Abstract

Obtaining accurate disparity values in textureless and texture-free regions is a very challenging task. To solve this problem, we present a novel algorithm. First, we use the guided filter method to fuse the color cost volume and the gradient cost volume. Second, we use three types of image category information to merge the different scale disparity maps and obtain the primary disparity map. Third, during the disparity refinement procedure, we also utilize the three types of category information to define different support regions and assign different weights for pixels remaining to be refined. Extensive experiments show that the performance of our method is not inferior to many state-of-the-art methods on the Middlebury data set.

© 2019 Optical Society of America

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References

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    [Crossref]
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    [Crossref]
  38. Y. Xiao, D. Xu, G. Wang, X. Hu, Y. Zhang, X. Ji, and L. Zhang, “Confidence map based 3D cost aggregation with multiple minimum spanning trees for stereo matching,” in International Conference on Computer Analysis of Images and Patterns (CAIP) (submitted).
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  40. J. Navarro and A. Buades, “Dense and robust image registration by shift adapted weighted aggregation and variational completion,” Image and Vision Computing (submitted).
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    [Crossref]
  42. R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim, “Improvement of stereo matching algorithm for 3D surface reconstruction,” Signal Process. Image Commun. 65, 165–172 (2018).
    [Crossref]
  43. H. Li, Y. Sun, and L. Sun, “Edge-preserved disparity estimation with piecewise cost aggregation,” International Journal of Geo-Information (submitted).
  44. T. Yan, Y. Gan, Z. Xia, and Q. Zhao, “Segment-based disparity refinement with occlusion handling for stereo matching,” IEEE Trans. Image Process. 28, 3885–3897 (2019).
    [Crossref]
  45. S. Safwana Abd Razak, M. Othman, and A. Kadmin, “The effect of adaptive weighted bilateral filter on stereo matching algorithm,” Int. J. Eng. Adv. Technol. 8, C5839028319 (2019).
  46. H. Li and C. Cheng, “Adaptive weighted matching cost based on sparse representation,” IEEE Transactions on Image Processing (submitted).
  47. C. Cheng, H. Li, and L. Zhang, “A new stereo matching cost based on two-branch convolutional sparse coding and sparse representation,” IEEE Transactions on Image Processing (submitted).
  48. S. Patil, T. Prakash, B. Comandur, and A. Kak, “A comparative evaluation of SGM variants for dense stereo matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence (submitted).

2019 (3)

W. Wu, H. Zhu, S. Yu, and J. Shi, “Stereo matching with fusing adaptive support weights,” IEEE Access 7, 61960–61974 (2019).
[Crossref]

T. Yan, Y. Gan, Z. Xia, and Q. Zhao, “Segment-based disparity refinement with occlusion handling for stereo matching,” IEEE Trans. Image Process. 28, 3885–3897 (2019).
[Crossref]

S. Safwana Abd Razak, M. Othman, and A. Kadmin, “The effect of adaptive weighted bilateral filter on stereo matching algorithm,” Int. J. Eng. Adv. Technol. 8, C5839028319 (2019).

2018 (3)

R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim, “Improvement of stereo matching algorithm for 3D surface reconstruction,” Signal Process. Image Commun. 65, 165–172 (2018).
[Crossref]

T. Taniai, Y. Matsushita, Y. Sato, and T. Naemura, “Continuous 3D label stereo matching using local expansion moves,” IEEE Trans. Pattern Anal. Mach. Intell. 40, 2725–2739 (2018).
[Crossref]

L. Li, S. Zhang, X. Yu, and L. Zhang, “PMSC: patchmatch-based superpixel cut for accurate stereo matching,” IEEE Trans. Circuits Syst. Video Technol. 28, 679–692 (2018).
[Crossref]

2017 (3)

S. Zhu and L. Yan, “Local stereo matching algorithm with efficient matching cost and adaptive guided image filter,” Vis. Comput. 33, 1087–1102 (2017).
[Crossref]

G. S. Hong and B. G. Kim, “A local stereo matching algorithm based on weighted guided image filtering for improving the generation of depth range image,” Displays 49, 80–87 (2017).
[Crossref]

H. Ma, S. Zheng, C. Li, Y. Li, L. Gui, and R. Huang, “Cross-scale cost aggregation integrating intra-scale smoothness constraint with weighted least squares in stereo matching,” J. Opt. Soc. Am. A 34, 648–656 (2017).
[Crossref]

2016 (4)

H. Shi, H. Zhu, J. Wang, S. Y. Yu, and Z. F. Fu, “Segment-based adaptive window and multi-feature fusion for stereo matching,” J. Algorithms Comput. Technol. 10, 3–200 (2016).
[Crossref]

R. A. Hamzah, H. Ibrahim, and A. H. A. Hassan, “Stereo matching algorithm based on illumination control to improve the accuracy,” Image Anal. Stereol. 35, 39–52 (2016).
[Crossref]

J. Bontar and Y. Lecun, “Stereo matching by training a convolutional neural network to compare image patches,” J. Mach. Learn. Res. 17, 2287–2318 (2016).

X. Huang and Y. J. Zhang, “An O(1) disparity refinement method for stereo matching,” Pattern Recogn. 55, 198–206 (2016).
[Crossref]

2015 (2)

S. Zhu and Z. Li, “Local stereo matching using combined matching cost and adaptive cost aggregation,” TIIS 9, 224–241 (2015).
[Crossref]

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2015).
[Crossref]

2014 (2)

Q. Yang, “Hardware-efficient bilateral filtering for stereo matching,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 1026–1032 (2014).
[Crossref]

F. Besse, C. Rother, A. Fitzgibbon, and J. Kautz, “PMBP: PatchMatch belief propagation for correspondence field estimation,” Int. J. Comput. Vis. 110, 2–13 (2014)
[Crossref]

2011 (1)

T. Brox and J. Malik, “Large displacement optical flow: descriptor matching in variational motion estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 500–513 (2011).
[Crossref]

2009 (1)

K. Zhang, J. Lu, and G. Lafruit, “Cross-based local stereo matching using orthogonal integral images,” IEEE Trans. Circuits Syst. Video Technol. 19, 1073–1079 (2009).
[Crossref]

2006 (1)

K. J. Yoon and I. S. Kweon, “Adaptive support-weight approach for correspondence search,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 650–656 (2006).
[Crossref]

2004 (1)

G. Egnal, M. Mintz, and R. P. Wildes, “A stereo confidence metric using single view imagery with comparison to five alternative approaches,” Image Vision Comput. 22, 943–957 (2004).
[Crossref]

2003 (1)

J. Sun, N. N. Zheng, and H. Y. Shum, “Stereo matching using belief propagation,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 787–800 (2003).
[Crossref]

2002 (2)

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

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002).
[Crossref]

2001 (2)

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001).
[Crossref]

K. Briechle and U. D. Hanebeck, “Template matching using fast normalized cross correlation,” Proc. SPIE 4387, 95–102 (2001).
[Crossref]

Addimanda, E.

F. Tombari, S. Mattoccia, L. D. Stefano, and E. Addimanda, “Classification and evaluation of cost aggregation methods for stereo correspondence,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008), pp. 1–8.

Batsos, K.

K. Batsos, C. Cai, and P. Mordohai, “CBMV: a coalesced bidirectional matching volume for disparity estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018), pp. 2060–2069.

Besse, F.

F. Besse, C. Rother, A. Fitzgibbon, and J. Kautz, “PMBP: PatchMatch belief propagation for correspondence field estimation,” Int. J. Comput. Vis. 110, 2–13 (2014)
[Crossref]

Bleyer, M.

M. Bleyer, C. Rhemann, and C. Rother, “PatchMatch stereo–stereo matching with slanted support windows,” in British Machine Vision Conference (BMVA) (2011), pp. 1–11.

C. Rhemann, A. Hosni, and M. Bleyer, “Fast cost-volume filtering for visual correspondence and beyond,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011), pp. 3017–3024.

Bontar, J.

J. Bontar and Y. Lecun, “Stereo matching by training a convolutional neural network to compare image patches,” J. Mach. Learn. Res. 17, 2287–2318 (2016).

Boykov, Y.

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001).
[Crossref]

Briechle, K.

K. Briechle and U. D. Hanebeck, “Template matching using fast normalized cross correlation,” Proc. SPIE 4387, 95–102 (2001).
[Crossref]

Brown, M. S.

Y. Li, D. Min, M. S. Brown, M. N. Do, and J. Lu, “SPM-BP: sped-up PatchMatch belief propagation for continuous MRFs,” in International Conference on Computer Vision (ICCV) (2015), pp. 4006–4014.

Brox, T.

T. Brox and J. Malik, “Large displacement optical flow: descriptor matching in variational motion estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 500–513 (2011).
[Crossref]

Buades, A.

J. Navarro and A. Buades, “Dense and robust image registration by shift adapted weighted aggregation and variational completion,” Image and Vision Computing (submitted).

Cai, C.

K. Batsos, C. Cai, and P. Mordohai, “CBMV: a coalesced bidirectional matching volume for disparity estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018), pp. 2060–2069.

Cheng, C.

H. Li and C. Cheng, “Adaptive weighted matching cost based on sparse representation,” IEEE Transactions on Image Processing (submitted).

C. Cheng, H. Li, and L. Zhang, “A new stereo matching cost based on two-branch convolutional sparse coding and sparse representation,” IEEE Transactions on Image Processing (submitted).

Comandur, B.

S. Patil, T. Prakash, B. Comandur, and A. Kak, “A comparative evaluation of SGM variants for dense stereo matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence (submitted).

Comaniciu, D.

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002).
[Crossref]

Do, M. N.

Y. Li, D. Min, M. S. Brown, M. N. Do, and J. Lu, “SPM-BP: sped-up PatchMatch belief propagation for continuous MRFs,” in International Conference on Computer Vision (ICCV) (2015), pp. 4006–4014.

Dong, W.

X. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang, “Segment-tree based cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (IEEE, 2013), pp. 313–320.

Du, Y.

G. Zhao, Y. Du, and Y. Tang, “Adaptive rank transform for stereo matching,” in International Conference on Intelligent Robotics and Applications (2011), pp. 95–104.

Egnal, G.

G. Egnal, M. Mintz, and R. P. Wildes, “A stereo confidence metric using single view imagery with comparison to five alternative approaches,” Image Vision Comput. 22, 943–957 (2004).
[Crossref]

Fakhar, S.

R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim, “Improvement of stereo matching algorithm for 3D surface reconstruction,” Signal Process. Image Commun. 65, 165–172 (2018).
[Crossref]

Fang, Y.

K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan, and Q. Tian, “Cross-scale cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (2014), pp. 1590–1597.

Fitzgibbon, A.

F. Besse, C. Rother, A. Fitzgibbon, and J. Kautz, “PMBP: PatchMatch belief propagation for correspondence field estimation,” Int. J. Comput. Vis. 110, 2–13 (2014)
[Crossref]

Fu, Z. F.

H. Shi, H. Zhu, J. Wang, S. Y. Yu, and Z. F. Fu, “Segment-based adaptive window and multi-feature fusion for stereo matching,” J. Algorithms Comput. Technol. 10, 3–200 (2016).
[Crossref]

Gan, Y.

T. Yan, Y. Gan, Z. Xia, and Q. Zhao, “Segment-based disparity refinement with occlusion handling for stereo matching,” IEEE Trans. Image Process. 28, 3885–3897 (2019).
[Crossref]

Ghani, A.

R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim, “Improvement of stereo matching algorithm for 3D surface reconstruction,” Signal Process. Image Commun. 65, 165–172 (2018).
[Crossref]

Gu, Y.

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2015).
[Crossref]

Gui, L.

Hamid, M.

R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim, “Improvement of stereo matching algorithm for 3D surface reconstruction,” Signal Process. Image Commun. 65, 165–172 (2018).
[Crossref]

Hamzah, R.

R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim, “Improvement of stereo matching algorithm for 3D surface reconstruction,” Signal Process. Image Commun. 65, 165–172 (2018).
[Crossref]

Hamzah, R. A.

R. A. Hamzah, H. Ibrahim, and A. H. A. Hassan, “Stereo matching algorithm based on illumination control to improve the accuracy,” Image Anal. Stereol. 35, 39–52 (2016).
[Crossref]

Hanebeck, U. D.

K. Briechle and U. D. Hanebeck, “Template matching using fast normalized cross correlation,” Proc. SPIE 4387, 95–102 (2001).
[Crossref]

Hassan, A. H. A.

R. A. Hamzah, H. Ibrahim, and A. H. A. Hassan, “Stereo matching algorithm based on illumination control to improve the accuracy,” Image Anal. Stereol. 35, 39–52 (2016).
[Crossref]

He, K.

Z. Ma, K. He, Y. Wei, J. Sun, and E. Wu, “Constant time weighted median filtering for stereo matching and beyond,” in IEEE International Conference on Computer Vision (2014), pp. 49–56.

K. He, J. Sun, and X. Tang, “Guided image filtering,” in European Conference on Computer Vision (2010), pp. 1–14.

Hong, G. S.

G. S. Hong and B. G. Kim, “A local stereo matching algorithm based on weighted guided image filtering for improving the generation of depth range image,” Displays 49, 80–87 (2017).
[Crossref]

Hosni, A.

C. Rhemann, A. Hosni, and M. Bleyer, “Fast cost-volume filtering for visual correspondence and beyond,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011), pp. 3017–3024.

Hu, K.

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2015).
[Crossref]

Hu, X.

Y. Xiao, D. Xu, G. Wang, X. Hu, Y. Zhang, X. Ji, and L. Zhang, “Confidence map based 3D cost aggregation with multiple minimum spanning trees for stereo matching,” in International Conference on Computer Analysis of Images and Patterns (CAIP) (submitted).

Huang, K.

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2015).
[Crossref]

Huang, R.

Huang, X.

X. Huang and Y. J. Zhang, “An O(1) disparity refinement method for stereo matching,” Pattern Recogn. 55, 198–206 (2016).
[Crossref]

Ibrahim, H.

R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim, “Improvement of stereo matching algorithm for 3D surface reconstruction,” Signal Process. Image Commun. 65, 165–172 (2018).
[Crossref]

R. A. Hamzah, H. Ibrahim, and A. H. A. Hassan, “Stereo matching algorithm based on illumination control to improve the accuracy,” Image Anal. Stereol. 35, 39–52 (2016).
[Crossref]

Ji, X.

Y. Xiao, D. Xu, G. Wang, X. Hu, Y. Zhang, X. Ji, and L. Zhang, “Confidence map based 3D cost aggregation with multiple minimum spanning trees for stereo matching,” in International Conference on Computer Analysis of Images and Patterns (CAIP) (submitted).

Jiao, S.

X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in IEEE International Conference on Computer Vision Workshops (IEEE, 2012), pp. 467–474.

Kadmin, A.

S. Safwana Abd Razak, M. Othman, and A. Kadmin, “The effect of adaptive weighted bilateral filter on stereo matching algorithm,” Int. J. Eng. Adv. Technol. 8, C5839028319 (2019).

R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim, “Improvement of stereo matching algorithm for 3D surface reconstruction,” Signal Process. Image Commun. 65, 165–172 (2018).
[Crossref]

Kak, A.

S. Patil, T. Prakash, B. Comandur, and A. Kak, “A comparative evaluation of SGM variants for dense stereo matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence (submitted).

Kautz, J.

F. Besse, C. Rother, A. Fitzgibbon, and J. Kautz, “PMBP: PatchMatch belief propagation for correspondence field estimation,” Int. J. Comput. Vis. 110, 2–13 (2014)
[Crossref]

Kim, B. G.

G. S. Hong and B. G. Kim, “A local stereo matching algorithm based on weighted guided image filtering for improving the generation of depth range image,” Displays 49, 80–87 (2017).
[Crossref]

Kweon, I. S.

K. J. Yoon and I. S. Kweon, “Adaptive support-weight approach for correspondence search,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 650–656 (2006).
[Crossref]

Lafruit, G.

K. Zhang, J. Lu, and G. Lafruit, “Cross-based local stereo matching using orthogonal integral images,” IEEE Trans. Circuits Syst. Video Technol. 19, 1073–1079 (2009).
[Crossref]

Lecun, Y.

J. Bontar and Y. Lecun, “Stereo matching by training a convolutional neural network to compare image patches,” J. Mach. Learn. Res. 17, 2287–2318 (2016).

Lei, C.

C. Lei and Y. H. Yang, “Optical flow estimation on coarse-to-fine region-trees using discrete optimization,” in IEEE International Conference on Computer Vision (ICCV) (2009), pp. 1562–1569.

Li, C.

Li, H.

H. Li and C. Cheng, “Adaptive weighted matching cost based on sparse representation,” IEEE Transactions on Image Processing (submitted).

C. Cheng, H. Li, and L. Zhang, “A new stereo matching cost based on two-branch convolutional sparse coding and sparse representation,” IEEE Transactions on Image Processing (submitted).

H. Li, Y. Sun, and L. Sun, “Edge-preserved disparity estimation with piecewise cost aggregation,” International Journal of Geo-Information (submitted).

Li, L.

L. Li, S. Zhang, X. Yu, and L. Zhang, “PMSC: patchmatch-based superpixel cut for accurate stereo matching,” IEEE Trans. Circuits Syst. Video Technol. 28, 679–692 (2018).
[Crossref]

Li, Y.

H. Ma, S. Zheng, C. Li, Y. Li, L. Gui, and R. Huang, “Cross-scale cost aggregation integrating intra-scale smoothness constraint with weighted least squares in stereo matching,” J. Opt. Soc. Am. A 34, 648–656 (2017).
[Crossref]

Y. Li, D. Min, M. S. Brown, M. N. Do, and J. Lu, “SPM-BP: sped-up PatchMatch belief propagation for continuous MRFs,” in International Conference on Computer Vision (ICCV) (2015), pp. 4006–4014.

Li, Z.

S. Zhu and Z. Li, “Local stereo matching using combined matching cost and adaptive cost aggregation,” TIIS 9, 224–241 (2015).
[Crossref]

Lu, J.

K. Zhang, J. Lu, and G. Lafruit, “Cross-based local stereo matching using orthogonal integral images,” IEEE Trans. Circuits Syst. Video Technol. 19, 1073–1079 (2009).
[Crossref]

Y. Li, D. Min, M. S. Brown, M. N. Do, and J. Lu, “SPM-BP: sped-up PatchMatch belief propagation for continuous MRFs,” in International Conference on Computer Vision (ICCV) (2015), pp. 4006–4014.

Ma, H.

Ma, Z.

Z. Ma, K. He, Y. Wei, J. Sun, and E. Wu, “Constant time weighted median filtering for stereo matching and beyond,” in IEEE International Conference on Computer Vision (2014), pp. 49–56.

Malik, J.

T. Brox and J. Malik, “Large displacement optical flow: descriptor matching in variational motion estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 500–513 (2011).
[Crossref]

Matsushita, Y.

T. Taniai, Y. Matsushita, Y. Sato, and T. Naemura, “Continuous 3D label stereo matching using local expansion moves,” IEEE Trans. Pattern Anal. Mach. Intell. 40, 2725–2739 (2018).
[Crossref]

T. Taniai, Y. Matsushita, and T. Naemura, “Graph cut based continuous stereo matching using locally shared labels,” in Conference on Computer Vision and Pattern Recognition (2014), pp. 1613–1620.

Mattoccia, S.

F. Tombari, S. Mattoccia, L. D. Stefano, and E. Addimanda, “Classification and evaluation of cost aggregation methods for stereo correspondence,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008), pp. 1–8.

Meer, P.

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002).
[Crossref]

Mei, X.

X. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang, “Segment-tree based cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (IEEE, 2013), pp. 313–320.

X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in IEEE International Conference on Computer Vision Workshops (IEEE, 2012), pp. 467–474.

Min, D.

K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan, and Q. Tian, “Cross-scale cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (2014), pp. 1590–1597.

Y. Li, D. Min, M. S. Brown, M. N. Do, and J. Lu, “SPM-BP: sped-up PatchMatch belief propagation for continuous MRFs,” in International Conference on Computer Vision (ICCV) (2015), pp. 4006–4014.

Mintz, M.

G. Egnal, M. Mintz, and R. P. Wildes, “A stereo confidence metric using single view imagery with comparison to five alternative approaches,” Image Vision Comput. 22, 943–957 (2004).
[Crossref]

Mordohai, P.

K. Batsos, C. Cai, and P. Mordohai, “CBMV: a coalesced bidirectional matching volume for disparity estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018), pp. 2060–2069.

Naemura, T.

T. Taniai, Y. Matsushita, Y. Sato, and T. Naemura, “Continuous 3D label stereo matching using local expansion moves,” IEEE Trans. Pattern Anal. Mach. Intell. 40, 2725–2739 (2018).
[Crossref]

T. Taniai, Y. Matsushita, and T. Naemura, “Graph cut based continuous stereo matching using locally shared labels,” in Conference on Computer Vision and Pattern Recognition (2014), pp. 1613–1620.

Navarro, J.

J. Navarro and A. Buades, “Dense and robust image registration by shift adapted weighted aggregation and variational completion,” Image and Vision Computing (submitted).

Othman, M.

S. Safwana Abd Razak, M. Othman, and A. Kadmin, “The effect of adaptive weighted bilateral filter on stereo matching algorithm,” Int. J. Eng. Adv. Technol. 8, C5839028319 (2019).

Patil, S.

S. Patil, T. Prakash, B. Comandur, and A. Kak, “A comparative evaluation of SGM variants for dense stereo matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence (submitted).

Prakash, T.

S. Patil, T. Prakash, B. Comandur, and A. Kak, “A comparative evaluation of SGM variants for dense stereo matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence (submitted).

Rhemann, C.

C. Rhemann, A. Hosni, and M. Bleyer, “Fast cost-volume filtering for visual correspondence and beyond,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011), pp. 3017–3024.

M. Bleyer, C. Rhemann, and C. Rother, “PatchMatch stereo–stereo matching with slanted support windows,” in British Machine Vision Conference (BMVA) (2011), pp. 1–11.

Rother, C.

F. Besse, C. Rother, A. Fitzgibbon, and J. Kautz, “PMBP: PatchMatch belief propagation for correspondence field estimation,” Int. J. Comput. Vis. 110, 2–13 (2014)
[Crossref]

M. Bleyer, C. Rhemann, and C. Rother, “PatchMatch stereo–stereo matching with slanted support windows,” in British Machine Vision Conference (BMVA) (2011), pp. 1–11.

Safwana Abd Razak, S.

S. Safwana Abd Razak, M. Othman, and A. Kadmin, “The effect of adaptive weighted bilateral filter on stereo matching algorithm,” Int. J. Eng. Adv. Technol. 8, C5839028319 (2019).

Sato, Y.

T. Taniai, Y. Matsushita, Y. Sato, and T. Naemura, “Continuous 3D label stereo matching using local expansion moves,” IEEE Trans. Pattern Anal. Mach. Intell. 40, 2725–2739 (2018).
[Crossref]

Scharstein, D.

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

Shi, H.

H. Shi, H. Zhu, J. Wang, S. Y. Yu, and Z. F. Fu, “Segment-based adaptive window and multi-feature fusion for stereo matching,” J. Algorithms Comput. Technol. 10, 3–200 (2016).
[Crossref]

Shi, J.

W. Wu, H. Zhu, S. Yu, and J. Shi, “Stereo matching with fusing adaptive support weights,” IEEE Access 7, 61960–61974 (2019).
[Crossref]

Shum, H. Y.

J. Sun, N. N. Zheng, and H. Y. Shum, “Stereo matching using belief propagation,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 787–800 (2003).
[Crossref]

Stefano, L. D.

F. Tombari, S. Mattoccia, L. D. Stefano, and E. Addimanda, “Classification and evaluation of cost aggregation methods for stereo correspondence,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008), pp. 1–8.

Sun, J.

J. Sun, N. N. Zheng, and H. Y. Shum, “Stereo matching using belief propagation,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 787–800 (2003).
[Crossref]

Z. Ma, K. He, Y. Wei, J. Sun, and E. Wu, “Constant time weighted median filtering for stereo matching and beyond,” in IEEE International Conference on Computer Vision (2014), pp. 49–56.

K. He, J. Sun, and X. Tang, “Guided image filtering,” in European Conference on Computer Vision (2010), pp. 1–14.

Sun, L.

K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan, and Q. Tian, “Cross-scale cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (2014), pp. 1590–1597.

H. Li, Y. Sun, and L. Sun, “Edge-preserved disparity estimation with piecewise cost aggregation,” International Journal of Geo-Information (submitted).

Sun, X.

X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in IEEE International Conference on Computer Vision Workshops (IEEE, 2012), pp. 467–474.

X. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang, “Segment-tree based cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (IEEE, 2013), pp. 313–320.

Sun, Y.

H. Li, Y. Sun, and L. Sun, “Edge-preserved disparity estimation with piecewise cost aggregation,” International Journal of Geo-Information (submitted).

Szeliski, R.

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

Tang, X.

K. He, J. Sun, and X. Tang, “Guided image filtering,” in European Conference on Computer Vision (2010), pp. 1–14.

Tang, Y.

G. Zhao, Y. Du, and Y. Tang, “Adaptive rank transform for stereo matching,” in International Conference on Intelligent Robotics and Applications (2011), pp. 95–104.

Taniai, T.

T. Taniai, Y. Matsushita, Y. Sato, and T. Naemura, “Continuous 3D label stereo matching using local expansion moves,” IEEE Trans. Pattern Anal. Mach. Intell. 40, 2725–2739 (2018).
[Crossref]

T. Taniai, Y. Matsushita, and T. Naemura, “Graph cut based continuous stereo matching using locally shared labels,” in Conference on Computer Vision and Pattern Recognition (2014), pp. 1613–1620.

Tian, Q.

K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan, and Q. Tian, “Cross-scale cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (2014), pp. 1590–1597.

Tombari, F.

F. Tombari, S. Mattoccia, L. D. Stefano, and E. Addimanda, “Classification and evaluation of cost aggregation methods for stereo correspondence,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008), pp. 1–8.

Veksler, O.

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001).
[Crossref]

O. Veksler, “Fast variable window for stereo correspondence using integral images,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2003), Vol. 1, pp. I-556–I-561.

Wang, G.

Y. Xiao, D. Xu, G. Wang, X. Hu, Y. Zhang, X. Ji, and L. Zhang, “Confidence map based 3D cost aggregation with multiple minimum spanning trees for stereo matching,” in International Conference on Computer Analysis of Images and Patterns (CAIP) (submitted).

Wang, H.

X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in IEEE International Conference on Computer Vision Workshops (IEEE, 2012), pp. 467–474.

X. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang, “Segment-tree based cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (IEEE, 2013), pp. 313–320.

Wang, J.

H. Shi, H. Zhu, J. Wang, S. Y. Yu, and Z. F. Fu, “Segment-based adaptive window and multi-feature fusion for stereo matching,” J. Algorithms Comput. Technol. 10, 3–200 (2016).
[Crossref]

Wei, Y.

Z. Ma, K. He, Y. Wei, J. Sun, and E. Wu, “Constant time weighted median filtering for stereo matching and beyond,” in IEEE International Conference on Computer Vision (2014), pp. 49–56.

Wildes, R. P.

G. Egnal, M. Mintz, and R. P. Wildes, “A stereo confidence metric using single view imagery with comparison to five alternative approaches,” Image Vision Comput. 22, 943–957 (2004).
[Crossref]

Woodfill, J.

R. Zabih and J. Woodfill, “Non-parametric local transforms for computing visual correspondence,” in European Conference on Computer Vision (1994), pp. 151–158.

Wu, E.

Z. Ma, K. He, Y. Wei, J. Sun, and E. Wu, “Constant time weighted median filtering for stereo matching and beyond,” in IEEE International Conference on Computer Vision (2014), pp. 49–56.

Wu, W.

W. Wu, H. Zhu, S. Yu, and J. Shi, “Stereo matching with fusing adaptive support weights,” IEEE Access 7, 61960–61974 (2019).
[Crossref]

Xia, Z.

T. Yan, Y. Gan, Z. Xia, and Q. Zhao, “Segment-based disparity refinement with occlusion handling for stereo matching,” IEEE Trans. Image Process. 28, 3885–3897 (2019).
[Crossref]

Xiao, Y.

Y. Xiao, D. Xu, G. Wang, X. Hu, Y. Zhang, X. Ji, and L. Zhang, “Confidence map based 3D cost aggregation with multiple minimum spanning trees for stereo matching,” in International Conference on Computer Analysis of Images and Patterns (CAIP) (submitted).

Xu, D.

Y. Xiao, D. Xu, G. Wang, X. Hu, Y. Zhang, X. Ji, and L. Zhang, “Confidence map based 3D cost aggregation with multiple minimum spanning trees for stereo matching,” in International Conference on Computer Analysis of Images and Patterns (CAIP) (submitted).

Yan, L.

S. Zhu and L. Yan, “Local stereo matching algorithm with efficient matching cost and adaptive guided image filter,” Vis. Comput. 33, 1087–1102 (2017).
[Crossref]

Yan, S.

K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan, and Q. Tian, “Cross-scale cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (2014), pp. 1590–1597.

Yan, T.

T. Yan, Y. Gan, Z. Xia, and Q. Zhao, “Segment-based disparity refinement with occlusion handling for stereo matching,” IEEE Trans. Image Process. 28, 3885–3897 (2019).
[Crossref]

Yang, Q.

Q. Yang, “Hardware-efficient bilateral filtering for stereo matching,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 1026–1032 (2014).
[Crossref]

Q. Yang, “A non-local cost aggregation method for stereo matching,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012), pp. 1402–1409.

Yang, S.

K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan, and Q. Tian, “Cross-scale cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (2014), pp. 1590–1597.

Yang, Y. H.

C. Lei and Y. H. Yang, “Optical flow estimation on coarse-to-fine region-trees using discrete optimization,” in IEEE International Conference on Computer Vision (ICCV) (2009), pp. 1562–1569.

Yoon, K. J.

K. J. Yoon and I. S. Kweon, “Adaptive support-weight approach for correspondence search,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 650–656 (2006).
[Crossref]

Yu, S.

W. Wu, H. Zhu, S. Yu, and J. Shi, “Stereo matching with fusing adaptive support weights,” IEEE Access 7, 61960–61974 (2019).
[Crossref]

Yu, S. Y.

H. Shi, H. Zhu, J. Wang, S. Y. Yu, and Z. F. Fu, “Segment-based adaptive window and multi-feature fusion for stereo matching,” J. Algorithms Comput. Technol. 10, 3–200 (2016).
[Crossref]

Yu, X.

L. Li, S. Zhang, X. Yu, and L. Zhang, “PMSC: patchmatch-based superpixel cut for accurate stereo matching,” IEEE Trans. Circuits Syst. Video Technol. 28, 679–692 (2018).
[Crossref]

Zabih, R.

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

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001).
[Crossref]

R. Zabih and J. Woodfill, “Non-parametric local transforms for computing visual correspondence,” in European Conference on Computer Vision (1994), pp. 151–158.

Zhan, Y.

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2015).
[Crossref]

Zhang, C.

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2015).
[Crossref]

Zhang, K.

K. Zhang, J. Lu, and G. Lafruit, “Cross-based local stereo matching using orthogonal integral images,” IEEE Trans. Circuits Syst. Video Technol. 19, 1073–1079 (2009).
[Crossref]

K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan, and Q. Tian, “Cross-scale cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (2014), pp. 1590–1597.

Zhang, L.

L. Li, S. Zhang, X. Yu, and L. Zhang, “PMSC: patchmatch-based superpixel cut for accurate stereo matching,” IEEE Trans. Circuits Syst. Video Technol. 28, 679–692 (2018).
[Crossref]

Y. Xiao, D. Xu, G. Wang, X. Hu, Y. Zhang, X. Ji, and L. Zhang, “Confidence map based 3D cost aggregation with multiple minimum spanning trees for stereo matching,” in International Conference on Computer Analysis of Images and Patterns (CAIP) (submitted).

C. Cheng, H. Li, and L. Zhang, “A new stereo matching cost based on two-branch convolutional sparse coding and sparse representation,” IEEE Transactions on Image Processing (submitted).

Zhang, S.

L. Li, S. Zhang, X. Yu, and L. Zhang, “PMSC: patchmatch-based superpixel cut for accurate stereo matching,” IEEE Trans. Circuits Syst. Video Technol. 28, 679–692 (2018).
[Crossref]

Zhang, X.

X. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang, “Segment-tree based cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (IEEE, 2013), pp. 313–320.

X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in IEEE International Conference on Computer Vision Workshops (IEEE, 2012), pp. 467–474.

Zhang, Y.

Y. Xiao, D. Xu, G. Wang, X. Hu, Y. Zhang, X. Ji, and L. Zhang, “Confidence map based 3D cost aggregation with multiple minimum spanning trees for stereo matching,” in International Conference on Computer Analysis of Images and Patterns (CAIP) (submitted).

Zhang, Y. J.

X. Huang and Y. J. Zhang, “An O(1) disparity refinement method for stereo matching,” Pattern Recogn. 55, 198–206 (2016).
[Crossref]

Zhao, G.

G. Zhao, Y. Du, and Y. Tang, “Adaptive rank transform for stereo matching,” in International Conference on Intelligent Robotics and Applications (2011), pp. 95–104.

Zhao, Q.

T. Yan, Y. Gan, Z. Xia, and Q. Zhao, “Segment-based disparity refinement with occlusion handling for stereo matching,” IEEE Trans. Image Process. 28, 3885–3897 (2019).
[Crossref]

Zheng, N. N.

J. Sun, N. N. Zheng, and H. Y. Shum, “Stereo matching using belief propagation,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 787–800 (2003).
[Crossref]

Zheng, S.

Zhou, M.

X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in IEEE International Conference on Computer Vision Workshops (IEEE, 2012), pp. 467–474.

Zhu, H.

W. Wu, H. Zhu, S. Yu, and J. Shi, “Stereo matching with fusing adaptive support weights,” IEEE Access 7, 61960–61974 (2019).
[Crossref]

H. Shi, H. Zhu, J. Wang, S. Y. Yu, and Z. F. Fu, “Segment-based adaptive window and multi-feature fusion for stereo matching,” J. Algorithms Comput. Technol. 10, 3–200 (2016).
[Crossref]

Zhu, S.

S. Zhu and L. Yan, “Local stereo matching algorithm with efficient matching cost and adaptive guided image filter,” Vis. Comput. 33, 1087–1102 (2017).
[Crossref]

S. Zhu and Z. Li, “Local stereo matching using combined matching cost and adaptive cost aggregation,” TIIS 9, 224–241 (2015).
[Crossref]

Displays (1)

G. S. Hong and B. G. Kim, “A local stereo matching algorithm based on weighted guided image filtering for improving the generation of depth range image,” Displays 49, 80–87 (2017).
[Crossref]

IEEE Access (1)

W. Wu, H. Zhu, S. Yu, and J. Shi, “Stereo matching with fusing adaptive support weights,” IEEE Access 7, 61960–61974 (2019).
[Crossref]

IEEE Trans. Circuits Syst. Video Technol. (3)

Y. Zhan, Y. Gu, K. Huang, C. Zhang, and K. Hu, “Accurate image-guided stereo matching with efficient matching cost and disparity refinement,” IEEE Trans. Circuits Syst. Video Technol. 26, 1632–1645 (2015).
[Crossref]

K. Zhang, J. Lu, and G. Lafruit, “Cross-based local stereo matching using orthogonal integral images,” IEEE Trans. Circuits Syst. Video Technol. 19, 1073–1079 (2009).
[Crossref]

L. Li, S. Zhang, X. Yu, and L. Zhang, “PMSC: patchmatch-based superpixel cut for accurate stereo matching,” IEEE Trans. Circuits Syst. Video Technol. 28, 679–692 (2018).
[Crossref]

IEEE Trans. Image Process. (1)

T. Yan, Y. Gan, Z. Xia, and Q. Zhao, “Segment-based disparity refinement with occlusion handling for stereo matching,” IEEE Trans. Image Process. 28, 3885–3897 (2019).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (7)

T. Taniai, Y. Matsushita, Y. Sato, and T. Naemura, “Continuous 3D label stereo matching using local expansion moves,” IEEE Trans. Pattern Anal. Mach. Intell. 40, 2725–2739 (2018).
[Crossref]

T. Brox and J. Malik, “Large displacement optical flow: descriptor matching in variational motion estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 500–513 (2011).
[Crossref]

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002).
[Crossref]

J. Sun, N. N. Zheng, and H. Y. Shum, “Stereo matching using belief propagation,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 787–800 (2003).
[Crossref]

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001).
[Crossref]

K. J. Yoon and I. S. Kweon, “Adaptive support-weight approach for correspondence search,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 650–656 (2006).
[Crossref]

Q. Yang, “Hardware-efficient bilateral filtering for stereo matching,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 1026–1032 (2014).
[Crossref]

Image Anal. Stereol. (1)

R. A. Hamzah, H. Ibrahim, and A. H. A. Hassan, “Stereo matching algorithm based on illumination control to improve the accuracy,” Image Anal. Stereol. 35, 39–52 (2016).
[Crossref]

Image Vision Comput. (1)

G. Egnal, M. Mintz, and R. P. Wildes, “A stereo confidence metric using single view imagery with comparison to five alternative approaches,” Image Vision Comput. 22, 943–957 (2004).
[Crossref]

Int. J. Comput. Vis. (2)

F. Besse, C. Rother, A. Fitzgibbon, and J. Kautz, “PMBP: PatchMatch belief propagation for correspondence field estimation,” Int. J. Comput. Vis. 110, 2–13 (2014)
[Crossref]

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

Int. J. Eng. Adv. Technol. (1)

S. Safwana Abd Razak, M. Othman, and A. Kadmin, “The effect of adaptive weighted bilateral filter on stereo matching algorithm,” Int. J. Eng. Adv. Technol. 8, C5839028319 (2019).

J. Algorithms Comput. Technol. (1)

H. Shi, H. Zhu, J. Wang, S. Y. Yu, and Z. F. Fu, “Segment-based adaptive window and multi-feature fusion for stereo matching,” J. Algorithms Comput. Technol. 10, 3–200 (2016).
[Crossref]

J. Mach. Learn. Res. (1)

J. Bontar and Y. Lecun, “Stereo matching by training a convolutional neural network to compare image patches,” J. Mach. Learn. Res. 17, 2287–2318 (2016).

J. Opt. Soc. Am. A (1)

Pattern Recogn. (1)

X. Huang and Y. J. Zhang, “An O(1) disparity refinement method for stereo matching,” Pattern Recogn. 55, 198–206 (2016).
[Crossref]

Proc. SPIE (1)

K. Briechle and U. D. Hanebeck, “Template matching using fast normalized cross correlation,” Proc. SPIE 4387, 95–102 (2001).
[Crossref]

Signal Process. Image Commun. (1)

R. Hamzah, A. Kadmin, M. Hamid, S. Fakhar, A. Ghani, and H. Ibrahim, “Improvement of stereo matching algorithm for 3D surface reconstruction,” Signal Process. Image Commun. 65, 165–172 (2018).
[Crossref]

TIIS (1)

S. Zhu and Z. Li, “Local stereo matching using combined matching cost and adaptive cost aggregation,” TIIS 9, 224–241 (2015).
[Crossref]

Vis. Comput. (1)

S. Zhu and L. Yan, “Local stereo matching algorithm with efficient matching cost and adaptive guided image filter,” Vis. Comput. 33, 1087–1102 (2017).
[Crossref]

Other (22)

O. Veksler, “Fast variable window for stereo correspondence using integral images,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2003), Vol. 1, pp. I-556–I-561.

C. Rhemann, A. Hosni, and M. Bleyer, “Fast cost-volume filtering for visual correspondence and beyond,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011), pp. 3017–3024.

K. He, J. Sun, and X. Tang, “Guided image filtering,” in European Conference on Computer Vision (2010), pp. 1–14.

X. Mei, X. Sun, M. Zhou, S. Jiao, H. Wang, and X. Zhang, “On building an accurate stereo matching system on graphics hardware,” in IEEE International Conference on Computer Vision Workshops (IEEE, 2012), pp. 467–474.

F. Tombari, S. Mattoccia, L. D. Stefano, and E. Addimanda, “Classification and evaluation of cost aggregation methods for stereo correspondence,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008), pp. 1–8.

G. Zhao, Y. Du, and Y. Tang, “Adaptive rank transform for stereo matching,” in International Conference on Intelligent Robotics and Applications (2011), pp. 95–104.

R. Zabih and J. Woodfill, “Non-parametric local transforms for computing visual correspondence,” in European Conference on Computer Vision (1994), pp. 151–158.

Z. Ma, K. He, Y. Wei, J. Sun, and E. Wu, “Constant time weighted median filtering for stereo matching and beyond,” in IEEE International Conference on Computer Vision (2014), pp. 49–56.

Y. Li, D. Min, M. S. Brown, M. N. Do, and J. Lu, “SPM-BP: sped-up PatchMatch belief propagation for continuous MRFs,” in International Conference on Computer Vision (ICCV) (2015), pp. 4006–4014.

C. Lei and Y. H. Yang, “Optical flow estimation on coarse-to-fine region-trees using discrete optimization,” in IEEE International Conference on Computer Vision (ICCV) (2009), pp. 1562–1569.

Q. Yang, “A non-local cost aggregation method for stereo matching,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012), pp. 1402–1409.

X. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang, “Segment-tree based cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (IEEE, 2013), pp. 313–320.

K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan, and Q. Tian, “Cross-scale cost aggregation for stereo matching,” in Computer Vision and Pattern Recognition (2014), pp. 1590–1597.

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Figures (15)

Fig. 1.
Fig. 1. Overview of the proposed method.
Fig. 2.
Fig. 2. Disparity maps under different cost volumes. (a), (c) Disparity maps under single color cost volume; (b), (d) disparity maps under single gradient cost volume.
Fig. 3.
Fig. 3. Disparity maps under different cost volume merging algorithms. (a), (c) Disparity maps under the fixed proportion merging method of [15]; (b), (d) disparity maps under our fused cost volume. All results are obtained under the guided filter cost aggregation method.
Fig. 4.
Fig. 4. Disparity maps under different scales. (a) Disparity maps obtained under the original scale, (b) disparity maps obtained under the half-scale, and (c) disparity maps obtained under our disparity merging method.
Fig. 5.
Fig. 5. Pixel category images. (a) Original left image, (b) segmentation category image by mean shift, (c) texture category image, and (d) the LRC category image.
Fig. 6.
Fig. 6. Edge regions detection. (a) First filled disparity map, (b) canny edge of left image, (c) ground-truth disparity map, and (d) the location relationship of the expanded edge region and the true edge.
Fig. 7.
Fig. 7. Disparity limitation on edge region cost aggregation.
Fig. 8.
Fig. 8. Disparity map under different weight computation algorithms. (a) Disparity map before edge optimization, (b) disparity maps under the color and distance weights and without the disparity limitation, and (c) disparity maps under our weight assignment method.
Fig. 9.
Fig. 9. Support region setting for disparity filling. (a) Horizontal support region, (b) rectangle support region, (c) eight scan lines support region, (d) cross-support region. Pixels with red edges form the support regions.
Fig. 10.
Fig. 10. Results of every procedure under the proposed method. (a) Primary disparity maps, (b) first filled disparity maps, (c) edge-optimized disparity maps, (d) second filled disparity maps.
Fig. 11.
Fig. 11. Results of the proposed method: (a), (c), and (e) original images; (b), (d), and (f) the final results of our method (red pixels are error pixels). The original images from left to right are Adirondack, Midd1, and Dolls.
Fig. 12.
Fig. 12. Comparison results between CSCA and the proposed method. (a) Left images, (b) disparity maps under CSCA, and (c) disparity maps under the proposed method.
Fig. 13.
Fig. 13. Comparison of disparity maps generated from different algorithms. Error pixels are marked in red. (a) Left image, (b) PMBP, (c) SPM-BP, (d) GCLCL, (e) PMSC, and (f) our proposed method. Best viewed with zoom-in on a digital display.
Fig. 14.
Fig. 14. Comparison of disparity maps generated from different algorithms. Error pixels are marked in red. (a) Left image, (b) PatchMatch, (c) PMF, (d) LocalExp, (e) our proposed. Best viewed with zoom-in on a digital display.
Fig. 15.
Fig. 15. Comparison of disparity maps generated from different algorithms on Middlebury 2014. Error pixels are marked in red. From top to bottom are the results of 3DMST-CM [38], CBMV-ROB [39], DAWA-F [40], FASW [41], ISM [42], PWCA-SGM [43], SDR [44], SM-AWP [45], SMSSR [46], TCSCSM [47], Ref. [48], and our proposed method. Images from left to right are Adirondack, Jade Plant, Motorcycle, Piano, Pipes, Playroom, PlaytableP, Recycle, Shelves, and Teddy. Best viewed with zoom-in on a digital display.

Tables (7)

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Table 1. First Disparity Filling Process

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Table 2. Second Disparity Filling Process

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Table 3. Parameters Set in our Experiments

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Table 4. Computation Time in Seconds

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Table 5. Comparison Result between CostFilter and the Proposed Algorithms

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Table 6. Comparison Result under Middlebury 2006 Data Set on Non-occluded Regions a

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Table 7. Average Error Rates of the 10 Images on Middlebury 2014 Data Sets in Non-occluded Regions a

Equations (19)

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C A D ( p , d ) = min ( 1 3 i ( R , G , B ) | I i left ( p ) I i right ( p d ) | , τ A D ) ,
C G D ( p , d ) = x C G D ( p , d ) + y C G D ( p , d ) ,
x C G D ( p , d ) = min ( | x I gray left ( p ) x I gray right ( p d ) | , τ G D ) ,
y C G D ( p , d ) = min ( | y I gray left ( p ) y I gray right ( p d ) | , τ G D ) .
C ( p , d ) = ( 1 α ) C A D ( p , d ) + α C G D ( p , d ) .
C ( p , d ) = a k × C A D ( p , d ) + b k p Ω ( x ) ,
C ( p , d ) = C G D ( p , d ) n p ,
E ( a k , b k ) = p Ω ( x ) ( ( a k × C A D ( p , d ) + b k C G D ( p , d ) ) 2 + ε a k 2 ) ,
a k = 1 | w | p Ω ( x ) C A D ( p , d ) × C G D ( p , d ) μ × C G D ¯ δ 2 + ε ,
b k = C G D ¯ a k μ ,
T ( p ) = 1 | w | q Ω ( p ) t ( q ) ,
t ( q ) = { 1 if S ( p ) = S ( q ) 0 otherwise ,
C ( p , d ) = q Ω ( p ) w ( p , q ) × C ( p , d ) ,
Col ( p , q ) = exp ( c ( R , G , B ) | I c ( q ) I c ( p ) | λ 1 ) ,
D ( p , q ) = exp ( | x q x p | + | y q y p | r 7 ) ,
Dis ( p , q ) = exp ( disp ( q ) λ 2 ) .
w ( p , q ) = { Dis ( p , q ) D ( p , q ) Col ( p , q ) if disp ¯ fill 1 numDisp 2 D ( p , q ) Col ( p , q ) if disp ¯ fill 1 > numDisp 2 Col ( p , q ) if S ( q ) = S ( p ) and disp fill 1 ( q ) disp fill 1 ( p ) 0 if LRC ( q ) = 1 ,
w ( p , q ) = { 0 if disp ¯ fill 1 > numDisp 2 and disp fill 1 ( q ) < disp ¯ fill 1 λ 3 Col ( p , q ) otherwise .
similar ( p , q ) = c ( R , G , B ) | I c ( q ) I c ( p ) | ,

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