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Sequence image splicing method and system of low-altitude unmanned vehicle

A technology of unmanned aerial vehicles and sequence images, which is applied in image communication, TV system components, TV, etc., and can solve the problems of sequence image error accumulation and so on

Active Publication Date: 2011-06-08
CAPITAL NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The object of the present invention is to provide a method and system dedicated to splicing sequence images of unmanned aerial vehicles, which can seamlessly splice images from a large area of ​​unmanned aerial vehicles without knowing the camera pose parameters and ground control points, and solve the sequence Automatic recognition and removal of bad images such as error accumulation and yaw in image stitching

Method used

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  • Sequence image splicing method and system of low-altitude unmanned vehicle
  • Sequence image splicing method and system of low-altitude unmanned vehicle
  • Sequence image splicing method and system of low-altitude unmanned vehicle

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Embodiment 1

[0043] In this embodiment, the SURF feature points and the HARRIS-AFFINE feature points, as well as the RANSAC fault-tolerant algorithm and epipolar geometric constraints are used to solve the homography matrix and improve the splicing quality.

[0044] figure 1 Shown is the flowchart of the low-altitude unmanned aerial vehicle sequence image mosaic method of embodiment one of the present invention, including:

[0045] Step 11, camera distortion correction.

[0046] Step 12, UAV sequence image acquisition.

[0047] Step 13, image correction, determining the homography matrix between adjacent images, including the following sub-steps:

[0048] Step 131, feature extraction, extracting SURF feature points and HARRIS-AFFINE feature points from sequence images;

[0049] Step 132, image matching, includes the following sub-steps:

[0050] Step 1321, using Mahalanobis distance to calculate the similarity of feature points between adjacent images for initial matching;

[0051] St...

Embodiment 2

[0137] Different from the first embodiment, the second embodiment adds a matching verification step. Although the homography matrix is ​​calculated and the images are matched in the first embodiment, further verification is required whether the matching result is correct. Especially for the image sequence acquired by the aircraft, there are bad images such as complete yaw inside, which need to be automatically removed during stitching.

[0138] figure 2 Shown is the flowchart of the low-altitude unmanned aerial vehicle sequence image mosaic method of the second embodiment of the present invention, including:

[0139] Step 21, camera distortion correction.

[0140] Step 22, UAV sequence image acquisition.

[0141] Step 23, select an image in the sequence of images.

[0142] Step 24, image correction, determining the homography matrix between adjacent images, including the following sub-steps:

[0143] Step 241, feature extraction, extracting SURF features and HARRIS-AFFINE...

Embodiment 3

[0167] Different from the second embodiment, the step of global optimization and adjustment of the homography matrix is ​​further added in the third embodiment.

[0168] image 3 Shown is the flowchart of the low-altitude unmanned aerial vehicle sequence image mosaic method of the third embodiment of the present invention, including:

[0169] Step 31, camera distortion correction.

[0170] Step 32, sequence image acquisition of UAV.

[0171] Step 33, select an image in the sequence of images.

[0172] Step 34, image correction, determining the homography matrix between adjacent images, including the following sub-steps:

[0173] Step 341, feature extraction, extracting SURF features and HARRIS-AFFINE features from sequence images;

[0174] Step 342, image matching, includes the following sub-steps:

[0175] Step 3421, using Mahalanobis distance to calculate the similarity of feature points between adjacent images for initial matching;

[0176] Step 3422, calculating the ...

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Abstract

The invention provides a sequence image splicing method of a low-altitude unmanned vehicle, comprising the following steps: acquiring sequence images, and correcting camera distortion; extracting characteristic points, and matching the images; generating an image splicing sequence; carrying out global optimization splicing, and splicing the images in accordance with splicing strategies; and carrying out global error compensation. A sequence image splicing system of a low-altitude unmanned vehicle comprises a characteristic point extraction module, an image matching module, a splicing sequencegeneration module and an image splicing module, wherein the characteristic point extraction module is used for extracting SURF characteristic points and HARRIS-AFFINE characteristic points of the sequence images; the image matching module is used for calculating a homography matrix between adjacent images in accordance with the characteristic points, and eliminating deteriorated images which are off-course completely; the splicing sequence generation module is used for determining the splicing sequence of the sequence images; and the image splicing module is used for carrying out splicing andglobal error compensation in accordance with the splicing sequence.

Description

technical field [0001] The invention belongs to the technical field of computer vision image processing, and in particular relates to a method and system for automatic splicing of aerial sequence images based on an unmanned aerial vehicle. Background technique [0002] Digital aerial photography based on unmanned aerial vehicles has been widely used in remote sensing, surveying and mapping, computer vision and pattern recognition and other fields. Automatic stitching of aerial image sequences is the process of automatically stitching aerial images with a certain overlapping area into a ground panorama image with a larger field of view. The content has a more comprehensive and intuitive understanding. [0003] Unmanned aerial vehicles, also known as unmanned aerial vehicles, mainly include unmanned airships, unmanned helicopters and unmanned fixed-wing aircraft. Since it is difficult for unmanned aerial vehicles to carry large loads, they are generally only equipped with lo...

Claims

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Application Information

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IPC IPC(8): H04N5/262
Inventor 张爱武宫辉力胡少兴王书民崔营营乔警卫
Owner CAPITAL NORMAL UNIVERSITY
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