A Stereo Matching Algorithm with Support Regions Based on Color and Texture Estimate

2012 ◽  
Vol 433-440 ◽  
pp. 3656-3661
Author(s):  
Cheng Hui Zhu ◽  
Qi Yi Jiao ◽  
Jian Ping Wang ◽  
Xiao Bing Xu
Keyword(s):  
Stereo Matching ◽  
Experimental Results ◽  
Disparity Map ◽  
Matching Algorithm ◽  
Support Weight ◽  
Adjustment Method ◽  
Initial Support

A stereo matching algorithm with support regions based on color and texture estimate is proposed. Firstly, the initial support regions are selected from the image according to the distribution of the quantized color labels. Then, the texture similarity is used to determine the arm length growing and combine adjacent regions. The accurate support regions are obtained. Thirdly, the support weight is introduced under the constraint of support region. Finally, the initial disparity can be corrected by using disparity adjustment method until a reasonable disparity map is obtained. The experimental results show that the good disparity result can be obtained.

A Global Stereo Matching Algorithm Based on Adaptive Support-Weight and Graph Cut

2012 ◽  
Vol 151 ◽  
pp. 612-616 ◽  
Author(s):  
Guo He Yu ◽  
Jian Ming Liu ◽  
Xu Sheng Xie ◽  
Ji Guo Zeng
Keyword(s):  
Energy Function ◽  
Stereo Matching ◽  
Graph Cut ◽  
Experimental Results ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Support Weight ◽  
Depth Discontinuity ◽  
Matching Error ◽  
Adaptive Support

For the stereo matching problem in the non-texture, occluded and depth discontinuity regions, a new stereo matching algorithm that based on the adaptive support-weight of Graph Cuts is proposed. It can reduce the matching error in the depth discontinuity and non-texture regions by the single adaptive support-weight matching methods. The occlusion and smoothness penalty is considered by building the energy function. The experimental results show that the proposed algorithm can achieve more precise and reliability matching.

scholarly journals Stereo matching algorithm based on illumination normal similarity and adaptive support weight

2013 ◽  
Vol 52 (2) ◽  
pp. 027201 ◽  
Author(s):  
Kai Gao ◽  
He-xin Chen ◽  
Yan Zhao ◽  
Ying-nan Geng ◽  
Gang Wang
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Support Weight ◽  
Adaptive Support

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

Adaptive Support Weight Stereo Matching Algorithm Based on Human Visual Characteristics

2018 ◽  
Vol 55 (3) ◽  
pp. 031013
Author(s):  
刘雪松 Liu Xuesong ◽  
沈建新 Shen Jianxin ◽  
张燕平 Zhang Yanping
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Support Weight ◽  
Adaptive Support ◽  
Visual Characteristics ◽  
Human Visual Characteristics

A Novel Adaptive Support-Weight Stereo Matching Algorithm

2014 ◽  
Vol 2 (4) ◽  
pp. 152-155
Author(s):  
Jinlong Zhou ◽  
Xuelong Hu ◽  
Liankui Hao ◽  
Chunxiao Li
Keyword(s):  
Stereo Matching ◽  
Matching Algorithm ◽  
Support Weight ◽  
Adaptive Support

Research on Stereo Matching Algorithm in Intelligent Vehicle Application

2014 ◽  
Vol 678 ◽  
pp. 35-38 ◽  
Author(s):  
Peng He ◽  
Feng Gao
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Driver Assistance System ◽  
Experimental Results ◽  
Assistance System ◽  
Intelligent Vehicle ◽  
Driver Assistance ◽  
Matching Algorithm ◽  
Environment Perception ◽  
Vehicle Technologies

Perception of environment in front of driving vehicle is a core investigation theme of intelligent vehicle technologies aiming to increase safety, convenience and efficiency of driving. Using stereo vision for environment perception is a hot technology. This paper developed an algorithm for stereo matching in intelligent vehicle application. The experimental results indicate that this algorithm is effective. Furthermore, this algorithm paves the way for the implementation of automotive driver assistance system.

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.

OCCLUSION-AIDED SUPPORT WEIGHTS FOR LOCAL STEREO MATCHING

2012 ◽  
Vol 26 (03) ◽  
pp. 1255007 ◽  
Author(s):  
WEI WANG ◽  
CAIMING ZHANG ◽  
SHUOZHEN WANG ◽  
XUEMEI LI
Keyword(s):  
Stereo Matching ◽  
Experimental Results ◽  
Disparity Estimation ◽  
Consistency Check ◽  
Negative Effects ◽  
Support Weight ◽  
Cost Aggregation ◽  
Depth Discontinuities ◽  
Local Methods ◽  
Adaptive Support

There has been a significant improvement in stereo matching with the introduction of adaptive support weights. Existing local methods mainly focus on the computation of support weight which is critical in cost aggregation and usually get excellent results. However, the negative effects of occluded regions are often ignored, which results in the problem of foreground fattening and blurred depth borders. This paper proposes a novel support aggregation strategy by utilizing the occlusion information obtained from left-right consistency check. The weights of invalid points are noticeably reduced at each disparity estimation stage. Experimental results on the Middlebury images show that our method is highly effective in improving the disparities of points around occluded areas and depth discontinuities. According to the Middlebury benchmark, the proposed method achieves the best performance among all the local methods. Moreover, our approach can be easily integrated into nearly all the existing support weights strategies.

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