Acta Optica Sinica, Volume. 38, Issue 11, 1115007(2018)
Stereo Matching Method Based on Improved Cost Computation and Adaptive Guided Filter
Figures & Tables(15)
Fig. 2. Initial disparity maps based on different cost methods for Tsukuba. (a) Absolute difference in images gradients; (b) absolute difference in enhanced images gradients; (c) traditional Census transform; (d) Census transformation based on enhanced images gradients
Fig. 3. Schematic of adaptive window construction. (a) Cross-based support region construction; (b) adaptive window in Ref. [17]; (c) adaptive window in Ref. [14]; (d) adaptive window in proposed method
Fig. 4. Disparity maps under different illumination conditions for Aloe and Baby1. (a) Left image; (b) right image; (c) ground truth; (d) SAD+Grad; (e) AD+Cen; (f) AD+Grad+Cen; (g) proposed cost computation
Fig. 5. Disparity maps with different exposures for Aloe and Baby1. (a) Left image; (b) right image; (c) ground truth; (d) SAD+Grad; (e) AD+Cen; (f) AD+Grad+Cen; (g) proposed cost computation
Fig. 6. Disparity maps of different cost aggregation algorithms for textureless images. (a) Left images; (b) ground truth maps; (c) results of local stereo method based on guided filter; (d) error maps for method based on guided filter; (e) results of the proposed method; (f) error maps of the proposed method
Table 1. Experimental parameter settings
View tableTable 1. Experimental parameter settings
Parameter Value Parameter Value λ GRAD25 λ CTg15 τ 130 L 131 τ 26 L 280 d Lim9 ε 0.012
Table 2. Error matching rates of various cost computations under different illuminations%
View tableTable 2. Error matching rates of various cost computations under different illuminations%
Algorithm Aloe Baby1 Bowling1 Cloth1 Flowerpots Rocks1 Avg SAD+Grad 32.175 16.882 40.900 10.829 53.528 27.238 30.259 AD+Cen 32.274 25.055 46.147 13.212 56.000 18.732 31.903 AD+Grad+Cen 37.149 23.175 46.658 12.690 72.106 32.375 37.359 Proposed 22.034 11.115 26.946 11.333 34.185 13.849 19.910
Table 3. Error matching rates of various cost computations under different exposures%
View tableTable 3. Error matching rates of various cost computations under different exposures%
Algorithm Aloe Baby1 Bowling1 Cloth1 Flowerpots Rocks1 Avg SAD+Grad 52.510 50.672 46.434 50.178 87.562 79.773 61.188 AD+Cen 16.173 11.118 20.022 11.096 41.021 15.329 19.127 AD+Grad+Cen 31.012 30.182 31.374 13.543 77.590 44.218 37.987 Proposed 15.205 10.658 22.782 11.060 29.834 14.094 17.272
Table 4. Error matching rates of various cost computations without radiometric changes%
View tableTable 4. Error matching rates of various cost computations without radiometric changes%
Algorithm Aloe Baby1 Bowling1 Cloth1 Flowerpots Rocks1 Avg SAD+Grad 12.409 12.009 26.122 9.619 20.697 10.598 15.242 AD+Cen 13.610 11.811 23.859 10.475 22.676 12.766 15.866 AD+Grad+Cen 15.349 12.350 24.563 11.236 21.832 12.586 16.319 Proposed 14.478 9.749 18.663 11.085 18.644 12.008 14.104
Table 5. Error matching rates of different algorithms for different images%
View tableTable 5. Error matching rates of different algorithms for different images%
Algorithm Tsukuba Venus Teddy Cones Avg n-occ all disc n-occ all disc n-occ all disc n-occ all disc GF 2.21 2.59 8.56 0.32 0.68 4.31 4.77 8.62 13.1 2.53 7.90 7.67 5.27 Proposed 1.74 1.95 8.35 0.23 0.42 3.17 3.95 7.88 10.8 2.80 8.11 8.25 4.80
Table 6. Error matching rates of different algorithms in all regions%
View tableTable 6. Error matching rates of different algorithms in all regions%
Algorithm Aloe Baby1 Baby2 Baby3 Bowling1 Bowling2 GF 7.407 2.575 5.534 5.981 7.940 12.184 Proposed 8.626 4.092 10.635 6.197 14.636 14.794 Algorithm Cloth1 Cloth2 Cloth3 Cloth4 Flowerpots Lampshade1 GF 2.960 8.613 3.940 8.393 12.405 11.223 Proposed 3.225 10.418 4.332 8.454 12.696 9.540 Algorithm Lampshade2 Midd1 Midd2 Monopoly Plastic Rocks1 GF 15.729 37.653 35.381 22.803 32.666 4.183 Proposed 8.570 13.857 16.270 7.335 25.724 4.968 Algorithm Rocks2 Wood1 Wood2 Avg(all) GF 3.587 3.829 0.965 11.712 Proposed 3.973 8.574 0.484 9.400
Table 7. Error matching rates of different algorithms for textureless images%
View tableTable 7. Error matching rates of different algorithms for textureless images%
Algorithm Lampshade1 Lampshade2 Midd1 Midd2 Monopoly Plastic Avg CostFilter 23.242 31.811 48.993 45.200 36.796 43.758 38.300 CS-GF 10.720 8.634 29.127 25.892 14.439 22.178 18.498 CS-MST 14.955 16.360 18.294 17.496 30.626 37.933 22.610 CS-ST 13.201 12.188 16.072 9.587 24.053 30.724 17.638 Proposed 9.540 8.570 13.857 16.270 7.335 25.724 13.549
Table 8. Runtime comparison of different algorithms for benchmark stereo imagess
View tableTable 8. Runtime comparison of different algorithms for benchmark stereo imagess
Algorithm Tsukuba Venus Teddy Cones CostFilter 1.18 2.46 6.41 6.47 CS-GF 2.76 5.12 15.07 15.55 CS-MST 2.14 2.59 5.88 5.98 CS-ST 1.95 2.51 5.57 5.61 Proposed 3.42 5.85 14.469 14.253
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Li Yan, Rui Wang, Hua Liu, Changjun Chen. Stereo Matching Method Based on Improved Cost Computation and Adaptive Guided Filter[J]. Acta Optica Sinica, 2018, 38(11): 1115007
Paper Information
Category: Machine Vision
Received: May. 23, 2018
Accepted: Jul. 12, 2018
Published Online: May. 9, 2019
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