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Singular value decomposition-random sample consensus (SVD-RANSAC) sub-pixel phase correlation matching method

A phase correlation and matching method technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as high computational complexity, weak least squares robustness, and influence of sub-pixel matching accuracy, so as to improve matching accuracy , suppress pixellocking, good practical value effect

Inactive Publication Date: 2014-05-28
TONGJI UNIV
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Problems solved by technology

However, the existing phase-correlated sub-pixel matching methods still have problems: the method of determining the peak value by interpolation is relatively low in accuracy; the method of using linear phase difference generally has the disadvantages of high computational complexity and poor resistance to gross error; in addition, sub-pixel Pixel matching accuracy is affected by pixel locking effect
Even though the singular value decomposition to obtain the main singular value vector itself is a process of improving SNR, the aliasing, noise and other errors in the correlation process will still affect the phase information between the two images, thus affecting the normalized cross power spectrum matrix. Part of the magnitude, resulting in a deviation of the phase angle vector after the singular value decomposition, which does not show a strict linear relationship. Due to the weak robustness of the least squares, the estimated offset value will be seriously affected by the deviation, resulting in sub- Decreased accuracy and stability of pixel-level estimation results

Method used

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  • Singular value decomposition-random sample consensus (SVD-RANSAC) sub-pixel phase correlation matching method
  • Singular value decomposition-random sample consensus (SVD-RANSAC) sub-pixel phase correlation matching method
  • Singular value decomposition-random sample consensus (SVD-RANSAC) sub-pixel phase correlation matching method

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Embodiment 1

[0036] Such as figure 1 As shown, an SVD-RANSAC sub-pixel phase correlation matching method includes the following steps:

[0037] 1) Obtain image A and image B with horizontal offset a and vertical offset b. Image A is expressed as g(x, y), and image B is expressed as h(x, y). The Hanning window function weights to weaken the edge effect, and then performs Discrete Fourier Transform (Discrete Fourier Transform) to obtain G(u, v) and H(u, v), then

[0038] H(u,v)=G(u,v)exp{-i(au+bv)} (1)

[0039] Calculate the normalized cross power spectrum matrix Q(u,v) of the two images:

[0040] Q ( u , v ) = G ( u , v ) H ( u , v ) * | G ( u , v ) H ( u , v ) * | = exp { - i ( au + bv ) } - - - ( 2 )

[0041] The inverse Fourier transform of the cross power spectrum under the whole pixel offset is displayed as a single-peak impact function, and the matching result at...

Embodiment 2

[0075] This embodiment will be as figure 1 The method shown is used in video image tracking experiments.

[0076] 2.1 Experimental data

[0077] In this embodiment, a DALSA 4M60 high-speed CMOS camera was used to shoot the video image sequence of the shaking table experiment of the dam model of the dam body with the overburden accumulation of the barrier lake at a frame rate of 60HZ. During the experiment, black circular mark points were pasted on the dam body model. In the tracking experiment of this embodiment, 5 tracking points were selected for matching and tracking analysis. The specific situation is as follows: Figure 7 As shown, C1 to C5 represent 5 tracking points. Select 100 frames of the video image sequence for analysis. The vibration direction of the vibrating table is the X direction, so the tracking results in the X direction are mainly concerned.

[0078] 2.2 Experimental results

[0079] According to the selected 5 tracking points, take the 101*101 template with the...

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Abstract

The invention relates to a singular value decomposition-random sample consensus (SVD-RANSAC) sub-pixel phase correlation matching method. The method includes the following steps that: at first, singular value decomposition is performed on a cross-power spectrum matrix of two images; and then, a random sample consensus algorithm is adopted to estimate the slope of phase angle vectors, and therefore, sub-pixel phase correlation matching of the two images can be realized. Compared with the prior art, and according to the method of the invention, the random sample consensus (RANSAC) algorithm is adopted to robustly estimate the slope of phase angle vector straight lines which are corresponding to main singular value vectors after the singular value decomposition, and only data that accord with a straight line model are selected to be estimated, and data which are subjected to deviation influence are discarded, and therefore, the singular value decomposition-random sample consensus (SVD-RANSAC) sub-pixel phase correlation matching method is advantageous in high-precision and high-stability results and effective inhabitation of a pixel locking phenomenon.

Description

Technical field [0001] The invention relates to an image region matching algorithm, in particular to an SVD-RANSAC sub-pixel phase correlation matching method. Background technique [0002] The precise sub-pixel matching of images is one of the research hotspots and problems in the field of photogrammetry and remote sensing. Through the sub-pixel matching between images, data such as stereo parallax, ground object displacement, and surface deformation field can be accurately obtained. Image registration based on sub-pixel matching can be used in DEM / DSM generation, image mosaic, image fusion, information extraction and deformation Monitoring and other fields have extremely important applications. [0003] Image matching is generally divided into two categories: feature matching and region matching. Compared with feature matching, regional matching has the advantages of high accuracy, more convenient gross error elimination, and uniform distribution. Phase correlation is a region...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 童小华叶真徐聿升刘世杰李凌云李天鹏王凤香
Owner TONGJI UNIV
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