Acta Optica Sinica, Volume. 38, Issue 2, 0215006(2018)
Stereo Matching Algorithm for Improved Census Transform and Gradient Fusion
Figures & Tables(16)
Fig. 1. Support window coordinates and weight distribution map with size of 3×3 and standard deviation of 1.5. (a) coordinate distribution map; (b) weight coordinate distribution map; (c) weight distribution map
Fig. 2. Disparity maps before and after improvement by guidance filter algorithm. (a)(c) before improvement; (b)(d) after improvement
Fig. 3. Disparity maps obtained by different matching cost algorithms. (a) CT; (b) MCT; (c) GRD;(d) proposed algorithm
Fig. 4. Disparity maps obtained by different aggregation algorithms. (a) BoxF, R=10.81%; (b) BF, R=8.12%;(c) GF, R=7.85%; (d) MST, R=8.31%; (e) proposed algorithm, R=7.57%
Fig. 5. Comparison of false matching rates of different cost aggregation algorithms in no-occluded regions
Fig. 6. Test results of different stereo matching algorithms on Aloe image pairs. (a) Aloe left image;(b) Aloe right image; (c) GRD, R=10.19%; (d) MCT, R=10.07%; (e) MCT', R=9.74%; (f) proposed algorithm, R=6.74%
Fig. 7. Test results of different stereo matching algorithms on Baby1 image pairs. (a) Baby1 left image; (b) Baby1 right image; (c) GRD, R=12.82%; (d) MCT, R=4.91%; (e) MCT', R=4.53%; (f) proposed algorithm, R=3.75%
Fig. 8. Test results of different stereo matching algorithms on Bowling2 image pairs. (a) Bowling2 left image; (b) Bowling2 right image; (c) GRD, R=12.91%; (d) MCT, R=15.77%; (e) MCT', R=14.21%; (f) proposed algorithm, R=9.04%
Fig. 9. Test results of different stereo matching algorithms on Dolls image pairs. (a) Dolls left image; (b) Dolls right image; (c) GRD, R=7.66%;(d) MCT, R=11.55%; (e) MCT', R=11.87%; (f) proposed algorithm, R=5.77%
Fig. 10. Experimental results of proposed algorithm on Middlebury2.0 image pairs. (a) Testing left image; (b) standard disparity map; (c) disparity map of proposed algorithm(without disparity refinement); (d) mismatched map (without disparity refinement); (e) disparity maps obtained by proposed algorithm (disparity refinement); (f) mismatched map (disparity refinement)
Table 1. Parameters involved in proposed stereo matching algorithm
View tableTable 1. Parameters involved in proposed stereo matching algorithm
Parameter ωk T gε σ T hω 1T cenλ Gω 2Value 9 4.335 1×10-5 1.5 1 0.9 45 0.065025 0.1
Table 2. Percentage of false match in no-occluded region of different matching cost algorithms%
View tableTable 2. Percentage of false match in no-occluded region of different matching cost algorithms%
Algorithm SAD CT MCT MCT’ GRD Proposed algorithm Tsukuba 4.65 3.54 4.58 4.50 2.72 2.53 Venus 3.45 2.00 3.63 3.55 1.68 1.60 Teddy 14.21 8.56 13.05 11.32 7.45 7.57 Cones 7.70 4.87 7.49 6.78 4.45 4.04 Avg 7.50 4.74 7.19 6.54 4.08 3.93
Table 3. Percentage of false match in all regions of different matching cost algorithms%
View tableTable 3. Percentage of false match in all regions of different matching cost algorithms%
Algorithm SAD CT MCT MCT’ GRD Proposed algorithm Tsukuba 4.97 3.12 3.36 3.59 3.59 2.86 Venus 8.34 5.25 7.17 7.15 4.12 4.13 Teddy 25.54 19.28 23.48 22.76 17.56 17.27 Cones 23.40 17.04 19.71 18.88 16.13 16.06 Avg 15.56 11.17 13.43 13.10 10.35 10.08
Table 5. False matching rates of different stereo matching algorithms in no-occlusion region and all regions%
View tableTable 5. False matching rates of different stereo matching algorithms in no-occlusion region and all regions%
Algorithm Tsukuba Venus Teddy Cones Average No-occluded All No-occluded All No-occluded All No-occluded All BPcompressed[ 21 ]2.68 3.63 1.33 1.89 8.36 13.9 3.71 9.85 5.67 GC-occ[ 5 ]1.19 2.01 1.64 2.19 11.20 17.40 5.36 12.4 6.67 AdaptAggrDP[ 22 ]1.57 3.50 1.53 2.69 6.79 14.3 5.53 13.2 6.14 RTCensus[ 23 ]5.08 6.25 1.58 2.42 7.96 13.8 4.10 9.54 6.34 FastAggreg[ 24 ]1.16 2.11 4.03 4.75 9.04 15.2 5.37 12.6 6.78 SemiGlob[ 25 ]3.26 3.96 1.00 1.57 6.02 12.2 3.06 9.75 5.10 GradAdaptWgt[ 26 ]2.26 2.63 6.99 1.39 8.00 13.10 2.61 7.67 5.58 Proposed algorithm 2.52 3.23 0.23 1.05 5.70 12.35 3.29 9.75 4.74
Table 6. False matching rate of different stereo matching algorithms in no-occluded region
View tableTable 6. False matching rate of different stereo matching algorithms in no-occluded region
Stereo pairs False matching rate /% MCT[ 16 ]MCT'[ 17 ]MST[ 6 ]AW[ 13 ]GF[ 14 ]CT-GF[ 15 ]CT-MST[ 15 ]Proposed algorithm Aloe Art Baby1 Baby2 Baby3 Books Bowling1 Bowling2 Cloth1 Cloth2 Cloth3 Cloth4 Dolls Flowerpots Lampshade1 Lampshade2 Laundry Midd1 Midd2 Moebius Monopoly Plastic Reindeer Rocks1 9.49 18.61 4.29 6.02 6.99 12.26 18.22 11.15 1.98 5.79 4.05 3.58 11.43 14.27 24.73 27.60 24.77 48.75 47.70 14.16 28.49 44.40 13.83 5.38 9.31 19.21 3.91 5.98 6.72 11.34 17.23 11.16 1.87 5.21 4.14 3.64 12.15 15.21 25.43 27.77 25.21 40.14 40.01 13.86 27.97 39.43 12.44 5.19 6.61 13.74 7.65 14.12 9.53 12.97 19.14 12.63 1.18 5.40 3.20 2.53 7.86 17.27 11.29 14.20 19.39 18.96 20.21 11.33 16.85 30.30 11.96 4.14 6.50 12.88 5.45 13.64 8.76 12.65 16.87 10.43 1.25 5.56 3.81 3.21 6.43 15.44 12.36 17.44 16.89 36.47 35.56 13.26 15.43 32.44 11.73 5.43 7.06 12.03 3.16 3.97 4.71 10.21 6.21 8.18 1.88 4.20 2.79 2.25 7.12 9.63 12.12 12.48 20.64 36.95 34.02 10.64 19.74 27.06 7.84 3.97 7.04 12.16 3.31 4.03 4.97 9.96 6.18 8.47 1.96 4.83 2.80 2.38 7.27 9.67 10.93 10.54 18.54 33.45 32.96 10.92 18.99 22.22 8.15 4.03 6.57 13.86 7.91 13.67 9.47 12.47 19.58 12.58 1.15 5.36 3.16 2.40 7.62 17.05 11.37 14.08 17.86 19.21 19.32 11.66 16.64 31.06 11.92 3.97 5.93 10.16 2.53 2.66 3.91 8.01 6.07 5.88 1.24 3.43 2.29 1.64 5.26 7.53 10.04 11.16 15.51 31.76 31.90 9.57 19.50 25.80 6.89 2.86 Rocks2 Wood1 Wood2 Average 4.13 6.49 4.41 15.67 3.24 7.17 4.37 14.79 3.33 11.17 4.20 11.52 5.02 8.99 6.15 12.59 2.67 4.93 2.71 10.34 2.74 4.93 2.95 9.87 3.98 10.98 4.12 11.45 1.94 3.72 2.18 8.67
Table 7. Average running time of 31 Middlebury stereo pairs with different algorithmss
View tableTable 7. Average running time of 31 Middlebury stereo pairs with different algorithmss
Method MCT[ 16 ]MCT'[ 17 ]MST[ 6 ]AW[ 13 ]GF[ 14 ]CT-GF[ 15 ]CT-MST[ 15 ]Proposed Average running time /s 3.97 4.35 1.56 15.87 4.76 6.75 4.12 6.63
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Hairui Fan, Fan Yang, Xuran Pan, Jie Wen, Xiaoyu Wang. Stereo Matching Algorithm for Improved Census Transform and Gradient Fusion[J]. Acta Optica Sinica, 2018, 38(2): 0215006
Paper Information
Category: Machine Vision
Received: Aug. 24, 2017
Accepted: --
Published Online: May. 9, 2019
The Author Email: Fan Yang (commanderjy@163.com)
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