## 4 Results  
### Improved Joint Similarity Measure Matching Experiment  
To verify the feasibility of the improved census transform rule, the region stereo matching algorithm, which is based on the traditional census transform, and the region stereo algorithm, which is based on the improved census transform, are both implemented by programming. Both algorithms do not adopt the operation of matching cost aggregation, that is, directly using the support window to compare the corresponding bit strings with the census transformation rules to obtain the difference. In this way, the difference between the two algorithms is that they use different census transform rules. The region stereo matching algorithm is based on the traditional census transform corresponding to the gray image, while the improved stereo matching algorithm is based on the improved census transform corresponding to the color image. The two matching algorithms are used to stereo-match the corrected heart model images, and the image on the left is for reference without post-processing. As shown in Figure 2, the \(31\times 31\) square pane is used as the support window. The mismatching rates [26] of the disparity map corresponding to the traditional census and the improved census are counted, respectively, as shown in Table 1.  
Figure 2: The corresponding matching results of the census before and after improvement. (**a**) Left image of heart model; (**b**) standard parallax map; (**c**) traditional (**d**) improved census corresponding parallax map. To compare the sensitivity of the traditional census algorithm and the improved census algorithm to illumination distortion, the left and right images of the two groups of heart models are adjusted, respectively. That is, 1/2 gray level, original image, and 3/2 gray level images are obtained by multiplying 1/2 and 3/2 based on the original image, respectively, and three different brightness images of one group of heart model images are shown in Figure 3.  
These images are combined in pairs; that is, there are nine combinations. The traditional census algorithm and the improved census algorithm are respectively used to carry out stereo matching operations on the nine combinations of the two sets of image pairs. The traditional census uses the gray image obtained after the gray transformation of the corresponding color image. The improved census directly uses color images after brightness adjustment to calculate its mismatching rate in different combination forms, as shown in Figure 4.  
The improved joint similarity measure is the Hamming distance between the sum of the absolute values of the gray level differences between the two pixels in the color image and the Hamming distance between the bit strings, which is obtained by using the improved census transform. To verify the feasibility of the improved joint similarity measure, the improved joint similarity measure is combined with the adaptive weight of the original stereo matching algorithm, which is based on the joint similarity measure and adaptive weight in this paper. The parallax image without post-processing is obtained with the left image for reference, as shown in Figure 5. The mismatching rates in the disparity map corresponding to the joint similarity measures before and after the improvement are respectively counted, as shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Matching Algorithm** & **Model Image 1** & **Model Image 2** & **Average Mismatch Rate** \\ \hline Traditional census & 25.64\% & 29.28\% & 27.46\% \\ Improved census & 13.57\% & 14.72\% & 14.15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The corresponding mismatching rate of the census algorithm before and after improvement. Figure 3: Left and right images of three different brightness cardiac models.