Abstract

This paper deals with tracking of deformable objects in the presence of occlusion using dominant point representation of the boundary contour. A novel nonintegral time propagation model for propagating the dominant points is proposed. It uses an initial guess generated from a linear operation and an analytical conjugate gradient approach for online robust learning of the shape deformation and motion model. A scheme is presented to automatically detect and correct the region of large local deformation. In order to deal with occlusion, admissible restrictions on deformation and motion of the object are automatically determined. The proposed method overcomes the need of offline learning and learns the deformation and motion model of the object using very few initial frames of the input video. The performance of the method is demonstrated using varieties of videos of different objects.

© 2013 Optical Society of America

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2013 (1)

2012 (2)

D. K. Prasad, “Survey of the problem of object detection in real images,” Int. J. Image Process. 6, 441–466 (2012).

D. K. Prasad, M. K. H. Leung, C. Quek, and S.-Y. Cho, “A novel framework for making dominant point detection methods non-parametric,” Image Vision Comput. 30, 843–859 (2012).
[CrossRef]

2011 (2)

A. Y.-S. Chia, S. Zhuo, R. K. Gupta, Y.-W. Tai, S.-Y. Cho, P. Tan, and S. Lin, “Semantic colorization with internet images,” ACM Trans. Graph. 30, 1 (2011).
[CrossRef]

N. Papadakis and A. Bugeau, “Tracking with occlusions via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 144–157 (2011).
[CrossRef]

2010 (4)

M. Blume, A. Martinez-Moller, A. Keil, N. Navab, and M. Rafecas, “Joint reconstruction of image and motion in gated positron emission tomography,” IEEE Trans. Med. Imaging 29, 1892–1906 (2010).
[CrossRef]

Y. N. Wu, Z. Z. Si, H. F. Gong, and S. C. Zhu, “Learning active basis model for object detection and recognition,” Int. J. Comput. Vision 90, 198–235 (2010).
[CrossRef]

V. Ferrari, F. Jurie, and C. Schmid, “From images to shape models for object detection,” Int. J. Comput. Vis. 87, 284–303 (2010).
[CrossRef]

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010).
[CrossRef]

2009 (4)

G. Park, J.-H. Jung, K. Hong, Y. Kim, Y.-H. Kim, S.-W. Min, and B. Lee, “Multi-viewer tracking integral imaging system and its viewing zone analysis,” Opt. Express 17, 17895–17908 (2009).
[CrossRef]

X. Zhang and J. Yang, “Moving object detection based on shape prediction,” J. Opt. Soc. Am. A 26, 342–349 (2009).
[CrossRef]

T. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu, “Sketch2photo: internet image montage,” ACM Trans. Graphics 28, 124 (2009).

K. Katija and J. O. Dabiri, “A viscosity-enhanced mechanism for biogenic ocean mixing,” Nature 460, 624–626 (2009).
[CrossRef]

2008 (4)

A. Basarab, H. Liebgott, F. Morestin, A. Lyshchik, T. Higashi, R. Asato, and P. Delachartre, “A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease,” Med. Image Anal. 12, 259–274 (2008).
[CrossRef]

R. Han, Z. Jing, and Y. Li, “Kernel based visual tracking with scale invariant features,” Chin. Opt. Lett. 6, 168–171 (2008).
[CrossRef]

N. Papadakis and E. Memin, “A variational technique for time consistent tracking of curves and motion,” J. Math. Imaging Vision 31, 81–103 (2008).
[CrossRef]

T. F. Cootes, C. J. Twining, K. O. Babalola, and C. J. Taylor, “Diffeomorphic statistical shape models,” Image Vision Comput. 26, 326–332 (2008).
[CrossRef]

2007 (3)

P. Bhowmick and B. B. Bhattacharya, “Fast polygonal approximation of digital curves using relaxed straightness properties,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 1590–1602 (2007).
[CrossRef]

J. Ling, E. Liu, H. Liang, and J. Yang, “Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure,” Appl. Opt. 46, 3239–3252 (2007).
[CrossRef]

A. Kolesnikov and P. Fränti, “Polygonal approximation of closed discrete curves,” Pattern Recogn. 40, 1282–1293 (2007).
[CrossRef]

2006 (3)

H. Hua, P. Krishnaswamy, and J. P. Rolland, “Video-based eyetracking methods and algorithms in head-mounted displays,” Opt. Express 14, 4328–4350 (2006).
[CrossRef]

D. Cremers, “Dynamical statistical shape priors for level set-based tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1262–1273 (2006).
[CrossRef]

T. Amiaz and N. Kiryati, “Piecewise-smooth dense optical flow via level sets,” Int. J. Comput. Vision 68, 111–124 (2006).
[CrossRef]

2005 (1)

2004 (1)

C. Rother, V. Kolmogorov, and A. Blake, ““Grabcut”: interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23, 309–314 (2004).
[CrossRef]

2002 (1)

E. Memin and P. Perez, “Hierarchical estimation and segmentation of dense motion fields,” Int. J. Comput. Vis. 46, 129–155 (2002).
[CrossRef]

2001 (1)

T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Process. 10, 266–277 (2001).
[CrossRef]

Amiaz, T.

T. Amiaz and N. Kiryati, “Piecewise-smooth dense optical flow via level sets,” Int. J. Comput. Vision 68, 111–124 (2006).
[CrossRef]

Asato, R.

A. Basarab, H. Liebgott, F. Morestin, A. Lyshchik, T. Higashi, R. Asato, and P. Delachartre, “A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease,” Med. Image Anal. 12, 259–274 (2008).
[CrossRef]

Babalola, K. O.

T. F. Cootes, C. J. Twining, K. O. Babalola, and C. J. Taylor, “Diffeomorphic statistical shape models,” Image Vision Comput. 26, 326–332 (2008).
[CrossRef]

Bartesaghi, A.

A. Bartesaghi and G. Sapiro, “Tracking of moving objects under severe and total occlusions,” in IEEE International Conference on Image Processing, 2005 (ICIP 2005) (2005), vol. 1, paper I-301-4.

Basarab, A.

A. Basarab, H. Liebgott, F. Morestin, A. Lyshchik, T. Higashi, R. Asato, and P. Delachartre, “A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease,” Med. Image Anal. 12, 259–274 (2008).
[CrossRef]

Bhattacharya, B. B.

P. Bhowmick and B. B. Bhattacharya, “Fast polygonal approximation of digital curves using relaxed straightness properties,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 1590–1602 (2007).
[CrossRef]

Bhowmick, P.

P. Bhowmick and B. B. Bhattacharya, “Fast polygonal approximation of digital curves using relaxed straightness properties,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 1590–1602 (2007).
[CrossRef]

Blake, A.

C. Rother, V. Kolmogorov, and A. Blake, ““Grabcut”: interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23, 309–314 (2004).
[CrossRef]

Blume, M.

M. Blume, A. Martinez-Moller, A. Keil, N. Navab, and M. Rafecas, “Joint reconstruction of image and motion in gated positron emission tomography,” IEEE Trans. Med. Imaging 29, 1892–1906 (2010).
[CrossRef]

Bugeau, A.

N. Papadakis and A. Bugeau, “Tracking with occlusions via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 144–157 (2011).
[CrossRef]

Chan, T. F.

T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Process. 10, 266–277 (2001).
[CrossRef]

Chen, T.

T. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu, “Sketch2photo: internet image montage,” ACM Trans. Graphics 28, 124 (2009).

Cheng, M.-M.

T. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu, “Sketch2photo: internet image montage,” ACM Trans. Graphics 28, 124 (2009).

Chia, A. Y.-S.

A. Y.-S. Chia, S. Zhuo, R. K. Gupta, Y.-W. Tai, S.-Y. Cho, P. Tan, and S. Lin, “Semantic colorization with internet images,” ACM Trans. Graph. 30, 1 (2011).
[CrossRef]

Cho, S.-Y.

D. K. Prasad, M. K. H. Leung, C. Quek, and S.-Y. Cho, “A novel framework for making dominant point detection methods non-parametric,” Image Vision Comput. 30, 843–859 (2012).
[CrossRef]

A. Y.-S. Chia, S. Zhuo, R. K. Gupta, Y.-W. Tai, S.-Y. Cho, P. Tan, and S. Lin, “Semantic colorization with internet images,” ACM Trans. Graph. 30, 1 (2011).
[CrossRef]

Cootes, T. F.

T. F. Cootes, C. J. Twining, K. O. Babalola, and C. J. Taylor, “Diffeomorphic statistical shape models,” Image Vision Comput. 26, 326–332 (2008).
[CrossRef]

Cremers, D.

D. Cremers, “Dynamical statistical shape priors for level set-based tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1262–1273 (2006).
[CrossRef]

Dabiri, J. O.

K. Katija and J. O. Dabiri, “A viscosity-enhanced mechanism for biogenic ocean mixing,” Nature 460, 624–626 (2009).
[CrossRef]

Delachartre, P.

A. Basarab, H. Liebgott, F. Morestin, A. Lyshchik, T. Higashi, R. Asato, and P. Delachartre, “A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease,” Med. Image Anal. 12, 259–274 (2008).
[CrossRef]

Felzenszwalb, P. F.

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010).
[CrossRef]

Ferrari, V.

V. Ferrari, F. Jurie, and C. Schmid, “From images to shape models for object detection,” Int. J. Comput. Vis. 87, 284–303 (2010).
[CrossRef]

Fit’o, N. V.

Fränti, P.

A. Kolesnikov and P. Fränti, “Polygonal approximation of closed discrete curves,” Pattern Recogn. 40, 1282–1293 (2007).
[CrossRef]

Girshick, R. B.

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010).
[CrossRef]

Gong, H. F.

Y. N. Wu, Z. Z. Si, H. F. Gong, and S. C. Zhu, “Learning active basis model for object detection and recognition,” Int. J. Comput. Vision 90, 198–235 (2010).
[CrossRef]

Gupta, R. K.

A. Y.-S. Chia, S. Zhuo, R. K. Gupta, Y.-W. Tai, S.-Y. Cho, P. Tan, and S. Lin, “Semantic colorization with internet images,” ACM Trans. Graph. 30, 1 (2011).
[CrossRef]

Han, R.

Higashi, T.

A. Basarab, H. Liebgott, F. Morestin, A. Lyshchik, T. Higashi, R. Asato, and P. Delachartre, “A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease,” Med. Image Anal. 12, 259–274 (2008).
[CrossRef]

Hong, K.

Hu, S.-M.

T. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu, “Sketch2photo: internet image montage,” ACM Trans. Graphics 28, 124 (2009).

Hua, H.

Javed, O.

A. Yilmaz, O. Javed, and M. Shah, “Object tracking: a survey,” ACM Comput. Surv.38 (2006).
[CrossRef]

Jing, Z.

Jung, J.-H.

Jurie, F.

V. Ferrari, F. Jurie, and C. Schmid, “From images to shape models for object detection,” Int. J. Comput. Vis. 87, 284–303 (2010).
[CrossRef]

Katija, K.

K. Katija and J. O. Dabiri, “A viscosity-enhanced mechanism for biogenic ocean mixing,” Nature 460, 624–626 (2009).
[CrossRef]

Keil, A.

M. Blume, A. Martinez-Moller, A. Keil, N. Navab, and M. Rafecas, “Joint reconstruction of image and motion in gated positron emission tomography,” IEEE Trans. Med. Imaging 29, 1892–1906 (2010).
[CrossRef]

Kim, Y.

Kim, Y.-H.

Kiryati, N.

T. Amiaz and N. Kiryati, “Piecewise-smooth dense optical flow via level sets,” Int. J. Comput. Vision 68, 111–124 (2006).
[CrossRef]

Kolesnikov, A.

A. Kolesnikov and P. Fränti, “Polygonal approximation of closed discrete curves,” Pattern Recogn. 40, 1282–1293 (2007).
[CrossRef]

Kolmogorov, V.

C. Rother, V. Kolmogorov, and A. Blake, ““Grabcut”: interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23, 309–314 (2004).
[CrossRef]

Krishnaswamy, P.

Lee, B.

Leung, M. K. H.

D. K. Prasad, M. K. H. Leung, C. Quek, and S.-Y. Cho, “A novel framework for making dominant point detection methods non-parametric,” Image Vision Comput. 30, 843–859 (2012).
[CrossRef]

D. K. Prasad and M. K. H. Leung, “Polygonal representation of digital curves,” in Digital Image Processing, S. G. Stanciu, ed. (InTech, 2012).

D. K. Prasad and M. K. H. Leung, “Reliability/precision uncertainity in shape fitting problems,” in 2010 17th IEEE International Conference on Image Processing (ICIP) (2010), pp. 4277–4280.

Li, Y.

Liang, H.

Liebgott, H.

A. Basarab, H. Liebgott, F. Morestin, A. Lyshchik, T. Higashi, R. Asato, and P. Delachartre, “A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease,” Med. Image Anal. 12, 259–274 (2008).
[CrossRef]

Lin, S.

A. Y.-S. Chia, S. Zhuo, R. K. Gupta, Y.-W. Tai, S.-Y. Cho, P. Tan, and S. Lin, “Semantic colorization with internet images,” ACM Trans. Graph. 30, 1 (2011).
[CrossRef]

Ling, J.

Liu, E.

Liu, S.

Lyshchik, A.

A. Basarab, H. Liebgott, F. Morestin, A. Lyshchik, T. Higashi, R. Asato, and P. Delachartre, “A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease,” Med. Image Anal. 12, 259–274 (2008).
[CrossRef]

Malcolm, J.

J. Malcolm, Y. Rathi, and A. Tannenbaum, “Multi-object tracking through clutter using graph cuts,” in IEEE 11th International Conference on Computer Vision, 2007 (ICCV 2007) (2007), pp. 1–5.

Martinez-Moller, A.

M. Blume, A. Martinez-Moller, A. Keil, N. Navab, and M. Rafecas, “Joint reconstruction of image and motion in gated positron emission tomography,” IEEE Trans. Med. Imaging 29, 1892–1906 (2010).
[CrossRef]

McAllester, D.

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010).
[CrossRef]

Memin, E.

N. Papadakis and E. Memin, “A variational technique for time consistent tracking of curves and motion,” J. Math. Imaging Vision 31, 81–103 (2008).
[CrossRef]

E. Memin and P. Perez, “Hierarchical estimation and segmentation of dense motion fields,” Int. J. Comput. Vis. 46, 129–155 (2002).
[CrossRef]

Min, S.-W.

Morestin, F.

A. Basarab, H. Liebgott, F. Morestin, A. Lyshchik, T. Higashi, R. Asato, and P. Delachartre, “A method for vector displacement estimation with ultrasound imaging and its application for thyroid nodular disease,” Med. Image Anal. 12, 259–274 (2008).
[CrossRef]

Muravskii, L. I.

Navab, N.

M. Blume, A. Martinez-Moller, A. Keil, N. Navab, and M. Rafecas, “Joint reconstruction of image and motion in gated positron emission tomography,” IEEE Trans. Med. Imaging 29, 1892–1906 (2010).
[CrossRef]

Papadakis, N.

N. Papadakis and A. Bugeau, “Tracking with occlusions via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 144–157 (2011).
[CrossRef]

N. Papadakis and E. Memin, “A variational technique for time consistent tracking of curves and motion,” J. Math. Imaging Vision 31, 81–103 (2008).
[CrossRef]

Park, G.

Perez, P.

E. Memin and P. Perez, “Hierarchical estimation and segmentation of dense motion fields,” Int. J. Comput. Vis. 46, 129–155 (2002).
[CrossRef]

Petrinec, K.

K. Petrinec, “Visible-surface reconstruction: identification of surface discontinuities using snakes,” Tech. Rep. , University of California, Los Angeles (2010).

Prasad, D. K.

D. K. Prasad, “Survey of the problem of object detection in real images,” Int. J. Image Process. 6, 441–466 (2012).

D. K. Prasad, M. K. H. Leung, C. Quek, and S.-Y. Cho, “A novel framework for making dominant point detection methods non-parametric,” Image Vision Comput. 30, 843–859 (2012).
[CrossRef]

D. K. Prasad and M. K. H. Leung, “Polygonal representation of digital curves,” in Digital Image Processing, S. G. Stanciu, ed. (InTech, 2012).

D. K. Prasad, “Geometric primitive feature extraction—concepts, algorithms, and applications,” Ph.D. thesis (Nanyang Technological University, 2012).

D. K. Prasad and M. K. H. Leung, “Reliability/precision uncertainity in shape fitting problems,” in 2010 17th IEEE International Conference on Image Processing (ICIP) (2010), pp. 4277–4280.

Quek, C.

D. K. Prasad, M. K. H. Leung, C. Quek, and S.-Y. Cho, “A novel framework for making dominant point detection methods non-parametric,” Image Vision Comput. 30, 843–859 (2012).
[CrossRef]

Rafecas, M.

M. Blume, A. Martinez-Moller, A. Keil, N. Navab, and M. Rafecas, “Joint reconstruction of image and motion in gated positron emission tomography,” IEEE Trans. Med. Imaging 29, 1892–1906 (2010).
[CrossRef]

Ramanan, D.

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010).
[CrossRef]

Rathi, Y.

J. Malcolm, Y. Rathi, and A. Tannenbaum, “Multi-object tracking through clutter using graph cuts,” in IEEE 11th International Conference on Computer Vision, 2007 (ICCV 2007) (2007), pp. 1–5.

Rolland, J. P.

Rother, C.

C. Rother, V. Kolmogorov, and A. Blake, ““Grabcut”: interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23, 309–314 (2004).
[CrossRef]

Sapiro, G.

A. Bartesaghi and G. Sapiro, “Tracking of moving objects under severe and total occlusions,” in IEEE International Conference on Image Processing, 2005 (ICIP 2005) (2005), vol. 1, paper I-301-4.

Schmid, C.

V. Ferrari, F. Jurie, and C. Schmid, “From images to shape models for object detection,” Int. J. Comput. Vis. 87, 284–303 (2010).
[CrossRef]

Shah, M.

A. Yilmaz, O. Javed, and M. Shah, “Object tracking: a survey,” ACM Comput. Surv.38 (2006).
[CrossRef]

Shamir, A.

T. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu, “Sketch2photo: internet image montage,” ACM Trans. Graphics 28, 124 (2009).

Si, Z. Z.

Y. N. Wu, Z. Z. Si, H. F. Gong, and S. C. Zhu, “Learning active basis model for object detection and recognition,” Int. J. Comput. Vision 90, 198–235 (2010).
[CrossRef]

Tai, Y.-W.

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Supplementary Material (12)

» Media 1: AVI (2544 KB)     
» Media 2: AVI (2531 KB)     
» Media 3: AVI (2767 KB)     
» Media 4: AVI (1382 KB)     
» Media 5: AVI (1340 KB)     
» Media 6: AVI (461 KB)     
» Media 7: AVI (1057 KB)     
» Media 8: AVI (1039 KB)     
» Media 9: AVI (1036 KB)     
» Media 10: AVI (2990 KB)     
» Media 11: AVI (2916 KB)     
» Media 12: AVI (3122 KB)     

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Figures (7)

Fig. 1.
Fig. 1.

Dominant point representation of shape of a digital contour.

Fig. 2.
Fig. 2.

Example of large deformation. It is seen that the propagated dominant points may cluster up or separate after a few frames in the regions with large local deformation.

Fig. 3.
Fig. 3.

Effect of correction described in Subsection 3.A. The areas that suffer from clustering (separation) of dominant points are highlighted in blue (red) dashed boxes in (b).

Fig. 4.
Fig. 4.

Bouncing Egg ball. Regions in cyan show the segmentation and shape obtained by various methods. From top to bottom, row 1, our method (Media 1); row 2, method used in [9] (Media 2); row 3, method used in [10] (Media 3).

Fig. 5.
Fig. 5.

Mug to Torus. Top row, object assumed stationary (Media 4). Bottom row, no assumption about motion (Media 5). Regions in cyan show the shapes segmented by our method.

Fig. 6.
Fig. 6.

Synthetic Video with occlusion (Media 6). Regions in cyan show the segmentation and shape obtained by various methods. From top to bottom: row 1, our method (Media 7); row 2, method used in [9] (Media 8); row 3, method used in [10] (Media 9).

Fig. 7.
Fig. 7.

Complex example of Jelly Fish. Adapted by permission from Macmillan Publishers Ltd: K. Katija and J. O. Dabiri, Nature 460, 624-626 (2009) [31]. Regions in cyan show the segmentation and shape obtained by various methods. From top to bottom: row 1, our method (Media 10); row 2, method used in [9] (Media 11); row 3, method used in [10] (Media 12).

Tables (1)

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Table 1. Jaccard Index for Comparison of the Three Methods for Subsection 4.C

Equations (11)

Equations on this page are rendered with MathJax. Learn more.

x(yayb)+y(xbxa)+ybxayaxb=0.
dm=|xm(y1yM)+ym(xMx1)+yMx1y1xM|(xMx1)2+(y1yM)2.
ϵp=J¯·(J¯XA¯)/|A¯|,
ϵr=m|XmA¯1|/smax,
[xn,t+1yn,t+1]=[cosθsinθsinθcosθ][xn+dxn+x0yn+dyn+y0].
v¯=[dxn;ndyn;nθ].
x0=x^0,t2x^0,t1,y0=y^0,t2y^0,t1,
f=n=1Nfn(x)+n=1Nfn(y),
fn(x)=(x^n,(t+1)(ref)x˜n,t)2,fn(y)=(y^n,(t+1)(ref)y˜n,t)2,
lc=max(2,rcnPn/N),
ls=min(rs(1)max(Pn;n),rs(2)nPn/N),

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