Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Sparse and dense characteristic matching combined image registration method

A dense feature, image registration technology, applied in the field of image processing, can solve problems such as unsatisfactory results

Active Publication Date: 2016-03-23
CHINA UNIV OF GEOSCIENCES (WUHAN)
View PDF3 Cites 24 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

They have achieved great success in matching rigid scenes (e.g. in the field of image stitching) and mildly non-rigid scenes (e.g. in the field of medical imaging) Unable to achieve satisfactory results

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Sparse and dense characteristic matching combined image registration method
  • Sparse and dense characteristic matching combined image registration method
  • Sparse and dense characteristic matching combined image registration method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] The present invention will be further described below in conjunction with embodiment.

[0052] The invention provides an image registration method combining sparse and dense feature matching, comprising the following steps:

[0053] (1) Establish a sparse feature matching energy function based on local linear constraints:

[0054] (1-1) Using the feature detection method to infer the hypothetical matching set of the two images to be registered x n with y n Two-bit column vectors representing the spatial positions of the feature points in the two images to be registered respectively; the hypothetical matching set S includes wrong matching and correct matching, wherein the correct matching is determined according to the geometric transformation Z between the two images with matching , that is, if (x n ,y n ) is a correct match, then y n =Z(x n ) is a correct match;

[0055] (1-2) The initial position plus a displacement function v will define a transformation Z: ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a sparse and dense characteristic matching combined image registration method. According to the method, sparse characteristic matching is combined with dense characteristic matching to obtain a new mathematic model which includes two variables, nonrigid geometric transformation and disperse displacement flow field, wherein the nonrigid geometric transformation is applicable to sparse matching flows, and adjusted through introducing local linear constraint to be well posed; and the disperse displacement flow field is applicable to dense matching flows, a model similar to SIFT flows is used and meanwhile a belief propagation algorithm is adopted for optimized solution, and accurate pixel comparison matching can be obtained for a remote sensing image including nonrigid movements.

Description

technical field [0001] The invention relates to an image registration method combining sparse and dense feature matching, and belongs to the technical field of image processing. Background technique [0002] Image registration is a fundamental and challenging problem in the field of remote sensing and a prerequisite in many wide-ranging applications including terrain reconstruction, environmental monitoring, change detection, image mosaicing, image fusion, and map updating, etc. [0003] Image registration aims to establish the correspondence between pixels between two images of the same scene acquired at different times and from different perspectives or by different detectors. Registration problems can be classified as rigid or nonrigid, depending on the application and data format. Rigid registration (involving only a small number of parameters) is relatively easy and has been extensively studied. In contrast, non-rigid registration is more difficult, because the underl...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T2207/10032
Inventor 陈珺罗林波刘超王勇罗大鹏
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com