Abstract

3-D information acquisition (registration) of whole face plays a significant role in 3-D human face recognition application. In this paper, we develop a prototype of 3-D system consisting of two binocular measurement units that allows a full 3-D reconstruction by utilizing the advantages of a novel correlation algorithm. In this system, we use optical modulation to produce temporally and spatially varying high-density binary speckle patterns to encode the tested face, then propose a spatial-temporal logical correlation (STLC) stereo matching algorithm to fast determine the accurate disparity with a coarse and refined strategy. Finally the 3-D information of whole face from left- and right ear (~180°) can be obtainable by fusing the data from two measurement units. Comparative researches are performed to test a plastic model and a real human face by simulating real application situations. The results verify the feasibility and good performances of our computational frameworks and experimental configuration in terms of accuracy and time cost, which show a good application prospect in our future 3-D human face recognition research.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2018 (3)

S. Zhang, “High-speed 3D shape measurement with structured light methods: A review,” Opt. Lasers Eng. 106, 119–131 (2018).
[Crossref]

D. Khan, M. A. Shirazi, and Y. K. Min, “Single shot laser speckle based 3D acquisition system for medical applications,” Opt. Lasers Eng. 105, 43–53 (2018).
[Crossref]

P. Zhou, J. Zhu, and H. Jing, “Optical 3-D surface reconstruction with color binary speckle pattern encoding,” Opt. Express 26(3), 3452–3465 (2018).
[Crossref] [PubMed]

2017 (2)

2016 (2)

2014 (2)

2013 (3)

P. Lutzke, M. Schaffer, P. Kühmstedt, R. Kowarschik, and G. Notni, “Experimental comparison of phase-shifting fringe projection and statistical pattern projection for active triangulation systems,” Proc. SPIE 8788, 878813 (2013).
[Crossref]

G. Wang, X. Yin, X. Pei, and C. Shi, “Depth estimation for speckle projection system using progressive reliable points growing matching,” Appl. Opt. 52(3), 516–524 (2013).
[Crossref] [PubMed]

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

2012 (1)

Z. H. Zhang, “Review of single-shot 3D shape measurement by phase calculation-based fringe projection techniques,” Opt. Lasers Eng. 50(8), 1097–1106 (2012).
[Crossref]

2011 (3)

J. Geng, “Structured-light 3D surface imaging: a tutorial,” Adv. Opt. Photonics 3(2), 128–160 (2011).
[Crossref]

M. Schaffer, M. Grosse, B. Harendt, and R. Kowarschik, “High-speed three-dimensional shape measurements of objects with laser speckles and acousto-optical deflection,” Opt. Lett. 36(16), 3097–3099 (2011).
[Crossref] [PubMed]

M. Große, M. Schaffer, B. Harendt, and R. Kowarschik, “Fast data acquisition for three-dimensional shape measurement using fixed-pattern projection and temporal coding,” Opt. Eng. 50(10), 100503 (2011).
[Crossref]

2010 (2)

2008 (1)

2006 (1)

2005 (1)

L. Di Stefano, S. Mattoccia, and F. Tombari, “ZNCC-based template matching using bounded partial correlation,” Pattern Recognit. Lett. 26(14), 2129–2134 (2005).
[Crossref]

2002 (1)

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” Int. J. Comput. Vis. 47(1/3), 7–42 (2002).
[Crossref]

1999 (1)

1993 (1)

1983 (1)

Bartoli, A.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Beiderman, Y.

Chao, Y.

Cheng, J.

J. Jiang, J. Cheng, and H. Zhao, ”Stereo matching based on random speckle projection for dynamic 3D sensing,” International Conference on Machine Learning and Applications, IEEE, 191–196(2013).

Di Stefano, L.

L. Di Stefano, S. Mattoccia, and F. Tombari, “ZNCC-based template matching using bounded partial correlation,” Pattern Recognit. Lett. 26(14), 2129–2134 (2005).
[Crossref]

Elhawary, H.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Elson, D.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Fan, X.

Ferreira, C.

García, J.

García-Martínez, P.

Geng, J.

J. Geng, “Structured-light 3D surface imaging: a tutorial,” Adv. Opt. Photonics 3(2), 128–160 (2011).
[Crossref]

Groch, A.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Grosse, M.

Große, M.

M. Große, M. Schaffer, B. Harendt, and R. Kowarschik, “Fast data acquisition for three-dimensional shape measurement using fixed-pattern projection and temporal coding,” Opt. Eng. 50(10), 100503 (2011).
[Crossref]

Grosse, M.

Guo, J.

Harendt, B.

Huntley, J. M.

Jiang, J.

J. Jiang, J. Cheng, and H. Zhao, ”Stereo matching based on random speckle projection for dynamic 3D sensing,” International Conference on Machine Learning and Applications, IEEE, 191–196(2013).

Jing, H.

Khan, D.

D. Khan, M. A. Shirazi, and Y. K. Min, “Single shot laser speckle based 3D acquisition system for medical applications,” Opt. Lasers Eng. 105, 43–53 (2018).
[Crossref]

Kolb, A.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Kowarschik, R.

Kühmstedt, P.

P. Lutzke, M. Schaffer, P. Kühmstedt, R. Kowarschik, and G. Notni, “Experimental comparison of phase-shifting fringe projection and statistical pattern projection for active triangulation systems,” Proc. SPIE 8788, 878813 (2013).
[Crossref]

Li, A.

Li, Z.

Liu, K.

Liu, X.

Lutzke, P.

P. Lutzke, M. Schaffer, P. Kühmstedt, R. Kowarschik, and G. Notni, “Experimental comparison of phase-shifting fringe projection and statistical pattern projection for active triangulation systems,” Proc. SPIE 8788, 878813 (2013).
[Crossref]

Ma, J.

Maier-Hein, L.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Mattoccia, S.

L. Di Stefano, S. Mattoccia, and F. Tombari, “ZNCC-based template matching using bounded partial correlation,” Pattern Recognit. Lett. 26(14), 2129–2134 (2005).
[Crossref]

Min, Y. K.

D. Khan, M. A. Shirazi, and Y. K. Min, “Single shot laser speckle based 3D acquisition system for medical applications,” Opt. Lasers Eng. 105, 43–53 (2018).
[Crossref]

Mountney, P.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Mutoh, K.

Notni, G.

P. Lutzke, M. Schaffer, P. Kühmstedt, R. Kowarschik, and G. Notni, “Experimental comparison of phase-shifting fringe projection and statistical pattern projection for active triangulation systems,” Proc. SPIE 8788, 878813 (2013).
[Crossref]

Pan, B.

Pei, X.

Peng, X.

Rodrigues, M.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Saldner, H.

Schaffer, M.

B. Harendt, M. Grosse, M. Schaffer, and R. Kowarschik, “3D shape measurement of static and moving objects with adaptive spatiotemporal correlation,” Appl. Opt. 53(31), 7507–7515 (2014).
[Crossref] [PubMed]

P. Lutzke, M. Schaffer, P. Kühmstedt, R. Kowarschik, and G. Notni, “Experimental comparison of phase-shifting fringe projection and statistical pattern projection for active triangulation systems,” Proc. SPIE 8788, 878813 (2013).
[Crossref]

M. Große, M. Schaffer, B. Harendt, and R. Kowarschik, “Fast data acquisition for three-dimensional shape measurement using fixed-pattern projection and temporal coding,” Opt. Eng. 50(10), 100503 (2011).
[Crossref]

M. Schaffer, M. Grosse, B. Harendt, and R. Kowarschik, “High-speed three-dimensional shape measurements of objects with laser speckles and acousto-optical deflection,” Opt. Lett. 36(16), 3097–3099 (2011).
[Crossref] [PubMed]

M. Schaffer, M. Grosse, and R. Kowarschik, “High-speed pattern projection for three-dimensional shape measurement using laser speckles,” Appl. Opt. 49(18), 3622–3629 (2010).
[Crossref] [PubMed]

Scharstein, D.

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” Int. J. Comput. Vis. 47(1/3), 7–42 (2002).
[Crossref]

Shi, C.

Shi, Y.

Shirazi, M. A.

D. Khan, M. A. Shirazi, and Y. K. Min, “Single shot laser speckle based 3D acquisition system for medical applications,” Opt. Lasers Eng. 105, 43–53 (2018).
[Crossref]

Sjödahl, M.

Sorger, J.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Speidel, S.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Stoyanov, D.

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Su, X.

Synnergren, P.

Szeliski, R.

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” Int. J. Comput. Vis. 47(1/3), 7–42 (2002).
[Crossref]

Takeda, M.

Teicher, M.

Tombari, F.

L. Di Stefano, S. Mattoccia, and F. Tombari, “ZNCC-based template matching using bounded partial correlation,” Pattern Recognit. Lett. 26(14), 2129–2134 (2005).
[Crossref]

Wagner, H.

Wang, G.

Wang, S.

Wang, Z.

Wei, S.

Wiegmann, A.

Xiao, C.

Xie, H.

Yin, X.

You, Z.

Yu, J.

Zalevsky, Z.

Zhan, G.

Zhang, S.

S. Zhang, “High-speed 3D shape measurement with structured light methods: A review,” Opt. Lasers Eng. 106, 119–131 (2018).
[Crossref]

Zhang, Z. H.

Z. H. Zhang, “Review of single-shot 3D shape measurement by phase calculation-based fringe projection techniques,” Opt. Lasers Eng. 50(8), 1097–1106 (2012).
[Crossref]

Zhao, H.

X. Liu, H. Zhao, G. Zhan, K. Zhong, Z. Li, Y. Chao, and Y. Shi, “Rapid and automatic 3D body measurement system based on a GPU-Steger line detector,” Appl. Opt. 55(21), 5539–5547 (2016).
[Crossref] [PubMed]

J. Jiang, J. Cheng, and H. Zhao, ”Stereo matching based on random speckle projection for dynamic 3D sensing,” International Conference on Machine Learning and Applications, IEEE, 191–196(2013).

Zhong, K.

Zhong, M.

Zhou, C.

Zhou, P.

Zhu, J.

Adv. Opt. Photonics (1)

J. Geng, “Structured-light 3D surface imaging: a tutorial,” Adv. Opt. Photonics 3(2), 128–160 (2011).
[Crossref]

Appl. Opt. (12)

M. Takeda and K. Mutoh, “Fourier transform profilometry for the automatic measurement of 3-D object shapes,” Appl. Opt. 22(24), 3977–3982 (1983).
[Crossref] [PubMed]

X. Liu, H. Zhao, G. Zhan, K. Zhong, Z. Li, Y. Chao, and Y. Shi, “Rapid and automatic 3D body measurement system based on a GPU-Steger line detector,” Appl. Opt. 55(21), 5539–5547 (2016).
[Crossref] [PubMed]

J. Guo, X. Peng, A. Li, X. Liu, and J. Yu, “Automatic and rapid whole-body 3D shape measurement based on multinode 3D sensing and speckle projection,” Appl. Opt. 56(31), 8759–8768 (2017).
[Crossref] [PubMed]

M. Sjödahl and P. Synnergren, “Measurement of shape by using projected random patterns and temporal digital speckle photography,” Appl. Opt. 38(10), 1990–1997 (1999).
[Crossref] [PubMed]

J. García, Z. Zalevsky, P. García-Martínez, C. Ferreira, M. Teicher, and Y. Beiderman, “Three-dimensional mapping and range measurement by means of projected speckle patterns,” Appl. Opt. 47(16), 3032–3040 (2008).
[Crossref] [PubMed]

B. Pan, H. Xie, and Z. Wang, “Equivalence of digital image correlation criteria for pattern matching,” Appl. Opt. 49(28), 5501–5509 (2010).
[Crossref] [PubMed]

K. Liu, C. Zhou, S. Wei, S. Wang, X. Fan, and J. Ma, “Optimized stereo matching in binocular three-dimensional measurement system using structured light,” Appl. Opt. 53(26), 6083–6090 (2014).
[Crossref] [PubMed]

G. Wang, X. Yin, X. Pei, and C. Shi, “Depth estimation for speckle projection system using progressive reliable points growing matching,” Appl. Opt. 52(3), 516–524 (2013).
[Crossref] [PubMed]

M. Schaffer, M. Grosse, and R. Kowarschik, “High-speed pattern projection for three-dimensional shape measurement using laser speckles,” Appl. Opt. 49(18), 3622–3629 (2010).
[Crossref] [PubMed]

B. Harendt, M. Grosse, M. Schaffer, and R. Kowarschik, “3D shape measurement of static and moving objects with adaptive spatiotemporal correlation,” Appl. Opt. 53(31), 7507–7515 (2014).
[Crossref] [PubMed]

J. M. Huntley and H. Saldner, “Temporal phase-unwrapping algorithm for automated interferogram analysis,” Appl. Opt. 32(17), 3047–3052 (1993).
[Crossref] [PubMed]

P. Zhou, J. Zhu, X. Su, Z. You, H. Jing, C. Xiao, and M. Zhong, “Experimental study of temporal-spatial binary pattern projection for 3D shape acquisition,” Appl. Opt. 56(11), 2995–3003 (2017).
[Crossref] [PubMed]

Int. J. Comput. Vis. (1)

D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” Int. J. Comput. Vis. 47(1/3), 7–42 (2002).
[Crossref]

Med. Image Anal. (1)

L. Maier-Hein, P. Mountney, A. Bartoli, H. Elhawary, D. Elson, A. Groch, A. Kolb, M. Rodrigues, J. Sorger, S. Speidel, and D. Stoyanov, “Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery,” Med. Image Anal. 17(8), 974–996 (2013).
[Crossref] [PubMed]

Opt. Eng. (1)

M. Große, M. Schaffer, B. Harendt, and R. Kowarschik, “Fast data acquisition for three-dimensional shape measurement using fixed-pattern projection and temporal coding,” Opt. Eng. 50(10), 100503 (2011).
[Crossref]

Opt. Express (3)

Opt. Lasers Eng. (3)

D. Khan, M. A. Shirazi, and Y. K. Min, “Single shot laser speckle based 3D acquisition system for medical applications,” Opt. Lasers Eng. 105, 43–53 (2018).
[Crossref]

Z. H. Zhang, “Review of single-shot 3D shape measurement by phase calculation-based fringe projection techniques,” Opt. Lasers Eng. 50(8), 1097–1106 (2012).
[Crossref]

S. Zhang, “High-speed 3D shape measurement with structured light methods: A review,” Opt. Lasers Eng. 106, 119–131 (2018).
[Crossref]

Opt. Lett. (1)

Pattern Recognit. Lett. (1)

L. Di Stefano, S. Mattoccia, and F. Tombari, “ZNCC-based template matching using bounded partial correlation,” Pattern Recognit. Lett. 26(14), 2129–2134 (2005).
[Crossref]

Proc. SPIE (1)

P. Lutzke, M. Schaffer, P. Kühmstedt, R. Kowarschik, and G. Notni, “Experimental comparison of phase-shifting fringe projection and statistical pattern projection for active triangulation systems,” Proc. SPIE 8788, 878813 (2013).
[Crossref]

Other (8)

M. A. Sutton, J. J. Orteu, and H. Schreier, Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications, Springer US, (2009).

VDI/VDE 2634 Blatt 2: 2002–08 Optische 3D-Messsysteme; Systeme mit flachenhafter Antastung. Berlin: Beuth Verlag.

3dMD, “3dMD home page,” http://www.3dmd.com/ .

Wikipedia, “PrimeSense,” https://en.wikipedia.org/wiki/PrimeSense .

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Figures (12)

Fig. 1
Fig. 1 3-D information acquisition system of whole face: (a) schematic diagram of the 3-D system; (b) designed high-density speckle pattern; (c) schematic diagram of the projector within red box, two cameras are included.
Fig. 2
Fig. 2 3-D reconstruction scheme of whole face in registration with STLC.
Fig. 3
Fig. 3 Comparisons between raw input image (left) and LCB image(middle). To make the distinction clearer, LCB white pixels are highlighted in red in the raw input image (right).
Fig. 4
Fig. 4 Illustration of STLC-based stereo matching mechanism to compute a low-resolution coarse disparity map.
Fig. 5
Fig. 5 Pointcloud registration and fusion. (a) pointcloud data of left (red) and right (green) face to be registered and fused; (b) selecting feature points and (c) the process of rough registration; (d) the result of global registration after iteration 5times; (e) the process of fusion; (f)the final fusion result of whole face from left and right ear.
Fig. 6
Fig. 6 Tested results of ceramic standard plate. (a)-(c) one of phase-shifting fringe images with period number 1, 12 and 72; (d) the plane fitting error map of No. 5; (e)fitting Std at seven different spatial poses.
Fig. 7
Fig. 7 Fitting error distributions of dumbbell gauge (D1 and D2) of No. 3. Unit: mm.
Fig. 8
Fig. 8 A-unit: (a)-(c) one of phase-shifting fringe images with fringe number 1, 12 and 72; B-unit: (d)-(f) one of phase-shifting fringe images with period number 1,12 and 72; (g)-(i) 3-D reconstruction results of a plastic human face model from left- and right ear.
Fig. 9
Fig. 9 (a) 3-D reconstruction of the plastic model using ZNCC (N = 4) and (a') comparisons with SFP; (b) 3-D reconstruction using STLC (N = 4) and (b') comparisons with SFP. Units: mm.
Fig. 10
Fig. 10 (a) 3-D reconstruction of the plastic model using ZNCC (N = 1) and (a') comparisons with SFP; (b) 3-D reconstruction using STLC (N = 1) and (b') comparisons with SFP. Units: mm.
Fig. 11
Fig. 11 Computation time comparison between the traditional ZNCC and the proposed STLC by measuring a plastic model.
Fig. 12
Fig. 12 (a)-(b) and (c)-(d) the stereo images pairs sequences (N = 4) of measuring a real human face from A- and B-Unit; (e) 3-D reconstruction of whole face by registering and fusing point- cloud data from (a)-(b) and (c)-(d) by ZNCC; (e') 3-D reconstruction of whole face by registering and fusing point cloud data from (a)-(b) and (c)-(d) by STLC; (f) difference distributions between 3-D reconstructions of (e) and (e').

Tables (2)

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Table 1 Tested result of dumbbell gauge. (Unit: mm)

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Table 2 Performance comparison between the traditional ZNCC and the proposed STLC by measuring a plastic model . (Unit: mm)

Equations (8)

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N d o t = π D 2 4 ( Δ x ) 2 .
I L C B k ( i , j , t ) = { 1 i f I k ( i , j , t ) t h k ( i , j ) 0 i f I k ( i , j , t ) < t h k ( i , j ) ,       ( k { t o p , d o w n } ) .
t h k ( i , j ) = t = 1 N h = w y / 2 w y / 2 l = w x / 2 w x / 2 I k ( i + h , j + l , t ) N w x w y ,       ( k { t o p , d o w n } ) .
B k ( i , j , t ) = s = 1 S x S y 2 s 1 · I L C B k ( i s , j s , t )         ( k { t o p , d o w n } ) .
C S T L C ( i , j , d ) = 1 t = 1 N B t o p ( i , j , t )     X O R     B d o w n ( i , j d , t ) 1 N S x S y ,       ( k { t o p , d o w n } ) .
D I N T ( i , j ) = arg min d C S T L C ( i , j , d ) .
O x = w i d t h S x s t e p x + 1 ,       O y = h e i g h t S y s t e p y + 1.
D S U B ( i , j ) = D I N T ( i , j ) + d s u b .

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