The proposed method is evaluated on Middlebury stereo datasets (Tsukuba, Venus, Teddy, and Cones) [7, 58, 117], and the results are compared with some of the existing stereo matching methods.
Figure 3.20 shows the qualitative comparison of the proposed method with the method proposed in [9]. The method proposed in [9] uses Gabor phase for disparity map computation. It shows that the proposed method produces distinctively superior results as compared to Gabor phase-based stereo matching method. This is due to the fact that Gabor phase-based method uses scalogram to find the disparity map and scalogram has poor time-frequency resolution. In addition to this, phase-based
3.5 Experimental Results
methods suffer from phase non-linearities. This is due to the existence of singularities in phase signals.
Additionally, we have shown the disparity map generated by [10] and [11]. The proposed method shows better performance as compared to the methods proposed in [10] and [11]. This is mainly due to the spatial distance used in ASW, which is same for all the pixels irrespective of the location of the pixels in homogeneous, high textured or discontinuous regions. Also, guided filter suffers from halos effect near edges. Table 3.7 gives quantitative comparisons of the proposed method with the method proposed in [9–11] and some other existing stereo matching methods. Additionally, experimental evaluation is done to justify the incorporation of disparity map refinement step in our proposed method.
Table 3.7: Comparison of the proposed method with existing local stereo matching methods (Error threshold
= 1)
Algorithm Tsukuba Venus Teddy Cones Avg. %
N occ all disc N occ all disc N occ all disc N occ all disc
Proposed method 3.36 3.83 11.8 0.43 0.71 5.13 8.59 12.8 21.6 5.05 12 14.7 8.32
RTCensus [61] 5.08 6.25 19.2 1.58 2.42 14.2 7.96 13.8 20.3 4.10 9.54 12.2 9.73
Differential [138] 4.74 6.77 19.4 1.69 2.62 20.4 8.29 10.1 23.3 4.25 10.3 12.2 10.3
GF [11] 2.98 3.43 9.24 1.93 2.36 11.0 11.9 17.1 21.8 11.9 17.4 19.9 10.9
DCBGrid [87] 5.90 7.26 21.0 1.35 1.91 11.2 10.5 17.2 22.2 5.34 11.9 14.9 10.9
RINCensus [63] 4.78 6.00 14.4 1.11 1.76 7.91 9.76 17.3 26.1 8.09 16.2 17.6 10.9
BioPsyASW [139] 3.62 5.52 14.6 3.15 4.20 20.4 11.5 18.2 23.2 4.93 13.0 11.7 11.2 Without refinement 5.84 6.73 14.1 2.81 3.93 18.5 12.2 16.3 26.6 6.88 14.2 18.8 12.2
SAD+IGMCT [62] 5.81 7.14 22.6 2.61 3.33 25.3 9.79 15.5 25.7 5.08 11.5 15.0 12.5
PhaseBased [9] 4.26 6.53 15.4 6.71 8.16 26.4 14.5 23.1 25.5 10.8 20.5 21.2 15.3
SSD+MF [58] 5.23 7.07 24.1 3.74 5.16 11.9 16.5 24.8 32.9 10.9 19.8 26.3 15.7
PhaseDiff [140] 4.89 7.11 16.3 8.34 9.76 26.0 20.0 28.0 29.0 19.8 28.5 27.5 18.8
ASW [10] 6.09 8.00 8.16 9.71 11.2 18.5 21.4 29.5 32.2 23.8 32.4 32.2 19.4
LCDM+AdaptWgt [141] 5.98 7.84 22.2 14.5 15.4 35.9 20.8 27.3 38.3 8.90 17.2 20.0 19.5
Figure 3.21 shows the qualitative results for 2005 Middlebury dataset images. As discussed ear- lier, these images have large disparity ranges. Also, there are some non-textured image regions and complicated scene geometry in the images. Hence, this new dataset is more challenging as compared to standard stereo benchmark dataset from the point of accurate estimation of disparity. The exper- imental results clearly show that the proposed algorithm also gives substantially good results for the non-textured image regions and the regions having complex geometry. Also, the proposed method can tackle large disparity ranges.
Figure 3.20: Experimental results on Middlebury datasets - Tsukuba, Venus, Teddy, and Cones. First row shows the left input images, and second row shows ground truth disparity maps corresponding to the stereo pair. Third row shows the estimated disparity maps by our proposed method. Forth row shows the disparity maps generated by the method proposed in [9], fifth and sixth rows show the disparity maps generated by the methods proposed in [10] and [11] respectively.
3.5 Experimental Results
Figure 3.21: Experimental results on 2005 Middlebury datasets - Cloth1, Book, Dolls, Laundary, Moebius, and Reindeer [12,13]. First row shows the left images, second row shows ground truth disparity maps corresponding to the stereo pair, and third row shows the generated disparity maps by our proposed method.
In order to show the effects of various parameters on the accuracy of the generated disparity map, the above experiment is repeated for different values of4
• Local stereo window size (SW);
• Kuwahara filter window size (KWS);
• Median filter window size (MWS).
The experiment is also repeated for different numbers of
• Principal components (PC);
• Gabor wavelet filter orientations (Ntheta);
• Gabor wavelet filter scaling (Nscale)
• Variation of local stereo window size: Figure 3.22 shows the percentage of errors (Nocc, all, and disc) for different values of local stereo window for Tsukuba, Venus, Teddy and Cones images. The parameters used are: SW-starting from 3×3 to 15×15, KWS - 9×9, MWS - 3×3 , %PC=20% ,Ntheta = 2, and Nscale = 2 . As the window size increases, there are more variations in the percentage of unwanted/bad pixels in the discontinuous regions as compared to
4This work has been published inEECEA 2015(Refer item [7] in Page 135 for details)
the non-occluded regions and the entire image. Figure 3.28(a) shows the average percentage of the bad pixels. The proposed method produces significantly good results even with the smallest window. This is due to the fact that the correlation of the pixels in the smaller window is more compared to the pixels in the larger window.
(a) (b)
(c) (d)
Figure 3.22: Variations of local stereo window size. (a) Tsukuba, (b) Venus, (c) Teddy, and (d) Cones.
• Variation of Kuwahara filter window size: The percentage of errors for Tsukuba, Venus, Teddy, and Cones images are shown in Figure 3.23 for different Kuwahara filter window sizes.
The parameters used are: SW - 7×7 , KWS - 5×5,9×9, 13×13, 17×17, 21×21, and 25×25, MWS - 3×3, % PC= 20%,Ntheta = 2 andNscale= 2. In this case also, there are more variations in the percentage of unwanted/bad pixels for the bigger windows in the discontinuous regions compared to the non-occluded regions and the entire image. Figure 3.28(b) shows the average percentage of the bad pixels for different Kuwahara filter window sizes. It shows that a small window produces a detailed output image.
• Variation of median filter window size: Figure 3.24 shows the percentage of errors for different window sizes of the median filter. The parameters used are: SW - 3×3, KWS - 5×5, MWS - starting from 3×3 to 15×15, % PC=20% , Ntheta = 2 and Nscale = 2. The average error percentage is shown in Figure 3.28(c). Similar to Kuwahara filter, a bigger median filter window blurs the edges, whereas a small median filter window retains the detailed information.
3.5 Experimental Results
(a) (b)
(c) (d)
Figure 3.23: Variations of Kuwahara filter window size. (a) Tsukuba, (b) Venus, (c) Teddy, and (d) Cones.
(a) (b)
(c) (d)
Figure 3.24: Variations of Median filter window size. (a) Tsukuba, (b) Venus, (c) Teddy, and (d) Cones.
• Variation of number of principal components: Figure 3.25 shows the percentage of errors for different numbers of principal components used for local stereo correspondences for all the
four images of Middlebury datasets. The parameters used are: SW - 9×9, KWS - 5×5, MWS - 3 ×3, no. of PC = 5, 10, 20, 50, 100, 150, 200, and all the coefficients, Ntheta = 2 and Nscale = 2. Figure 11 shows that error is maximum for PC = 5 and it gradually decreases for PC = 10 and 20, after that the error does not increase significantly. Figure 14(d) shows the average percentage of bad pixels. When the Eigen values are arranged in the decreasing order, the principal components corresponding to the largest Eigen values contain more information.
Figure 3.25 and 3.28(d) shows that 20 principal components corresponding to the largest Eigen values contain most important information.
(a) (b)
(c) (d)
Figure 3.25: Variations of number of principal components. (a) Tsukuba, (b) Venus, (c) Teddy, and (d) Cones.
• Variation of number of Gabor wavelet filter orientations: The percentage of errors for four Middlebury database images for different orientations of Gabor wavelet filter is shown in Figure 3.26. The parameters used are: SW - 5×5, KWS - 5×5, MWS - 3×3, % PC= 20%, Ntheta = 2,3,4,5, and Nscale = 2 . It shows that the change in error is quite insignificant with respect to the number of orientations. Change in the average error for all the four images shown in Figure 3.28(e), which remains almost constant. This is due to the fact that the percentage of principal components remains same i.e., 20% of the total number of coefficients are used in this experiment.