Multi-Feature Background Modeling Algorithm Based on Improved Census Transform

Zhicheng, 郭治成 Guo; Jianwu, 党建武 Dang; Yangping, 王阳萍 Wang; Jing, 金静 Jin

doi:10.3788/aos201939.0815003

Cited by 2 publications

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“…At present, the matching cost computation methods mainly include the normalized mutual correlation method, the absolute differences (AD) method and non-parametric transforms such as rank and Census, but these methods are prone to be disturbed by illumination distortion and noise in complex environments. The Census transform is more resistant to illumina-FIGURE 12 Comparison of the average mismatching rate for the three kinds of regions of the corresponding algorithm tion distortion and noise and has good robustness in comparison with other methods. However, the Census transform has slightly poor matching results in weak-textured regions, and the AD algorithm, which uses the average difference in RGB channels as the matching cost, can compensate for this drawback.…”

Section: Computation Of the Initial Matching Costmentioning

confidence: 99%

“…The local stereo matching algorithm [6][7][8] outperforms the global stereo matching algorithm [9,10] in terms of real-time performance, but its matching accuracy is reduced [11]. In the local stereo matching algorithm, the Census transform used in the matching cost computation stage is more resistant and robust to illumination distortion [12], which is suitable for complex working environments, but the matching results for weak-textured regions are slightly worse and overly depend on the central pixel. A large number of scholars have made relevant improvements for the Census transform.…”

Section: Introductionmentioning

confidence: 99%

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The stereo matching algorithm based on an improved adaptive support window

Liu

2022

IET Image Processing

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In binocular stereo matching, there has been a problem of low matching accuracy and noise immunity in discontinuous regions and weak-textured regions. This paper proposes a stereo matching algorithm based on an improved adaptive support window. In the cost computation stage, first, according to the preset arm length and colour threshold, a crossbased arm is obtained, which centres on the pixel to be matched; then the adaptive regions of the vertical arm and the horizontal arm are constructed respectively, which have different shape and size. Finally, the union of the two adaptive regions is used as the final support window of Census transform. Performance evaluations on Middlebury stereo data sets demonstrate that the proposed algorithm outperforms other seven most challenging stereo matching algorithms. The mismatching rate of this algorithm is greatly reduced, and the anti-noise performance is also improved considerably. Because the construction of the adaptive region is based on strict criteria and comprehensive consideration, the algorithm proposed in the paper can improve the matching accuracy in the weak-textured regions and discontinuous disparity regions.

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Section: Computation Of the Initial Matching Costmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The stereo matching algorithm based on an improved adaptive support window

Liu

2022

IET Image Processing

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“…Among them, Census transform typically takes the left image as the reference image, extracts the gray information within the neighborhood window of the center point, and uses this as a measure to find the corresponding points in the right image. It is a classical local stereo matching algorithm [15]. Now, Census transform is widely applied in real-time applications involving 3D measurements because of its fast-running speed, speedy performance, resilience to variations in illumination and ease of implementation.…”

Section: Introductionmentioning

confidence: 99%

Stereo Matching Algorithm of Multi-Feature Fusion Based on Improved Census Transform

Zhou,

Pang

2023

Electronics

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This article proposes an improved stereo matching algorithm in order to address the issue that the conventional Census transform is overly dependent on the center pixel of the window, which makes the algorithm susceptible to noise interference and results in low matching accuracy in regions with weak texture and complex texture. In the cost calculation stage, the noise threshold is set utilizing the absolute difference detection approach, and pixels that exceed the threshold are replaced with the mean gray values of the neighboring pixels in the 3 × 3 window. This stage also includes the introduction of the gradient cost, which is coupled with the edge and feature point information to provide the final matching cost. The cross approach is employed to build the adaptive support domain and aggregate the costs during the cost aggregation stage. The disparity is finally calculated using the WTA technique, and a multi-step refinement process is employed to produce the final disparity map. The experiments demonstrate that the proposed algorithm has good anti-noise performance. Compared with other improved algorithms or composite algorithms, the average matching rate of the four standard images on the Middlebury test platform is 5.53%, which is higher than the remaining algorithms, indicating that the matching accuracy is high. The proposed algorithm provides ideas for subsequent improved algorithms.

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Multi-Feature Background Modeling Algorithm Based on Improved Census Transform

Cited by 2 publications

References 0 publications

The stereo matching algorithm based on an improved adaptive support window

The stereo matching algorithm based on an improved adaptive support window

Stereo Matching Algorithm of Multi-Feature Fusion Based on Improved Census Transform

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