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Stereoscopic matching algorithm based on Spearman correlation coefficient and dynamic planning fusion

A correlation coefficient and dynamic programming technology, applied in the field of computer vision, can solve the problems of low precision of local algorithms and poor real-time performance of global algorithms, and achieve the effect of precise precision

Active Publication Date: 2018-06-29
UNIV OF SHANGHAI FOR SCI & TECH
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Problems solved by technology

[0006] The present invention aims at the problems of low accuracy of local algorithms in specific areas and poor real-time performance of global algorithms, and proposes a stereo matching algorithm based on the fusion of Spearman correlation coefficient and dynamic programming, using Spearman correlation coefficient to calculate the matching cost, Introducing the Spearman correlation coefficient to find the matching cost is more suitable for special image areas such as illumination radiation

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  • Stereoscopic matching algorithm based on Spearman correlation coefficient and dynamic planning fusion
  • Stereoscopic matching algorithm based on Spearman correlation coefficient and dynamic planning fusion
  • Stereoscopic matching algorithm based on Spearman correlation coefficient and dynamic planning fusion

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[0024] Such as figure 1 The flow chart of the stereo matching algorithm based on Spearman correlation coefficient and dynamic programming fusion is shown, and the specific steps of the algorithm are as follows:

[0025] The first step is to read the left and right corrected pictures from the binocular stereo camera. For any reference point p(x, y) in the left picture, there is a corresponding point q(x-d, y), then the matching cost e(x, y, d) of p and q can be expressed as:

[0026] e(x,y,d)=min{|I L (x,y)-I R (x-d,y)|,T},

[0027] where d ∈ (0, d max ), d max is the maximum parallax, I L (x, y) is the pixel value of p(x, y) in the left image, I R (x-d, y) is the pixel value of q(x-d, y) in the right image, and T is the pixel threshold. so that d max +1 matching cost map.

[0028] The second step, cost aggregation:

[0029] Use 3*3 windows for cost aggregation for each matching cost map, and the matching cost after aggregation is where e(X, Y, d), C 1 (x, y, d) a...

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Abstract

The invention relates to a stereoscopic matching algorithm based on a Spearman correlation coefficient and dynamic planting fusion. The Spearman correlation coefficient is utilized to calculate a matching cost, and introduction of the Spearman correlation coefficient for figuring out the matching cost is more applicable to an illumination radiation image area and other special image areas. Then, an energy model is created for each pixel point, that is to say that various matching costs are fused to create the energy models, and the energy models are optimized through dynamic planning to obtaincandidate vision difference values. The models are fused with various matching costs, the matching precision of one single matching cost can be improved, and marginal information can be reserved better by fusing pixel differences and gradient information. The stereoscopic matching algorithm aims at solving the problems that precise matching is difficult to achieve on the illumination radiation image area and other special image areas, and the matching precision of one signal matching cost in some special areas is not high enough; by figuring out the average of the candidate vision differencevalues, a final vision difference value can be obtained, and therefore the precision of the obtained vision difference value can be accurate to the decimal.

Description

technical field [0001] The invention relates to a computer vision technology, in particular to a stereo matching algorithm based on Spearman correlation coefficient calculation matching cost and dynamic programming fusion. Background technique [0002] Stereo vision is a vital branch of the field of computer vision. It is a method of simulating the principle of human vision and using a computer to passively perceive distance. Observe an object from two or more points, obtain images under different viewing angles, find matching points according to the matching relationship between pixels in the images, and obtain the deviation between pixels, and then obtain the depth information of the object through triangulation. [0003] Stereo matching is a key step in stereo vision, and its purpose is to find homologous points in image pairs. In the 1980s, Marr of the Massachusetts Institute of Technology proposed a computer theory and applied it to binocular matching, so that two plan...

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Application Information

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IPC IPC(8): G06T7/55
CPCG06T2207/10004G06T2207/20021G06T2207/20228G06T7/55
Inventor 于修成王永雄宋燕李航
Owner UNIV OF SHANGHAI FOR SCI & TECH
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