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Super-large image rapid matching splicing method based on block subimage search

A super-large-scale, sub-picture technology, applied in the field of two-dimensional image matching, can solve the problems of long time-consuming, low-efficiency large-scale image matching and splicing, and achieve the effect of shortening the matching time

Active Publication Date: 2016-12-07
XIDIAN UNIV
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Problems solved by technology

[0003] The purpose of the present invention is to provide a method for fast matching and stitching of ultra-large images based on block subgraph search, so as to solve the problems of low efficiency and time-consuming matching and stitching of large images

Method used

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  • Super-large image rapid matching splicing method based on block subimage search
  • Super-large image rapid matching splicing method based on block subimage search
  • Super-large image rapid matching splicing method based on block subimage search

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Embodiment Construction

[0041] like figure 1 As shown, the method of fast matching and mosaicing of ultra-large images based on block subgraph search is characterized in that it includes the following steps,

[0042] Step 1, use the GDAL library to read and display two large-scale images (here, the large-scale image file size is generally greater than 10000×10000pixels), the image with the largest total number of pixels is marked as A, and the image with the smallest total number of pixels is marked as B;

[0043] Step 2, determine the block method of the two images, and cut and block;

[0044] Step 3, search for the block, comprehensively use the color feature, texture feature and shape feature of the image to calculate the similarity between the blocks, and search for a pair of block subgraphs whose similarity meets the conditions in order; if the search is successful , proceed to feature matching in step 5; if the search fails, proceed to step 4;

[0045] Step 4, swap the names of the two images...

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Abstract

The invention discloses a super-large image rapid matching splicing method based on block subimage search, and the method is used for speeding up the matching, and improving the matching efficiency. The method comprises the steps: firstly reading and displaying two large images named as A and B through employing a GDAL secondly carrying out the partitioning and cutting of the two large images in a certain mode, obtaining a plurality of subimages, taking one subimage of the image B as the target image, searching a subimage with the maximum similarity from the subimages of the image A, and carrying out the feature matching of the pair of subimages if the similarity meets a condition; selecting another subimage of the image B as the target image if the similarity does not meet the condition, carrying out the search in the image A, and carrying out the loop operation according to the above steps; renaming the images A and B if no subimage meeting the condition is searched, interchanging the names A and B of the images A and B, carrying out the above partitioning and search processes again, and selecting the pair of subimages meeting the condition for feature matching. The method can speed up the matching of the large-size images, and improves the registering efficiency.

Description

technical field [0001] The invention belongs to the field of two-dimensional image matching. Specifically, it is a method for fast matching and stitching of ultra-large images based on block subgraph search. Background technique [0002] Image registration and stitching technology has a wide range of applications in the fields of computer vision, medical image processing, and material mechanics. Images acquired under different conditions for the same object, such as images from different acquisition devices, at different times, from different shooting angles, etc., sometimes require image registration and stitching for different objects. Traditional image registration methods include grayscale-based and feature-based methods. The grayscale-based registration algorithm directly uses the grayscale information of the image to measure the similarity between images, and then adopts a certain search strategy to determine the transformation parameters to maximize the similarity. ...

Claims

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Application Information

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IPC IPC(8): G06T3/00G06T3/40G06T7/00
CPCG06T3/4038G06T2207/20221G06T3/14
Inventor 刘贵喜赵丹张娜李斯王义敏
Owner XIDIAN UNIV
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