Dasari, and M. S. Feld, “Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose,” Anal. Chem. 82(23), 9719–9726 (2010).
[Crossref] [PubMed]

Appl. Opt. (3)

Biomed. Opt. Express (1)

Chin. Opt. Lett. (1)

IEEE Trans. Neural Netw. (1)

V. N. Vapnik, “An overview of statistical learning theory,” IEEE Trans. Neural Netw. 10(5), 988–999 (1999).
[Crossref] [PubMed]

Neural Comput. (1)

B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, “New support vector algorithms,” Neural Comput. 12(5), 1207–1245 (2000).
[Crossref] [PubMed]

Opt. Lett. (1)

Proc. SPIE (1)

E. Tajahuerce, O. Matoba, Y. Frauel, M. A. Castro, and B. Javidi, “New approaches to 3D image recognition,” Proc. SPIE 81, 170–185 (2001).

Other (6)

B. Javidi, Image Recognition and Classification: Algorithms, Systems, and Applications (Marcel Dekker, Inc., 2002).

J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, New York, 1996).

T. C. Poon, Digital Holography and Three Dimensional Display: Principles and Applications (Springer, New York, 2006), pp. 145–168.

C. M. Bishop, Pattern Recognition and Machine Learning (Springer, 2006).

B. V. K. V. Kumar, A. Mahalanobis, and R. D. Juday, Correlation Pattern Recognition (Cambridge University, 2005).

B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond (MIT, Cambridge, MA, 2002)

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Figures (6)

Fig. 1
Fig. 1

The 3D object recognition scheme is that the series cars during (−5°~ + 5°) are generated using digital holography and training with the SVM.

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Fig. 2
Fig. 2

Experimental scheme of 3D object recognition with a single exposure on-axis scheme.

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Fig. 3
Fig. 3

Digital holograms using phase-shift interferometry.

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Fig. 4
Fig. 4

Reconstructed images using different view angles.

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Fig. 5
Fig. 5

(a) Collected image of the target objects(256 × 256 pixels), (b) collected image of the non-target objects(256 × 256 pixels), (c) SVM correlation filter synthesized by the in-plane rotated samples of the reconstructed image, and (d) SVM correlation filter synthesized by the out-of-plane rotated samples of the reconstructed image.

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Fig. 6
Fig. 6

(a) Comparison of resistance to in-plane rotation of four correlation filters, (b) comparison of resistance to in-plane rotation of four correlation filters under certain noise distortion, (c) comparison of resistance to out-of-plane rotation of four correlation filters, (d) comparison of resistance to out-of-plane rotation of four correlation filters under certain noise distortion, (e) under certain out-of-plane rotation angle and without noise distortion, the correction decision rate obtained with the number of training samples for four correlation filters, and (f) with both certain out-of-plane rotation angle and noise distortion, the correction decision rate obtained with the number of training samples for four correlation filters.

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Tables (1)

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Table 1 Comparison of average synthesizing time (second).

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Equations (4)

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H(x,y)= 1 4 U R * { I H (x,y;0)− I H (x,y;π)+i[ I H (x,y;π/2)− I H (x,y;3π/2) ] }
h(x,y)= a 1 s 1 (x,y)+ a 2 s 2 (x,y)+...+ a n s n (x,y)= ∑ i=1 n a n s n (x,y)
f(x)=sgn{ ( w → * ⋅ x → − b * ) }=sgn{ ∑ i=1 n a i * y i ( x → i ⋅ x → )− b * }
f ′ (x)= ∑ i=1 n a i ∗ y i ( x → i ⋅ x → ) = x → ⋅ ∑ i=1 n a i ∗ y i x → i = x → ⋅ ∑ i=1 n A i ∗ x → i = x → ⋅ h ′ (x)

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