Stereo Matching Algorithm Based on HSV Color Space and Improved Census Transform

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions.

[1]  Andrew Howard,et al.  Real-time stereo visual odometry for autonomous ground vehicles , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Margrit Gelautz,et al.  Secrets of adaptive support weight techniques for local stereo matching , 2013, Comput. Vis. Image Underst..

[3]  Tian-Sheuan Chang,et al.  Algorithm and Architecture of Disparity Estimation With Mini-Census Adaptive Support Weight , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Qiqi Kou,et al.  Principal curvatures based local binary pattern for rotation invariant texture classification , 2019, Optik.

[5]  Marsha Jo Hannah,et al.  Computer matching of areas in stereo images. , 1974 .

[6]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[7]  Qiqi Kou,et al.  Content-guided deep residual network for single image super-resolution , 2020 .

[8]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[10]  Hossein Afrakhte,et al.  Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm , 2021 .

[11]  Baljit Singh Khehra,et al.  Deep Transfer Learning Based Multiway Feature Pyramid Network for Object Detection in Images , 2021 .

[12]  Lifeng Sun,et al.  Cross-Scale Cost Aggregation for Stereo Matching , 2014, CVPR.

[13]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.