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Fast Image Stitching Method Based on pca-sift

An image stitching and fast technology, applied in the field of image processing, can solve the problems of high time consumption, difficulty in satisfying real-time performance, and failure to consider the distribution of feature points, etc., and achieve the effect of excellent matching rate

Active Publication Date: 2021-11-02
FUZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this algorithm does not consider the distribution of feature points, and too many feature points will be extracted in areas with complex details. This disadvantage is that it is easy to cause mismatches, and the SIFT algorithm is complex to calculate and time-consuming, and it is difficult to meet real-time performance.

Method used

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  • Fast Image Stitching Method Based on pca-sift
  • Fast Image Stitching Method Based on pca-sift
  • Fast Image Stitching Method Based on pca-sift

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Experimental program
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Effect test

Embodiment 1

[0042] Embodiment 1, as shown in Figure 2(a), adopts the lena picture as the experimental image of dispersion analysis, as shown in Figure 2(b) and Figure 2(c), the SIFT algorithm can extract 1127 features in the image points, the dispersion of feature points is 126.628, the improved method of the present invention can extract 701 feature points, and the dispersion is 133.706. For the distribution of feature points in an image. The larger the dispersion S, the more discrete and uniform the distribution of feature points is; the smaller S is, the denser and more uneven the distribution of feature points is. It can be found from Fig. 2 that the feature point distribution of the picture processed by the improved method of the present invention is more uniform than that processed by the SIFT algorithm. In the area where the distribution of feature points is sparse, the position of the feature points extracted by the SIFT algorithm and the method in this paper are basically the sa...

Embodiment 2

[0043] Embodiment 2, as shown in Figure 3 (a), utilizes SIFT algorithm and the improved method of the present invention to match respectively, as shown in Figure 3 (b) and Figure 3 (c), the number of matching points of SIFT algorithm is 305 , the number of false matching point pairs is 18, and the correct matching rate is 94.10%; the number of matching point pairs of the improved algorithm of the present invention is 95, the number of false matching point pairs is 1, and the correct matching rate is 98.95%. It can be seen that the correct matching rate of the improved method of the present invention has increased by 4.85%.

Embodiment 3

[0044] Embodiment 3, as shown in Figure 4 (a), utilizes SIFT algorithm and the improved method of the present invention to match respectively, as shown in Figure 4 (b) and Figure 4 (c), the number of matching points of SIFT algorithm is 174 , the number of wrong matching point pairs is 17, and the correct matching rate is 90.23%; the number of matching point pairs in the improved method of the present invention is 72, the number of wrong matching point pairs is 2, and the correct matching rate is 97.22%. It can be seen that the correct matching rate of the improved method of the present invention has increased by 6.99%.

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Abstract

The invention discloses a PCA-SIFT-based fast image mosaic method, which includes: introducing an improved non-maximum value suppression method in the space extreme point detection stage to optimize initial feature points to obtain a more evenly distributed feature point set; In the stage of constructing the descriptor, the 64-dimensional SIFT descriptor is extracted based on the circular field, and the principal component analysis (PCA) method is used to further reduce the dimension of the descriptor to reduce the data complexity of the descriptor; in the feature matching stage, the K-D tree is introduced The BBF search strategy adopts random sampling consistency (RANSAC) algorithm to eliminate mismatching points, thereby improving the matching speed and matching accuracy. The image splicing experiment shows that the correct matching rate of the present invention is better than that of the SIFT algorithm, and the splicing speed is 1.6-2.2 times that of the traditional SIFT algorithm.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a fast image stitching method based on PCA-SIFT. Background technique [0002] Image stitching refers to the stitching of several overlapping images into a wide-field, seamless high-resolution image. At present, image stitching technology is widely used in medical image diagnosis, military, remote sensing image processing and other fields. One of the most important steps in image stitching is image registration, and the quality of the image registration algorithm directly determines the quality and efficiency of image stitching. Mikolajczyk evaluated the performance of several representative image registration algorithms in terms of scale scaling, image compression, and perspective transformation. The results show that SIFT is the most effective image registration algorithm in the field of image processing at present, with strong Stickiness. However, this algorithm do...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40G06K9/62G06K9/46
CPCG06T3/4038G06V10/462G06F18/2135
Inventor 郑茜颖杨炳坤程树英
Owner FUZHOU UNIV
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