A Modified Census Transform Based on the Neighborhood Information for Stereo Matching Algorithm

2013 ◽  
Author(s):  
Li Ma ◽  
Jingjiao Li ◽  
Ji Ma ◽  
Hanyue Zhang
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Census Transform ◽  
Neighborhood Information

scholarly journals mproved Stereo Matching Algorithm based on Census Transform and Dynamic Histogram Cost Computation

2021 ◽  
Vol 11 (8) ◽  
pp. 48-57
Author(s):  
A. F. Kadmin ◽  
◽  
R. A. Hamzah ◽  
M. N. Abd Manap ◽  
M. S. Hamid ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Disparity Map ◽  
Local Method ◽  
Matching Algorithm ◽  
Census Transform ◽  
Traditional Taxonomy ◽  
Matching Cost ◽  
Winner Takes All ◽  
The Cost ◽  
Vision Algorithm

Stereo matching is a significant subject in the stereo vision algorithm. Traditional taxonomy composition consists of several issues in the stereo correspondences process such as radiometric distortion, discontinuity, and low accuracy at the low texture regions. This new taxonomy improves the local method of stereo matching algorithm based on the dynamic cost computation for disparity map measurement. This method utilised modified dynamic cost computation in the matching cost stage. A modified Census Transform with dynamic histogram is used to provide the cost volume. An adaptive bilateral filtering is applied to retain the image depth and edge information in the cost aggregation stage. A Winner Takes All (WTA) optimisation is applied in the disparity selection and a left-right check with an adaptive bilateral median filtering are employed for final refinement. Based on the dataset of standard Middlebury, the taxonomy has better accuracy and outperformed several other state-ofthe-art algorithms. Keywords—Stereo matching, disparity map, dynamic cost, census transform, local method

Stereo matching algorithm based on improved Census transform and texture filtering

Optik ◽  
2021 ◽  
pp. 168186
Author(s):  
Yuguang Hou ◽  
Changying Liu ◽  
Bowen An ◽  
Yang Liu
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Census Transform ◽  
Texture Filtering

Stereo Matching Algorithm for Improved Census Transform and Gradient Fusion

2018 ◽  
Vol 38 (2) ◽  
pp. 0215006
Author(s):  
范海瑞 Fan Hairui ◽  
杨帆 Yang Fan ◽  
潘旭冉 Pan Xuran ◽  
温洁 Wen Jie ◽  
王晓宇 Wang Xiaoyu
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Census Transform

A Parallel Stereo Matching Algorithm Core for FPGA Modeled by DSP Builder

2014 ◽  
Vol 536-537 ◽  
pp. 67-76
Author(s):  
Xiang Zhang ◽  
Zhang Wei Chen
Keyword(s):  
Stereo Matching ◽  
Cost Calculation ◽  
Stereo Pair ◽  
Disparity Map ◽  
High Definition ◽  
Matching Algorithm ◽  
Census Transform ◽  
Cost Aggregation ◽  
Dsp Builder ◽  
Fpga Chip

This paper proposes a FPGA implementation to apply a stereo matching algorithm based on a kind of sparse census transform in a FPGA chip which can provide a high-definition dense disparity map in real-time. The parallel stereo matching algorithm core involves census transform, cost calculation and cost aggregation modules. The circuits of the algorithm core are modeled by the Matlab/Simulink-based tool box: DSP Builder. The system can process many different sizes of stereo pair images through a configuration interface. The maximum horizon resolution of stereo images is 2048.

An Efficient Stereo Matching Algorithm Based on Four-Moded Census Transform for High-Resolution Images

3D Research ◽  
2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Yizhong Yang ◽  
Dong Xu ◽  
Shen Rong ◽  
Guangjun Xie ◽  
Feng Chen
Keyword(s):  
High Resolution ◽  
Stereo Matching ◽  
Matching Algorithm ◽  
Census Transform ◽  
High Resolution Images

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

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chen Lv ◽  
Jiahan Li ◽  
Qiqi Kou ◽  
Huandong Zhuang ◽  
Shoufeng Tang
Keyword(s):  
Confidence Interval ◽  
Stereo Matching ◽  
Color Space ◽  
Absolute Difference ◽  
Cost Calculation ◽  
Matching Accuracy ◽  
Matching Algorithm ◽  
Hsv Color Space ◽  
Census Transform ◽  
The Cost

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.

Anti-Noise Stereo Matching Algorithm Based on Improved Census Transform and Outlier Elimination

2017 ◽  
Vol 37 (11) ◽  
pp. 1115004
Author(s):  
彭新俊 Peng Xinjun ◽  
韩 军 Han Jun ◽  
汤 踊 Tang Yong ◽  
尚裕之 Shang Yuzhi ◽  
俞玉瑾 Yu Yujin
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Census Transform

Stereo Matching Algorithm Based on Improved Census Transform and Gradient Fusion

2021 ◽  
Vol 58 (2) ◽  
pp. 0215008
Author(s):  
萧红 Xiao Hong ◽  
田川 Tian Chuan ◽  
张毅 Zhang Yi ◽  
魏博 Wei Bo ◽  
康家旗 Kang Jiaqi
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Census Transform
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